{"help": "https://data.iadb.org/pt-BR/api/3/action/help_show?name=datastore_search", "success": true, "result": {"include_total": false, "limit": 100, "records_format": "objects", "resource_id": "808f2b6b-7a4b-4da9-80ee-435bf888a525", "total_estimation_threshold": null, "last_id_operator": "gt", "records": [{"_id":1,"Resource_Name":"Applied Statistical Modelling & Health Informatics MSc, PGCert, PGDip","ID":"1","Institution_and/or_Platform":"King's College London","Main_URL":"https://www.kcl.ac.uk/study/postgraduate/taught-courses/applied-statistical-modelling-health-informatics","Resource_Type":"Diploma","Modality":"Presential","Level":"Introductory to the field/topic,Advanced training","Content_Details":"This course has been created to deliver a skill set and knowledge base in “multimodal” and “big data” analysis techniques.\nYou will receive world-class training in core statistical, machine learning and computational methodology, and you will have the opportunity to apply your skills to real-life settings facilitated by the world-leading Institute of Psychiatry, Psychology & Neuroscience (IoPPN).\nThe course will apply to a broad spectrum of graduates preparing for a career in medical statistics and health informatics, or professional methodologists and clinical researchers working in the private or public health sector.\nProgram Goals:\nOur course will help you to develop your career in the medical research and health sector, including public and private health services, pharmaceutical and computer companies, technology start-ups, government agencies as well as academia and other scientific organizations. Knowledge of applied statistical modelling and data science will also open careers in other sectors such as banking, marketing, insurance and consulting. It will also provide a strong foundation for students interested in obtaining a PhD in biostatistics, data science or informatics with an emphasis on applications in health science.\n\nCourse format and assessment:\nOur course combines training in core statistical, machine learning and computational methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical modelling and health informatics.\nEach FT MSc year you will take:\n- 8 modules and Research Project totaling 180 credits for the MSc.\nEach PT MSc year you will take:\n- 4 modules totaling 60 credits\n- 4 modules and Research Project totaling 120 credits.\nThe course has a unique delivery to allow flexibility of learning. Each programme module runs over 6-weeks and is made up of a familiarisation week supported through our VLE, 5 days on campus, face-to-face teaching and 4 weeks for independent (self) learning and assessment. The exam will take place on campus and tutors and module leads will communicate with you throughout the programme using our VLE.\nPGCert and PGDip. Each year you will take:\n- 4 modules totalling 60 credits for the PGCert;\n- 8 modules totalling 120 credits for the PGDip.\nThe course offers a unique delivery to allow flexibility of learning. Each programme module runs over 6-weeks and is made up of a familiarisation week supported through our VLE, 5 days on campus, face-to-face teaching and 4 weeks for independent (self) learning and assessment. The exam will take place on campus and tutors and module leads will communicate with you throughout the programme using our VLE.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"1 year","Language":"English","Cost":"£10,450 per year","Costs_Details":"Full time Home fees: £13,380 per year (MSc, 2021/22); £4,460 per year (PGCert, 2021/22); £8,920 per year (PGDip, 2021/22);\nFull time overseas fees: £31,350 per year (MSc, 2021/22); £10,450 per year (PGCert, 2021/22); £20,900 per year (PGDip, 2021/22);\nPart time Home fees: £6,690 per year (MSc, 2021/22);\nPart time overseas fees: £15,675 per year (MSc, 2021/22);","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":2,"Resource_Name":"Online Graduate Certificate in Applied Bioinformatics","ID":"2","Institution_and/or_Platform":"University of Delaware (UD)","Main_URL":"https://bioinformatics.udel.edu/education/online-graduate-certificate-in-applied-bioinformatics/","Resource_Type":"Diploma","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"The Online Graduate Certificate in Applied Bioinformatics (ABNF-CERT) is offered as a graduate level program ideally suited for working professionals who wish to gain knowledge and practical experience in bioinformatics. Building on the core curriculum of UD’s MS, PSM and PhD bioinformatics degree programs, the Online Graduate Certificate will allow students to gain core competency in bioinformatics for real-world applications from genomic medicine to agriculture. No previous programming or database experience is required but a familiarity with molecular biology concepts is recommended.\n\nProgram Goals:\n*Earn credential from a highly reputable bioinformatics program to advance your career\nEarn graduate level college credits that can be applied towards a Master’s or PhD degree program\n*Gain core competency for rapidly growing bioinformatics job opportunities in fields from genomics, pharmaceuticals and health care to big data analytics\n*Gain knowledge and experience in bioinformatics and systems biology methods and tools and practical programming and database skills for real-world applications\n*Learn cutting-edge state-of-the-art course contents from nationally and internationally renowned researchers and practitioners in the field\n*Learn in an interactive, experiential and multidisciplinary team environment that couples lecture-based instructions with hands-on exercises and term projects","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"1 year","Language":"English","Cost":"US$11,400  approximately","Costs_Details":"$950 per credit","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":3,"Resource_Name":"*Graduate Certificate in Bioinformatics (BINF-CERT)","ID":"3","Institution_and/or_Platform":"University of Delaware (UD)","Main_URL":"https://bioinformatics.udel.edu/education/binf-cert/","Resource_Type":"Diploma","Modality":"Presential","Level":"Introductory to the field/topic,Advanced training","Content_Details":"The Graduate Certificate is ideally suited for working professionals who cannot make a commitment to the BICB-MS or PSM program, but can use the Certificate as a stepping stone. The Certificate can also complement other UD degree programs, allowing current graduate students to gain bioinformatics knowledge and skills. The Computational Sciences Concentration (CS2) allows students to gain knowledge in developing bioinformatics methods, tools and/or databases for modern biotechnology or medicine. The Life Sciences Concentration (LSC2) allows students to gain knowledge in applying bioinformatics methods, tools and databases as an integral approach to modern biotechnology or medicine.\nProgram Goals:\nOur Graduate programs in Bioinformatics and Computational Biology aim to train the next generation of researchers and professionals who will play a key role in multi- and interdisciplinary teams, bridging life sciences and computational sciences. The program is administered through its academic home, the Department of Computer and Information Sciences (CIS), and is coordinated by the newly established Center for Bioinformatics and Computational Biology (CBCB). The scientific curriculum is built upon the research and educational strength from departments across the Colleges of Arts and Sciences, Engineering, Agriculture and Natural Resources, and Earth, Ocean and Environment.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"1 year","Language":"English","Cost":"(unknown)","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":4,"Resource_Name":"*Advanced Certificate in Bioinformatics","ID":"4","Institution_and/or_Platform":"Drexel University","Main_URL":"http://catalog.drexel.edu/graduate/schoolofbiomedicalengineeringscienceandhealthsystems/bioinformaticscert/","Resource_Type":"Diploma","Modality":"Presential","Level":"Introductory to the field/topic,Advanced training","Content_Details":"The certificate in Bioinformatics emphasizes a systems engineering approach to provide a foundation in systems biology and pathology informatics. Students are provided with hands-on experience in the application of genomic, proteomic, and other large-scale information to biomedical engineering, as well as experience in advanced computational methods used in systems biology: pathway and circuitry, feedback and control, machine learning, and biostatistics.\n\nThe certificate can be completed in a year and if successful in the program students have an option to seamlessly transfer into the MS program in Biomedical Science or Biomedical Engineering at the School of Biomedical Engineering, Science and Health Systems at Drexel University.\n\nProgram Goals:\nGraduates of the program are expected to have a broad understanding of the field of bioinformatics/computational biology and a high level of understanding of specific areas.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"1 year","Language":"English","Cost":"(unknown)","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics,Genetic Engineering,Biomedicine,Oncology,Systems Biology","Tools_Addressed":null,"Addittional_URLs":null},{"_id":5,"Resource_Name":"*Certificate In Bioinformatics & Biomarkers","ID":"5","Institution_and/or_Platform":"University of Toledo","Main_URL":"https://www.utoledo.edu/med/depts/bioinfo/pages/Certificate.html","Resource_Type":"Diploma","Modality":null,"Level":"Introductory to the field/topic,Advanced training","Content_Details":"This certificate program introduces students to the rapidly evolving fields of bioinformatics, proteomics and genomics, and provides a core knowledge of analytical approaches used in these fields. \n- This program provides essential training in the use of computer-based methods for analysis of biomedical data, including identification and evaluation of biomarkers.\n- Designed to supplement training for current PhD students, postdoctoral fellows, medical residents, or those currently employed in jobs where such training would be useful. Law students interested in relevant areas of intellectual property law also find this training useful.\n- Students can take one or two courses per term, earning the Certificate in one or two years.\n- Currently enrolled University of Toledo PhD in Biomedical Sciences or MSBS students may take individual BIPG courses as electives, with permission of the instructor.\n\nInvolves completion of three out of the five core courses:\n- Fundamentals of bioinformatics\n- Statistical methods in bioinformatics\n- Introduction to bioinformatic computation\n- Biomarker discovery, validation and implementation (offered only on even years)\n- Applications of bioinformatics and proteomics/genomics (offered only on odd years)","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"1 year","Language":"English","Cost":"(unknown)","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":6,"Resource_Name":"Diplomado en Bioinformática: Aplicaciones e Introducción a las Ciencias Genómicas","ID":"6","Institution_and/or_Platform":"Universidad Manuela Beltrán, Colombia","Main_URL":"https://www.emagister.com.co/diplomado-bioinformatica-aplicaciones-e-introduccion-ciencias-genomicas-cursos-2788297.htm \n\nhttps://umbvirtual.edu.co/programa/diplomado-en-bioinformatica-aplicaciones-e-introduccion-a-las-ciencias-genomicas/","Resource_Type":"Diploma","Modality":"Presential","Level":"Advanced training","Content_Details":"Emagister comparte con todos los interesados en dar un paso adelante en su educación el Diplomado en Bioinformática: Aplicaciones e Introducción a las Ciencias Genómicas, diseñado por la Universidad Manuela Beltrán.\n\nEste programa ha sido diseñado con el objetivo de responder a la creciente demanda de compañías en el país que requieren profesionales expertos en bioinformática que contribuyan al desarrollo de la ciencia. El egresado de esta formación estará en capacidad de manejar información genética y genómica con estándares internacionales y liderar investigaciones que den posibles soluciones a los cuestionamientos propios del ámbito científico.\n\nPúblico objetivo: \nEste programa de educación continua de la Universidad Manuela Beltrán está dirigido a: ●Personas con interés en la integración de la biología y las herramientas computacionales para resolver preguntas del ámbito biológico o biomédico por medio de datos de origen génico. ●Profesionales de las ciencias biológicas y biomédicas relacionados con análisis Bioinformáticos. ●Investigadores, funcionarios y docentes de instituciones públicas y privadas vinculados con los análisis genéticos y genómicos.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":null,"Language":"Español","Cost":"COL$2.917.502","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":7,"Resource_Name":"Diploma de Postítulo en Bioinformática y Biología Computacional","ID":"7","Institution_and/or_Platform":"Universidad de Chile","Main_URL":"https://www.postgradoquimica.cl/diploma-postitulo-bioinformatica-biologia-computacional/","Resource_Type":"Diploma","Modality":"Online","Level":"Advanced training","Content_Details":"La Facultad de Ciencias Químicas y Farmacéuticas y el Centro Avanzado de Enfermedades Crónicas (ACCDiS) de la Universidad de Chile, consciente del desafío para el país y para los profesionales que se desempeñan en estas áreas en cuanto a la actualización, perfeccionamiento y mejora continua, ha programado este diplomado, en el que participan como docentes, profesionales de diferentes instituciones chilenas e internacionales. \n \nAl finalizar este diplomado se espera que los participantes puedan resolver, de manera autónoma, problemas del ámbito de la bioquímica y genética molecular, a partir del desarrollo de pequeños scripts computacionales o usando de manera integrativa herramientas informáticas aplicadas a estudios genómicos, transcriptómicos y proteómicos.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"360 horas en modalidad Streaming.","Language":"Español","Cost":"CLP$ 1.760.000. - US$1.500","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":"R Studio,Python,Jupyter","Addittional_URLs":null},{"_id":8,"Resource_Name":"Diplomado en Genómica Computacional y Bioinformática","ID":"8","Institution_and/or_Platform":"Fundación Arturo Rosenblueth, México","Main_URL":"https://www.emagister.com.mx/diplomado/diplomados_genomica_computacional_bioinformatica-cursos-744424.htm","Resource_Type":"Diploma","Modality":"Online","Level":"Advanced training","Content_Details":"La formación tiene una duración de seis semanas, el temario abarca las principales competencias para tu desarrollo profesional, algunos de los temas que verás durante tu formación son: Genómica Computacional y Bioinformática, Conceptos Básicos de Biología Molecular, Estadística, Biología, Genómica y Proteómica e Introducción al Análisis de Datos Genómicos.\n\nLa computación se ha convertido en una herramienta muy poderosa para todas las áreas del conocimiento, lo que ha hecho que los procesos se vuelven más fácil, la genética se ha valido de los recursos que ofrecen los sistemas computacionales para ir haciendo grandes hallazgos, por esta razón, la Fundación Arturo Rosenblueth, para el avance de la ciencia ofrece, por medio del catálogo de Emagister.com.mx, el curso de Genómica Computacional y Bioinformática.\n\nObjetivos generales del curso\nConocer las diferentes tecnologías generadoras de datos genómicos, aprender a entender, interpretar, almacenar y analizar datos genómicos (secuencia de genes y proteínas, perfiles de expresión y estructuras tridimencionales entre otros) a través de herramientas computacionales, utilizar las tecnologías de la información en la administración y desarrollo de los proyectos Bioinformáticos dentro del campo de la genómica.\n\n¿A quién va dirigido?\nProfesionales con con estudios en Ciencias Computacionales Profesionales y/o estudiantes en Ciencias Biológicas, Biología Molecular, Quimica , Bioquímica, Medicina.\n\nRequisitos:\nConocimientos Básicos de: Computación, Programación, Análisis de datos, Biología y Lectura fluida del idioma Ingles. \n\nMaterias:\n- Genómica Computacional y Bioinformática\n- Conceptos Básicos de Biología Molecular\n- Estadística\n- Biología\n- Genómica y Proteómica\n- Introducción al Análisis de Datos Genómicos\n- Introducción a la Genómica Estadística y Poblacional\n- Análisis de Datos genómicos\n- Genética de poblaciones\n- Desarrollo de proyectos\n- Administración de proyectos\n- Análisis e interpretación de resultados\n- Investigación Genómica\n\nPrograma académico: 6 asignaturas\n- Conceptos Básicos de Biología Molecular, Genómica y Proteómica.\n- Introducción al Análisis de Datos Genómicos.\n- Introducción a la Genómica Estadística y Poblacional.\n- Introducción al Análisis de Datos Genómicos de Alta Densidad en Genética de Poblaciones.\n- Desarrollo y Administración de Proyectos Bioinformáticos.\n- Diseño. Desarrollo. Análisis e Interpretación de Resultados de Proyectos con Microarreglos. caso de Estudio en Investigación Genómica.","Availability":"On demand","Date_or_Duration":"126 hrs / 6 meses","Language":"Español","Cost":"MXN$ 10001-20000","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":9,"Resource_Name":"Diplomado en Bioinformática","ID":"9","Institution_and/or_Platform":"Universidad Nacional Abierta y a Distancia UNAD de Colombia","Main_URL":"https://estudios.unad.edu.co/diplomado-en-bioinformatica","Resource_Type":"Diploma","Modality":"Online","Level":"Advanced training","Content_Details":"En el Diplomado en Bioinformática se abordan los nuevos paradigmas de la investigación biológica a través del análisis computacional de grandes volúmenes de datos biológicos. Esta apuesta educativa brinda al estudiante los fundamentos de la bioinformática en el reconocimiento y uso de herramientas informáticas aplicadas en las diversas áreas de investigación biológica y ciencias ómicas, integrado con los conceptos y herramientas estadísticas que permitan dar una acertada interpretación de los datos.\nEl diplomado está constituido por tres unidades: En la unidad 1 está orientada a la Introducción a la biología de sistemas, donde se estudia el comportamiento dinámico de las biomoléculas en los sistemas vivos y se brinda los fundamentos de los conceptos de genes, transcritos, proteínas y metabolitos, entendiéndolo a partir de los estudios en bioinformática. La unidad 2 se enfoca a la introducción a la bioinformática, en esta unidad se realiza el reconocimiento y uso de las bases de datos biológicas en relación a cada ciencia ómica. En la tercera unidad se abordan los análisis de datos biológicos, se reconocen las herramientas computacionales y los principales métodos estadísticos para el análisis multivariado descriptivo y supervisado de los datos biológicos.\n\nPúblico Objetivo: \nDocentes, investigadores, egresados, estudiantes y profesionales del sector productivo que busquen desarrollar habilidades en el área de la bioinformática y la conceptualización de sus principios básicos.\n\nMetodología:\nLa metodología del diplomado en ciencia de datos para la industria es 100% virtual.y con acompañamiento tutorial permanente mediante videoconferencias, chat, correo interno y foros dispuestos en el campus virtual de la Universidad Nacional Abierta y a Distancia. Así mismo, todo el material dispuesto y organizado con acceso desde la plataforma institucional.\nEstructura académica:\nIntroducción a la biología de sistemas\n- Replicación y transcripción\n- Estructura, traducción y síntesis de las proteínas\n- Metabolismo y principales rutas metabólicas\n- Instrumentación analítica\nIntroducción a la bioinformática\n- Base de datos Biológicas de acceso abierto: KEGG, Uniprot, PubMed NCBI, Reactome, GenBank, FlyBase, HMDB.\n- Entorno de desarrollo integrado (IDE) para el lenguaje de programación R para el análisis de datos: RStudio Cloud\nAnálisis de datos biológicos\n- Característica de los datos multivariados \n- Reducción de dimensionalidad\n- Métodos descriptivos y no supervisados\n- Métodos supervisados de clasificación\n- Validación y machine learning\nConsideraciones generales:\n- Los diplomados no hacen parte de la oferta de diplomados como opción de grado de los programas de pregrado de la UNAD.\n- Los aspirantes extranjeros, pueden realizar su pago en línea con cargo a su tarjeta de crédito o débito y su entidad financiera realizará la conversión de la moneda de acuerdo a la TRM del día. Modalidad: En línea (on line) con los mismos estándares y calidad que se tienen en la modalidad presencial y acompañamiento on line por parte del director y/o tutor durante el diplomado.\n- El certificado de participación se expedirá al completar como mínimo el 80% de las actividades del diplomado.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"120 horas / 12 de Abril al 12 de Junio de 2021","Language":"Español","Cost":"COL$1.800.000","Costs_Details":"COL$1.800.000 + $9.000 de seguro estudiantil.","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":"R Studio,Pubmed,NCBI,NCBI - BLAST,UniProt,KEGG: Kyoto Encyclopedia of Genes and Genomes,Reactome,NCBI - GenBank,Directory of Open Access Journals (DOAJ)","Addittional_URLs":null},{"_id":10,"Resource_Name":"Diplomado En Bioinformática","ID":"10","Institution_and/or_Platform":"Universidad Autónoma de Sinaloa, México","Main_URL":"https://fic.uas.edu.mx/diplomado-en-bioinformatica/","Resource_Type":"Diploma","Modality":"Presential","Level":"Advanced training","Content_Details":"El Diplomado en Bioinformática propone brindar conocimientos teórico-prácticos de biología molecular y de cómputo científico para el desarrollo de habilidades en el procesamiento y análisis de secuencias biológicas generadas en la investigación en genómica, transcriptómica, proteómica, metabolómica, así como en otras áreas de la biología y la biotecnología.\n\nEl uso de estas metodologías innovadoras permitirá la solución de preguntas de investigación que requieran de un lenguaje común entre las ciencias biológicas y computacionales, y que impliquen procesamiento de grandes volúmenes de información biológica, validación e interpretación de resultados.\n\nPúblico objetivo:\nEl diplomado está dirigido a estudiantes, egresados, profesores y profesionales con escolaridad mínima de licenciatura en una disciplina altamente cuantitativa:\n\n● Matemáticas\n● Biología\n● Biotecnología\n● Biomedicina\n● Criminalística\n● Informática\n● Sistemas computacionales\n● Ciencias de la computación\n● Estadística\n● Actuaría\n● Medicina\n● Químico Farmacéutico Biólogo\n● Nutrición\n● Ingeniería bioquímica","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"150 horas / 6 de Septiembre al 21 de Diciembre de 2019","Language":"Español","Cost":"MXN$6,500","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":11,"Resource_Name":"Diplomado Internacional de Bioinformática y Biología de Sistemas (SIU- Indromics)","ID":"11","Institution_and/or_Platform":"Sociedad Iberoamericana de Bioinformática (SoIBio)","Main_URL":"http://www.soibio.org/en/node/228","Resource_Type":"Diploma","Modality":"Online","Level":"Advanced training","Content_Details":"Diplomado virtual en Bioinformática y Biología de Sistemas dado por la Universidad SIU con sede en Argentina y Coordinado desde Costa Rica en alianza con la empresa Indromics SA. Tiene un programa compuesto por 3 módulos básicos y 3 módulos de especialización: especialización en medicina molecular y genómica clínica, especialización en tecnologías de ciencias ómicas e infraestructuras de supercomputación en genómica (RNA Seq), lo cual lo hace ser único en América Latina. La formación la componen docentes provenientes de España, Costa Rica, México y Chile.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":null,"Language":"Español","Cost":"(unknown)","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":12,"Resource_Name":"Diplomado Bioinformática Genómica (*descontinuado)","ID":"12","Institution_and/or_Platform":"Universidad del Valle, Colombia","Main_URL":null,"Resource_Type":"Diploma","Modality":null,"Level":"Advanced training","Content_Details":"Las tecnologías de secuenciación han avanzado exponencialmente generando una enorme influencia en la investigación en las ciencias de la vida y la biotecnología. La enorme cantidad de información disponible dada por los proyectos genomas han hecho de las ciencias computacionales el centro de atención sobre el cual giran los análisis y la interpretación en los estudios, en especial, de la genética, medicina personalizada, agronomía, diversidad humana, medio ambiente, biodiversidad y ciencias forenses ya que proveen herramientas y métodos críticos en la investigación genómica.\nA través de métodos computacionales y estadísticos, la creación y manipulación de bases de datos, uso de algoritmos y muy diversas técnicas computacionales, la bioinformática y la genómica computacional buscan descifrar la biología, resolver e interpretar problemas prácticos y formales que incluyen datos biológicos. En consecuencia, es prioritario que desde la academia se ofrezca un programa de formación a fin de atender la avalancha y demanda que estas tecnologías están imponiendo al estado, la academia y la industría biotecnológica.\n\nDirigido a:\nEl presente programa está dirigido a los profesionales, profesores, estudiantes de pregrado y posgrado, quienes estudian y trabajan en actividades relacionadas con las ciencias de la computación (sistemas), ingenierías, estadística y matemáticas, y con las ciencias de la vida, como biología, bioquímica, genética, agronomía, ciencias de la salud y antropología.","Availability":null,"Date_or_Duration":"120 horas","Language":"Español","Cost":"(unknown)","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":13,"Resource_Name":"Bioinformática, Inmunoinformática y Modelado Molecular","ID":"13","Institution_and/or_Platform":"Pharbiopis","Main_URL":"https://www.pharbiois.com/diplomado-bioinformatica-docking","Resource_Type":"Diploma","Modality":"Online","Level":"Advanced training","Content_Details":"En este diplomado se verá de forma integral la bioinformática de secuencias y estructural en donde se incluye bioinformática general, modelado molecular, inmunoinformática, acoplamiento (docking) y dinámica molecular, además de revisar los conceptos, el alumnos irá realizando la practica de forma conjunta con el profesor de forma asincrónica.\n \nPROGRAMA\n \nMódulo 1\nBioinformática General\n1. Motivación y Presentación\n2. Arquitectura de Computadores\n3. Sistemas Operativos (generalidades Linux)\n4. Introducción a las Redes y Bases de Datos\n5. Programación Perl para Bioinformática\n6. Ejercicios Bioinformática en Perl, revisar BioPerl\n7. Conceptos generales de biología Celular\n8. Sistemática y Evolución\n9. Bases de Datos Moleculares\n10. Laboratorio Experimental Virtual\n11. Comparación de Secuencias\n12. Comparación de Secuencias , Alineamiento Múltiple, Filogenia Molecular\n13. Identificación de Genes\n14. Análisis de Variación Poblacional(SNPs)\n \nMódulo 2\nModelado Molecular General\n1. Descripción de amino ácidos: estereoquímica, nomenclatura, propiedades fisicoquímicas. \n2. Proteómica y OMICS\n3. Conceptos de inmuno-informática\n4. Fundamentos de Biotecnología (microarreglos)\n5. Estadística bioinformática \n6. Grupos Funcionales, Cargas atómicas, conformación y configuración, LogP, Efectos electrónicos y estéricos.\n7. Descriptores químicos y QSAR \n8. Diseño de fármacos considerando propiedades ADMET, virtual screening, PCAs y Drug screening workflow systems \n9. Mecánica molecular (generalidades) y Métodos semi-empíricos (generalidades)\n10. Mecánica cuántica (generalidades)\n11. Docking ligandos-proteínas \n12. Docking macromolecular-macromolécula \n13. Dendrímeros, estudios in silico.\n14. Principios básicos de Dinámica molecular.\n \nMódulo 3\nInmunoinformática\n1. Inmunidad innata y adaptativa\n2. Reconocimiento de péptidos por MHC \n3. MHC-Péptidos-TCR\n4. Estudios de QSAR en MHC\n5. Criterios para selección de proteína antigénica\n6. Búsqueda de secuencias y alineamiento múltiple para obtención de secuencia consenso\n7. Búsquedas en bases de datos de estructuras terciarias\n8. Construcción y refinamiento de proteínas\n9. Evaluación de una estructura terciaria\n10. Predicción de epítopes lineales e inmunoproteosoma\n11. Predicción de epítopes conformacionales\n12. Obtención de epítopes usando complejos proteína-anticuerpo\n13. Construcción e tercera dimensión y refinamiento estructural de epítopes\n14. Estudios de acoplamiento sobre MHC-I y MHCII\n15. Dendrímeros como nanoacarreadores de péptidos\n16. Estudio de acoplamiento péptido-dendrímero\n \nMódulo 4\nAcoplamiento molecular (docking)\n1. Presentación\n2. Grupos-funcionales-conformación-configuración\n3. Propiedades fisicoquímicas\n4. Propiedades ADMET\n5. Interacciones-no-covalentes\n6. Scoring-Sampling-Function (exploración y cálculo de energía)\n7. Preparación-Ligandos-docking\n8. Obtención-blanco-para-docking\n9. Instalación-Autodock-ADT-preparación-archivos\n10. Continuación-preparación-archivos para docking\n11. Análisis de resultados de docking\n12. Continuación-análisis-docking-validación\n13. Validación-docking\n14. Validación-docking-final\n15. ACTIVIDAD FINAL\nMódulo 5\nDinámica molecular en proteína\n1. Presentación.\n2. Estructura de proteínas.\n3. Aplicación de dinámica molecular (DM) en el área farmaceútica.\n4. Aplicación de DM en el área biotecnológica.\n5. Generalidades sobre Campos de Fuerza (force fields).\n6. Conceptos, (RMSD, RMSF, Rg, superficies accesibles a solvente, cambios conformacionales).\n7. Ejercicios de visualización con VMD.\n8. Archivos requeridos para una DM.\n9. Preparación de archivos para correr DM.\n10. Minimización de estructura para DM.\n11. Simulación de DM en NAMD.\n12. Continuación de simulación de DM en NAMD.\n13. Análisis de resultados por VMD.\n14. Análisis de resultados por CARMA.\nContinuación de análisis de resultados por CARMA y fin del módulo.","Availability":null,"Date_or_Duration":"Duración: 4.5 meses / Inicio de curso: 2 de Agosto 2021","Language":"Español","Cost":"MXN$ 5,500, US$275","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":14,"Resource_Name":"Programa de Biología Computacional y Estructural","ID":"14","Institution_and/or_Platform":"Pontificia Universidad Javeriana, Colombia","Main_URL":"https://www.javeriana.edu.co/educon/biologia-computacional-y-estructural151005","Resource_Type":"Diploma","Modality":null,"Level":"Advanced training","Content_Details":"Este programa busca contribuir a la formación integral del recurso humano altamente calificado en el análisis, y generación de información biológica por medios computacionales y que a su vez provea elementos clave para el desarrollo y entendimiento de las técnicas algorítmicas y computacionales que residen detrás de dichos análisis, apuntando a la potenciación de la investigación genómica y postgenómica en nuestro país, así como a promover la aplicación de los métodos y herramientas típicos en biología computacional en diversas áreas del conocimiento, tales como la identificación de causas moleculares de enfermedades y métodos para su diagnóstico, la identificación de factores que determinen la relación genotipo-fenotipo en cualquier sistema biológico, relación estructura-función de proteínas o la determinación de factores claves en procesos de patogenicidad.\n\nObjetivos Específicos:\n- Actualizar a los profesionales de las ciencias básicas, de la información y de la salud, mediante la transferencia del conocimiento científico y tecnológico, en métodos computacionales para el análisis y tratamiento de información biológica.\n- Elevar el nivel de competitividad de nuestros investigadores con una formación multidisciplinaria que responda a la tendencia mundial en investigación biológica.\n- Proveer a la comunidad científica nacional de herramientas que permitan impulsar el análisis efectivo de la información en sus respectivas áreas de formación, contribuyendo de esta manera con un efecto multiplicador que redunde en el avance de la ciencia en nuestro país.\n- Generar un espacio para que docentes, estudiantes y empresarios socialicen sus investigaciones y resultados en el área biología computacional, fomentando así la comunicación e intercambio de experiencias que permitan conectar las relaciones inherentes a la investigación científica, la academia y su aplicación en la industria.\n- Generar un manual de protocolos en Biología computacional y estructural que sirva de referencia para la formación de estudiantes y la investigación científica, tanto en nuestra universidad como fuera de ella.\n\nPropuesta de valor:\nLos participantes estarán en capacidad de adquirir conocimiento teórico en los fundamentos de los algoritmos principales de la Biología computacional y estructural en lo referente al conocimiento derivable de secuencias y estructuras moleculares; Habilidad en el manejo de herramientas computacionales para el análisis de datos biológicos; Interpretación de los resultados generados por programas que operan los principales algoritmos, en esencia de comparación, de la Biología computacional y estructural; Actitud crítica ante los resultados obtenidos con los diferentes programas de la Biología computacional y estructural.Dirigido aProfesionales y estudiantes de todas las áreas relacionadas con ciencias biológicas y  biomédicas. Requisitos mínimosProfesionales y estudiantes de áreas relacionadas con ciencias biológicas y biomédicas, interesados en la temática del diplomado. MetodologíaSesiones presenciales con prácticas computacionales. Ofrece amplias posibilidades de utilizar diversos medios de aprendizaje y modos de interactividad para el desarrollo de los contenidos.","Availability":null,"Date_or_Duration":"120 hrs / TBD","Language":"Español","Cost":"(unknown)","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":15,"Resource_Name":"How To Grow (almost) Anything","ID":"15","Institution_and/or_Platform":"MIT","Main_URL":"https://fab.cba.mit.edu/classes/S63.21/index.html","Resource_Type":"MOOC/Specialization","Modality":"Hybrid","Level":"Introductory to the field/topic,Advanced training","Content_Details":"Presential course of approximately 4 months and is open for applications outside MIT. At the same time it shares online a great amount of resources in the topic.  \n\n‘How to Grow (Almost) Anything,’ a course to teach experienced bio-enthusiasts and those new to the life sciences alike skills at the cutting edge of bioengineering and synthetic biology. During 2021 this course is being provided online only.\n\nTopics include: \n- Intro, Principles & Practices\n- Biodesign\n- Protein design\n- Next generation synthesis\n- Hardware and Cell-free systems\n- Cicuits & Sensors\n- Genome Engineering\n- Gene Drives & Ethics\n- Remote Lab Automation\n- 3D Bio Printing and Biofabrication\n- Bio-production\n- Engineering the Gut Microbiome\n- Measurement and Imaging","Availability":"Timed (yearly/monthy/etc),On demand","Date_or_Duration":"Dictado durante 4 meses.","Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"All,Genetic Engineering,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":"Registration form: https://docs.google.com/forms/d/e/1FAIpQLSd9eExJArv1YuFCpPwX9N9MCkfwenRUD3M_rI42CDd9JDQ3BA/viewform"},{"_id":16,"Resource_Name":"Computational Evolutionary Biology","ID":"16","Institution_and/or_Platform":"MIT (open courseware)","Main_URL":"https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-877j-computational-evolutionary-biology-fall-2005/index.htm","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"'--> Only reading material.\nCourse Description:\nWhy has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field?\nThis course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data.\n\nCourse Features:\n- Selected lecture notes\n\nInstructor(s)\nProf. Robert Berwick\n\nMIT Course Number\n6.877J / HST.949J\n\nAs Taught In\nFall 2005\n\nLevel\nGraduate","Availability":"On demand","Date_or_Duration":null,"Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Biostatistics,Genomics,Transcriptomics,Systems Biology,Evolution","Tools_Addressed":"UNIX & Bash/Awk scripting,Phylogenetic Analysis by Maximum Likelihood (PAML),PopG genetic simulation program,POPULUS","Addittional_URLs":"Other courses by the MIT Open Courseware platform: https://ocw.mit.edu/courses"},{"_id":17,"Resource_Name":"Genomics and Computational Biology","ID":"17","Institution_and/or_Platform":"MIT (open courseware)","Main_URL":"https://ocw.mit.edu/courses/health-sciences-and-technology/hst-508-genomics-and-computational-biology-fall-2002/index.htm","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"'--> Audio and reading resources available.\nThis course will assess the relationships among sequence, structure, and function in complex biological networks as well as progress in realistic modeling of quantitative, comprehensive, functional genomics analyses. Exercises will include algorithmic, statistical, database, and simulation approaches and practical applications to medicine, biotechnology, drug discovery, and genetic engineering. Future opportunities and current limitations will be critically addressed. In addition to the regular lecture sessions, supplementary sections are scheduled to address issues related to Perl, Mathematica and biology.\n\nCourse Features:\n- Audio lectures\n- Lecture notes\n- Projects (no examples)\n\nInstructor(s)\nDr. George Church\n\nMIT Course Number\nHST.508\n\nAs Taught In\nFall 2002\n\nLevel\nUndergraduate / Graduate","Availability":"On demand","Date_or_Duration":null,"Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Biostatistics,Genomics,Transcriptomics","Tools_Addressed":null,"Addittional_URLs":"Other courses by the MIT Open Courseware platform: https://ocw.mit.edu/courses"},{"_id":18,"Resource_Name":"Foundations of Algorithms and Computational Techniques in Systems Biology","ID":"18","Institution_and/or_Platform":"MIT (open courseware)","Main_URL":"https://ocw.mit.edu/courses/biological-engineering/20-482j-foundations-of-algorithms-and-computational-techniques-in-systems-biology-spring-2006/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"'--> Only reading material.\nThis subject describes and illustrates computational approaches to solving problems in systems biology. A series of case-studies will be explored that demonstrate how an effective match between the statement of a biological problem and the selection of an appropriate algorithm or computational technique can lead to fundamental advances. The subject will cover several discrete and numerical algorithms used in simulation, feature extraction, and optimization for molecular, network, and systems models in biology.\n\nCourse Features:\n- Lecture notes\n- Assignments: problem sets (no solutions)\n\nInstructor(s)\nProf. Bruce Tidor\nProf. Jacob White\n\nMIT Course Number\n20.482J / 6.581J\n\nAs Taught In\nSpring 2006\n\nLevel\nGraduate","Availability":"On demand","Date_or_Duration":null,"Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Biostatistics","Tools_Addressed":null,"Addittional_URLs":"Other courses by the MIT Open Courseware platform: https://ocw.mit.edu/courses"},{"_id":19,"Resource_Name":"Computation for Biological Engineers","ID":"19","Institution_and/or_Platform":"MIT (open courseware)","Main_URL":"https://ocw.mit.edu/courses/biological-engineering/20-181-computation-for-biological-engineers-fall-2006/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"'--> Only reading material.\nThis course covers the analytical, graphical, and numerical methods supporting the analysis and design of integrated biological systems. Topics include modularity and abstraction in biological systems, mathematical encoding of detailed physical problems, numerical methods for solving the dynamics of continuous and discrete chemical systems, statistics and probability in dynamic systems, applied local and global optimization, simple feedback and control analysis, statistics and probability in pattern recognition.\nAn official course Web site and Wiki is maintained on OpenWetWare: 20.181 Computation for Biological Engineers.\n\nCourse Features:\n- Selected lecture notes\n- Assignments: problem sets with solutions\n- Exams and solutions\n\nInstructor(s)\nProf. Eric Alm\nProf. Andrew Endy\n\nMIT Course Number\n20.181\n\nAs Taught In\nFall 2006\n\nLevel\nUndergraduate","Availability":"On demand","Date_or_Duration":null,"Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Biostatistics","Tools_Addressed":"Python,Jupyter,UNIX & Bash/Awk scripting,Protein Data Bank (PDB),Swiss-PdbViewer","Addittional_URLs":"Other courses by the MIT Open Courseware platform: https://ocw.mit.edu/courses"},{"_id":20,"Resource_Name":"Foundations of Computational and Systems Biology","ID":"20","Institution_and/or_Platform":"MIT (open courseware)","Main_URL":"https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/index.htm","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"'--> Videos, Audios, slides and other materials.\n\nThis course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.\n\nCourse Features:\n- Video lectures\n- Captions/transcript\n- Lecture notes\n- Projects (no examples)\n- Assignments: presentations (no examples)\n- Assignments: programming with examples\n- Assignments: written (no examples)\n\nThe MIT Initiative in Computational and Systems Biology (CSBi) is a campus-wide research and education program that links biology, engineering, and computer science in a multidisciplinary approach to the systematic analysis and modeling of complex biological phenomena. This course is one of a series of core subjects offered through the CSB Ph.D program, for students with an interest in interdisciplinary training and research in the area of computational and systems biology.\n\nInstructor(s)\nProf. Christopher Burge\nProf. David Gifford\nProf. Ernest Fraenkel\n\nMIT Course Number\n7.91J / 20.490J / 20.390J / 7.36J / 6.802J / 6.874J / HST.506J\n\nAs Taught In\nSpring 2014\n\nLevel\nUndergraduate / Graduate","Availability":"On demand","Date_or_Duration":null,"Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Cell Biology,Molecular Biology,Biochemistry,Microbiology,Biophysics,Systems Biology","Tools_Addressed":"NCBI - BLAST,Clustal + MUSCLE + MAFFT,Protein Data Bank (PDB),\"PDBePISA (Proteins, Interfaces, Structures and Assemblies) / PDBeFold\",Swiss-PdbViewer,EMBL-EBI,NCBI,Python,Rasmol,SCOP: Structural Classification of Proteins,Scansite,Bioinformatic Services by DTU Health Tech,DIP (Database of Interacting Proteins)","Addittional_URLs":"Other courses by the MIT Open Courseware platform: https://ocw.mit.edu/courses"},{"_id":21,"Resource_Name":"Biomedical Informatics: Data, Modeling and Analysis Graduate Certificate","ID":"21","Institution_and/or_Platform":"Stanford","Main_URL":"https://online.stanford.edu/programs/biomedical-informatics-data-modeling-and-analysis-graduate-certificate","Resource_Type":"Diploma,MOOC/Specialization","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"The Biomedical Informatics: Data, Modeling and Analysis Graduate Certificate explores the design and implementation of novel quantitative and computational methods that solve challenging problems across the entire spectrum of biology and medicine. You will acquire knowledge and skills in bio- and clinical informatics that go beyond the undergraduate level. From recent genomic research to new applications of basic biology, you will develop an in-depth understanding of the techniques used to analyze vast amounts of biological data.\n\nWho Should Apply:\nHealthcare professionals, computer scientists, biologists and bioinformaticians who want to understand how algorithms are applied to biological problems, including the different approaches and constraints necessary for practice in the modern medical field.\n\nYou Will Learn:\n- Decision science methodologies for the implementation of information systems\n- Knowledge representations and models that support medical decision-making\n- Techniques to analyze biological data from high throughput approaches\n- Topics ranging from sequence, structure and function analysis, to databases and visualization\n\nEarning the Certificate:\n- Tailor the certificate to your interests and career goals\n- Begin your certificate any academic quarter that an applicable course is offered, subject to prerequisites\n- Take courses for graduate credit and a grade\n\nRequirements:\n- Receive a B (3.0) or better in each course\n- Some courses require permission of instructor\nPrerequisites:\n- 1 year of computer programming (coursework or experience)\n- Basic college biology\n- One year of calculus, and coursework in probability and statistics is strongly advised, and is required for some courses\n- A conferred Bachelor’s degree with an undergraduate GPA of 3.0 or better.\n\nTime to Complete Certificate:\n--> 1-2 years average. 3 years maximum to complete.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"Start any academic quarter.","Language":"English","Cost":"US$3,445.00","Costs_Details":"Tuition\nTuition for each course is $1,352 per unit. Each course ranges from 3-5 units, as indicated on course enrollment pages.\n\nA required $125 one-time fee provides you with lifetime-access to your transcripts for all courses.","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Physiology,Neurobiology,Immunology,Pharmacology,Epidemiology,Oncology,Biomedicine","Tools_Addressed":null,"Addittional_URLs":"Other courses by the Stanford open courseware platform: \nhttps://online.stanford.edu/"},{"_id":22,"Resource_Name":"Computational Molecular Biology","ID":"22","Institution_and/or_Platform":"Stanford","Main_URL":"Best page: http://biochem218.stanford.edu/\n\nOfficial page: https://online.stanford.edu/courses/biomedin231-computational-molecular-biology","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"Gain a dynamic and practical perspective of computational molecular biology while exploring the most prevalent issues in the field. Critically dissect existing methods for representing and analyzing genomes, sequences, and proteins to find the strengths and limitations of each. Class projects will help molecular biologists and computer scientists to understand the major issues concerning genomes, sequences and structures.\nThis course is cross listed with BIOC218.\n\nTopics Include:\n- Genome databases\n- Quantitative and probabilistic pattern matching\n- Protein sequence and structural motifs\n- Pathway bioinformatics\n\nPrerequisites:\nGenetics, Biochemistry, and Molecular Biology (Stanford Course: BIO 41) or consent of instructor\n\nGrading:\nThere will be 7 homework assignments utilizing the tools described in the lectures. A final paper will be required for the course that critically and constructively analyzes one of the areas presented in the course.\n- Homework- 35%\n- Project- 65%","Availability":"On demand","Date_or_Duration":"(No dates at the time, but resources are available on demand)","Language":"English","Cost":"FREE","Costs_Details":"Fee option for certificate","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":"Other courses by the Stanford open courseware platform: \nhttps://online.stanford.edu/"},{"_id":23,"Resource_Name":"Stanford Genetics and Genomics Certificate","ID":"23","Institution_and/or_Platform":"Stanford","Main_URL":"https://online.stanford.edu/programs/stanford-genetics-and-genomics-certificate","Resource_Type":"Diploma,MOOC/Specialization","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"Take online courses in genetics and genomics and gain a greater understanding of biology, human health and personalized medicine. Tap into the world-class research of Stanford faculty and industry experts to acquire the skills and knowledge you need to succeed in the rapidly evolving genetics industry.\nThe certificate program topics include advancements in the field of personalized medicine, DNA sequencing technologies and commercial applications of genetics research.\n\nYou Will Gain:\n- A solid foundation of knowledge in the field of genetics\n- A broad understanding of the fundamentals of genomics\n- An introduction to cutting edge technologies used in the field\n- Familiarity with industry and medically-relevant applications\n- Real-world skills that will make you more marketable\n\nWho Should Enroll:\n- Emerging technology leaders, strategists and venture capitalists who trade within the science-medical space\n- R&D managers and new product teams \n- Medical practitioners looking to expand their knowledge in the scientific world\n- Medical sales representatives\n- Directors, Managers or Administrators who work in non-scientific roles in scientific environments\n\nCourse Structure:\nAll courses are online, on-demand and self-paced. Each consists of short online lecture videos, readings, exercises and a 20-35 question multiple choice final exam. You will also have email access to a Stanford teaching assistant to answer your questions\n\nEarning the Certificate:\nYou can earn the Stanford Genetics and Genomics Certificate of Achievement by successfully completing the two required and any four elective courses. You can enroll in courses individually or complete them through the All-Access Plan. We strongly recommend that you first complete Fundamentals of Genetics: The Genetics You Need to Know (XGEN101) and Genomics and the Other Omics: The Comprehensive Essentials (XGEN102).\n- Prerequisites5 years of work experience, preferably in science or tech-related fields\n- Bachelor’s degree or equivalent\n- High school level knowledge of biology and chemistry\n- An investigative and scientific spirit","Availability":"On demand","Date_or_Duration":"10-18 hours/course","Language":"English","Cost":"US$3,445.00","Costs_Details":"Tuition\nThe All-Access Plan – a full year to view and complete course materials, video lectures, assignments and exams, at your own pace. Revisit course materials or jump ahead – all content remains at your fingertips year-round. You also get 365 days of email access to your Stanford teaching assistant.\n$3370\n+ $75 (one-time document fee)\n$3,445\nIndividual Courses - 60 days to view and complete course materials, video lectures, assignments and exams, at your own pace. You also get 60 days of email access to your Stanford teaching assistant.\n- $695 per required course\n- $495 per elective course\n- $75 one-time document fee","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":"Other courses by the Stanford open courseware platform: \nhttps://online.stanford.edu/"},{"_id":24,"Resource_Name":"New Frontiers in Cancer Genomics","ID":"24","Institution_and/or_Platform":"Stanford","Main_URL":"https://online.stanford.edu/courses/xgen206-new-frontiers-cancer-genomics","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"New research shows that genetic variations continue to accrue throughout tumor development. Having the ability to conduct deep sequencing on the healthy and cancerous cells in a patient, at multiple stages of growth and treatment, has led to invaluable findings and new directions for analyses in the field.\nThis course explores the role of genomics in cancer diagnosis, prognosis and treatment. Providing a greater view of mutations through tumor profiling, more targeted and personalized health care can be administered and positively impact disease outcomes. Discover the latest research advancing the study of cancer and the power of genomics in medical decision making.\nThis course is an elective course in the Stanford Genetics and Genomics Certificate.\n\nWhat you will learn:\n- Assessments of hereditary risk through multi-gene panel screens\n- Classifications of cancers by genomic differences\n- Evolutions of cancer cells that cause treatment resistance\n- New technologies for non-invasive analyses\n- Spectrums and sub-types of cancer mutations\n\nTime to Complete:\nYou should expect to spend 10-18 hours to complete each course, depending on your familiarity with the topic.\nFor individual courses, we recommend that you designate 2-3 hours per week to watch video lectures and complete assignments in order to finish within 60 days.\nYou can earn the Stanford Genetics and Genomics Certificate by successfully completing the two required and any four elective courses. You may enroll in courses individually or through the All-Access Plan.\n\nMini-site\nClick here to visit the certificate's mini-site for more information about the program","Availability":"On demand","Date_or_Duration":"10-18 hours","Language":"English","Cost":"Starts from US$495.00","Costs_Details":"Tuition\nAll-Access Plan - a full year to view and complete course materials, video lectures, assignments and exams, at your own pace. Revisit course materials or jump ahead – all content remains at your fingertips year-round. You also get 365 days of email access to your Stanford teaching assistant.\n- $3370\n- $75 one-time document fee\nIndividual Courses - 60 days to view and complete course materials, video lectures, assignments and exams., at your own pace. You’ll also get 60 days of email access to your Stanford teaching assistant.\n- $695 per required course\n- $495 per elective course\n- $75 one-time document fee","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Physiology,Immunology,Pharmacology,Epidemiology,Oncology,Biomedicine","Tools_Addressed":null,"Addittional_URLs":"Other courses by the Stanford open courseware platform: \nhttps://online.stanford.edu/"},{"_id":25,"Resource_Name":"Genomics and the Other Omics: The Comprehensive Essentials","ID":"25","Institution_and/or_Platform":"Stanford","Main_URL":"https://online.stanford.edu/courses/xgen102-genomics-and-other-omics-comprehensive-essentials","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"Part of the Certificate/Program: Stanford Genetics and Genomics Certificate.\n\nGenetics and genomics are undergoing an unparalleled revolution. A better understanding of biology and human health can create breakthroughs in disease treatment and introduces the prospect of personalized medicine. This course will begin with an introduction and review of the general principles of genomics and molecular biology. You will then explore in detail the key genomic technologies and computational approaches that are driving advances in prognostics, diagnostics, and treatment. Learn how scientists sequence, assemble, and analyze the function and structure of genomes. Explore methods for determining the heritability of traits & diseases by studying the larger population, and learn how gene identification can help identify targets for therapeutic intervention. Explore how you could use personal genomics to manage your health.\nThis course is the required second course in the Stanford Genetics and Genomics Certificate.\n\n\nWhat you will learn:\n- The principles of genetics, genes and traits\n- The applications and implications of genome sequencing\n- How personal genomics might impact healthcare\n- Tools used to diagnose and treat diseases\n- Methods for determining the heritability of traits and diseases\n\nTopics include:\n- Tools, methods and applications of sequencing\n- Population genetics\n- Genome-wide association studies\n- Introduction to proteomics and protein profiling\n- ENCODE Project: the Encyclopedia of DNA Elements\n- Metabolomics and microbiomics\n\nTime to Complete: You should expect to spend 10-18 hours to complete each course, depending on your familiarity with the topic.","Availability":"On demand","Date_or_Duration":"10-18 hours","Language":"English","Cost":"Starts from US$695","Costs_Details":"Tuition\nAll-Access Plan - a full year to view and complete course materials, video lectures, assignments and exams, at your own pace. Revisit course materials or jump ahead – all content remains at your fingertips year-round. You also get 365 days of email access to your Stanford teaching assistant.\n- $3370\n- $75 one-time document fee\nIndividual Courses - 60 days to view and complete course materials, video lectures, assignments and exams., at your own pace. You’ll also get 60 days of email access to your Stanford teaching assistant.\n- $695 per required course\n- $495 per elective course\n- $75 one-time document fee","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":"NCBI,UC Santa Cruz - Genome","Addittional_URLs":"Other courses by the Stanford open courseware platform: \nhttps://online.stanford.edu/"},{"_id":26,"Resource_Name":"AI in Healthcare Specialization","ID":"26","Institution_and/or_Platform":"Coursera / Stanford","Main_URL":"https://www.coursera.org/specializations/ai-healthcare","Resource_Type":"MOOC/Specialization","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"What you will learn:\n-  Identify problems healthcare providers face that machine learning can solve\n- Analyze how AI affects patient care safety, quality, and research\n- Relate AI to the science, practice, and business of medicine\n- Apply the building blocks of AI to help you innovate and understand emerging technologies  \n\nArtificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses.\nIn this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically.  \nThis specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.\n\nThere are 5 Courses in this Specialization:\n- Introduction to Healthcare\n- Introduction to Clinical Data\n- Fundamentals of Machine Learning for Healthcare\n- Evaluations of AI Applications in Healthcare\n- AI in Healthcare Capstone\n\nCME Accreditation\nThe Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.  View the full CME accreditation information on the individual course FAQ page.","Availability":"On demand","Date_or_Duration":"9 months","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Physiology,Neurobiology,Immunology,Pharmacology,Epidemiology,Oncology,Biomedicine","Tools_Addressed":null,"Addittional_URLs":null},{"_id":27,"Resource_Name":"Designing and Implementing AI Solutions for Health Care","ID":"27","Institution_and/or_Platform":"Harvard","Main_URL":"https://online-learning.harvard.edu/course/designing-and-implementing-ai-solutions-health-care?delta=0","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This is a Harvard Medical School online executive education program will allow leaders across the ecosystem to gain insights into what it takes to successful utilize AI in the unique cultural, economic and regulatory context of health care.\n\nWhat you'll learn:\n- Key principles, technical aspects and potential pitfalls of deep learning and emerging AI approaches\n- Which problems are best suited for an AI solution and how AI adds value, drawing from established examples\n- Operational aspects and real-world AI implementation, such as data infrastructure and quality, annotation (e.g., clinical, biological or chemical), workflow integration, human/computer interface and regulatory pathways\n- Organizational needs, capabilities and structure to leverage AI in a variety of contexts (from large organizations to start-ups)\n- How to anticipate and address bias in AI\n\nCourse description:\nApplications of artificial intelligence in health care are expanding rapidly. Despite its great long-term potential, risks and challenges remain for both AI developers and their partners in health care, the life sciences industry and digital health.\nThis program will allow leaders across the ecosystem to gain insights into what it takes to successful utilize AI in the unique cultural, economic and regulatory context of health care. Interactive sessions will address technical concepts as well as real-world implementation, with examples drawn from health care delivery/operations and drug development.\nThe curriculum will feature a combination of live virtual class sessions, small group application exercises, pre-work and vigorous discussions. Upon completion of the program, participants will be able to immediately apply insights gained to the fast-moving and complex health care sector. A certificate of completion will be provided.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"2 weeks / June 15 – June 24, 2021","Language":"English","Cost":"US$2,150","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Physiology,Neurobiology,Immunology,Pharmacology,Epidemiology,Oncology,Biomedicine","Tools_Addressed":null,"Addittional_URLs":"Other courses by the Harvard Open Courseware platform: https://online-learning.harvard.edu/catalog"},{"_id":28,"Resource_Name":"Case Studies in Functional Genomics","ID":"28","Institution_and/or_Platform":"edX / Harvard","Main_URL":"https://www.edx.org/course/case-studies-in-functional-genomics","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.\n\nWhat you'll learn\n- Mapping reads\n- Quality assessment of Next Generation Data\n- Analyzing RNA-seq data\n- Analyzing DNA methylation data\n- Analyzing ChIP Seq data","Availability":"On demand","Date_or_Duration":"5 weeks with 2-4 hours/week.","Language":"English","Cost":"Free, or Verified Certificate for $149 USD","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics","Tools_Addressed":"UNIX & Bash/Awk scripting,NCBI - BLAST,NCBI - GenBank,NCBI,EMBL-EBI - The European Nucleotide Archive (ENA),EMBL-EBI,DNA Databank of Japan (DDBJ),Bioconductor,R Studio","Addittional_URLs":null},{"_id":29,"Resource_Name":"Using Python for Research","ID":"29","Institution_and/or_Platform":"edX / Harvard","Main_URL":"https://online-learning.harvard.edu/course/using-python-research?delta=1","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Take your introductory knowledge of Python programming to the next level and learn how to use Python 3 for your research.\n\nWhat you'll learn:\n- Python 3 programming basics (a review)\n- Python tools (e.g., NumPy and SciPy modules) for research applications\n- How to apply Python research tools in practical settings\n\nCourse description:\nThis course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings.\nUsing a combination of a guided introduction and more independent in-depth exploration, you will get to practice your new Python skills with various case studies chosen for their scientific breadth and their coverage of different Python features.\nThis run of the course includes revised assessments and a new module on machine learning.","Availability":"On demand,Timed (yearly/monthy/etc)","Date_or_Duration":"5 weeks / July 14, 2021 – July 8, 2022","Language":"English","Cost":"FREE","Costs_Details":"Audit for Free\nAdd a Verified Certificate for US$169","Biotech_Fields":"All","Tools_Addressed":"Python","Addittional_URLs":"Other courses by the Harvard Open Courseware platform: https://online-learning.harvard.edu/catalog"},{"_id":30,"Resource_Name":"Principles, Statistical and Computational Tools for Reproducible Data Science","ID":"30","Institution_and/or_Platform":"edX / Harvard","Main_URL":"https://www.edx.org/es/course/principles-statistical-and-computational-tools-for?index=spanish_product&queryID=d9564fe5378119f8f5af736fc693da8b&position=4","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.\n\nWhat you will learn: \n- Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproducible research.\n- Fundamentals of reproducible science using case studies that illustrate various practices\n- Key elements for ensuring data provenance and reproducible experimental design\n- Statistical methods for reproducible data analysis\n- Computational tools for reproducible data analysis and version control (Git/GitHub, Emacs/RStudio/Spyder), reproducible data (Data repositories/Dataverse) and reproducible dynamic report generation (Rmarkdown/R Notebook/Jupyter/Pandoc), and workflows.\n- How to develop new methods and tools for reproducible research and reporting\n- How to write your own reproducible paper.\n\nToday the principles and techniques of reproducible research are more important than ever, across diverse disciplines from astrophysics to political science. No one wants to do research that can’t be reproduced. Thus, this course is really for anyone who is doing any data intensive research. While many of us come from a biomedical background, this course is for a broad audience of data scientists.\nTo meet the needs of the scientific community, this course will examine the fundamentals of methods and tools for reproducible research. Led by experienced faculty from the Harvard T.H. Chan School of Public Health, you will participate in six modules that will include several case studies that illustrate the significant impact of reproducible research methods on scientific discovery.\nThis course will appeal to students and professionals in biostatistics, computational biology, bioinformatics, and data science. The course content will blend video lectures, case studies, peer-to-peer engagements and use of computational tools and platforms (such as R/RStudio, and Git/Github), culminating in a final presentation of a final reproducible research project.\nWe’ll cover Fundamentals of Reproducible Science; Case Studies; Data Provenance; Statistical Methods for Reproducible Science; Computational Tools for Reproducible Science; and Reproducible Reporting Science. These concepts are intended to translate to fields throughout the data sciences: physical and life sciences, applied mathematics and statistics, and computing.\nConsider this course a survey of best practices: we’d like to make you aware of pitfalls in reproducible data science, some failure - and success - stories in the past, and tools and design patterns that might help make it all easier. But ultimately it’ll be up to you to take the skills you learn from this course to create your own environment in which you can easily carry out reproducible research, and to encourage and integrate with similar environments for your collaborators and colleagues.","Availability":"Timed (yearly/monthy/etc),On demand","Date_or_Duration":"8 weeks","Language":"English","Cost":"FREE","Costs_Details":"GRATIS, Certificado Verificado por 99 US$","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":31,"Resource_Name":"Data Analysis for Life Sciences","ID":"31","Institution_and/or_Platform":"edX / Harvard","Main_URL":"https://www.edx.org/professional-certificate/harvardx-data-analysis-for-life-sciences","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"HarvardX's Data Analysis for Life Sciences Professional Certificate\nTechnological advances have transformed fields that rely on data by providing a wealth of information ready to be analyzed. From working with single genes to comparing entire genomes, biomedical research groups around the world are producing more data than they can handle and the ability to interpret this information is a key skill for any practitioner. The skills necessary to work with these massive datasets are in high demand, and this series will help you learn those skills.\nUsing the open-source R programming language, you’ll gain a nuanced understanding of the tools required to work with complex life sciences and genomics data. You’ll learn the mathematical concepts — and the data analytics techniques — that you need to drive data-driven research. From a strong foundation in statistics to specialized R programming skills, this series will lead you through the data analytics landscape step-by-step.\nTaught by Rafael Irizarry from the Harvard T.H. Chan School of Public Health, these courses will enable new discoveries and will help you improve individual and population health. If you’re working in the life sciences and want to learn how to analyze data, enroll now to take your research to the next level.\n\nWhat you will learn:\n- Basic statistical concepts and R programming skills for analyzing data in the life sciences.\n- The underlying math of linear models useful for data analysis in the life sciences.\n- The techniques used to perform statistical inference on high-throughput and high-dimensional data.\n- Several techniques widely used in the analysis of high-dimensional data.\n\nCourses in this program:\n1. Statistics and R\n2. Introduction to Linear Models and Matrix Algebra\n3. Statistical Inference and Modeling for High-throughput Experiments\n4. High-Dimensional Data Analysis","Availability":"On demand","Date_or_Duration":"4 months","Language":"English","Cost":"US$696","Costs_Details":null,"Biotech_Fields":"All","Tools_Addressed":"R Studio","Addittional_URLs":null},{"_id":32,"Resource_Name":"Essentials of Genomics and Biomedical Informatics","ID":"32","Institution_and/or_Platform":"edX / IsraelX","Main_URL":"https://www.edx.org/es/course/essentials-of-genomics-and-biomedical-informatics?index=spanish_product&queryID=d9564fe5378119f8f5af736fc693da8b&position=1","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"This course presents clinicians and digital health enthusiasts with an overview of the data revolution in medicine and how to exploit it for research and in the clinic. The course will not make you a bioinformatician but will introduce the main concepts, tools, algorithms, and databases in this field.\n\nThree innovations are driving the data revolution in medicine.\n- Next Generation Sequencing, and in particular, the ability to sequence individual genomes at diminishing costs.\n- \n- Electronic Medical Records, and our ability to mine, using machine learning techniques, huge datasets of medical records.\n- \n- Wearable devices, the Web, social networks and crowdsourcing - exemplifying the surprising capacity to collect medical data using non-conventional resources.\nIn order to take advantage of these technologies and participate in the revolution, physicians need a new toolbox that is generally lacking in the medical school curriculum.\nThis course is a product of a decade of a collaborative effort between researchers from the computational biology program at Bar-Ilan University, and clinicians from Sheba Medical Center to develop and deliver an extended curriculum in genomics and biomedical informatics. The program has been endorsed by the Israeli Medicine Association and Ministry of Health. Here, we present a condensed online course that includes selected topics chosen from the extended program.\nThis GaBI course on edX presents clinicians and digital health enthusiasts with an overview of the data revolution in medicine, and how to take advantage of it for research and in the clinic. In the scope of this single course, you will not become a bioinformatician, but you will be able to familiarize yourself with the main concepts, tools, algorithms, and databases used in this field, and understand the types of problems that these analysis techniques can help address.\nThe syllabus covers the main topics of this discipline in a logical order:\n● Methods used to obtain medical data (genotypic and phenotypic)\n● Analysis of biological molecules such as DNA, RNA, and proteins using various computational tools from the field of bioinformatics\n● Use of machine learning and artificial intelligence tools to mine the huge databases of medical information accumulating in Electronic Medical Records (EMRs), the Web, and numerous data science projects in medicine\n● Analysis of complex interaction networks between DNA, RNA and protein molecules to gain a more holistic and systematic view of biological systems and medical conditions\n● Practical applications in the clinic and in personalized medicine research, and the use of cutting edge technology to improve health","Availability":"On demand,Timed (yearly/monthy/etc)","Date_or_Duration":"12 weeks","Language":"English","Cost":"FREE","Costs_Details":"GRATIS, Certificado Verificado por 49 US$","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Immunology,Pharmacology,Epidemiology,Oncology,Biomedicine,Genomics,Transcriptomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":33,"Resource_Name":"How to analyze a microbiome","ID":"33","Institution_and/or_Platform":"edX / KU Leuven","Main_URL":"https://www.edx.org/es/course/how-to-analyze-a-microbiome?index=spanish_product&queryID=d9564fe5378119f8f5af736fc693da8b&position=5","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Learn common analysis techniques to make sense of microbial sequencing data.\n\nWhat you will learn:\n- How and why microbiome data are collected\n- How to extract species and function counts from sequencing data\n- The definitions of richness, evenness and diversity\n- How to compare diversity and microbial composition across conditions\n- How to compute and interpret taxon and function associations\n\nMicroorganisms play a major role in the biosphere and within our bodies, but only a tiny fraction has been cultured so far. Microbiome data, that is the genetic information of microorganisms, is therefore an important window into the hidden microbial world.\nMicrobiome data analysis elucidates the composition of microbial communities and how it changes in response to the environment. When analyzing sequencing data, we learn whether microbial diversity differs across conditions and identify links between microbes. In brief, microbiome data analysis gives us a first idea of how a microbial ecosystem works.\nThis course will illustrate with the help of real-world example data how to carry out typical analysis tasks, such as comparing microbial composition and diversity, clustering samples and computing associations. If you plan to work with microbiome data, this course will get you up to speed.\nThe instructors are experienced bioinformaticians who are internationally known for their analysis of large-scale microbiome data sets.","Availability":"On demand","Date_or_Duration":"7 weeks","Language":"English","Cost":"FREE","Costs_Details":"GRATIS, Certificado Verificado por 49 US$","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Cell Biology,Molecular Biology,Microbiology,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":34,"Resource_Name":"Demystifying Biomedical Big Data: A User’s Guide","ID":"34","Institution_and/or_Platform":"edX / Georgetown University","Main_URL":"https://www.edx.org/es/course/demystifying-biomedical-big-data-a-users-guide?index=spanish_product&queryID=d9564fe5378119f8f5af736fc693da8b&position=6","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Whether you are a student, basic scientist, researcher, clinician, or librarian, this course is designed to help you understand, analyze, and interpret biomedical big data.\n\nWhat you will learn:\n- Understand how biomedical data are being generated and processed\n- Learn about various biomedical big data resources (e.g. TCGA, G-DOC, UNIPROT, etc.)\n- Explore and analyze genomic, transcriptomic, and proteomic data using various online analysis tools\n- Make sense of big data using systems biology resources and tools\n- Appreciate the value of big data in biomedical research and clinical practice (e.g. enabling precision medicine)\n\nWith the continuous generation of massive amounts of biomedical data on a daily basis, whether from research laboratories or clinical labs, we need to improve our ability to understand and analyze the data in order to take full advantage of its power in scientific discoveries and patient care. For non-bioinformaticians, “handling” big data remains a daunting task. This course was designed to facilitate the understanding, analysis, and interpretation of biomedical big data to those in the biomedical field with limited or no significant experience in bioinformatics. The goal of this course is to “demystify” the process of analyzing biomedical big data through a series of lectures and online hands-on training sessions and demos. You will learn how to use publicly available online resources and tools for genomic, transcriptomic, and proteomic data analysis, as well as other analytic tools and online resources. This course is funded by a research grant from the US National Institutes of Health (NIH)-Big Data to Knowledge (BD2K) Initiative.","Availability":"On demand","Date_or_Duration":"8 weeks","Language":"English","Cost":"FREE","Costs_Details":"GRATIS, Certificado Verificado por 49 US$","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Physiology,Neurobiology,Immunology,Pharmacology,Epidemiology,Oncology,Biomedicine","Tools_Addressed":null,"Addittional_URLs":null},{"_id":35,"Resource_Name":"Genomic Medicine Gets Personal","ID":"35","Institution_and/or_Platform":"edX / Georgetown University","Main_URL":"https://www.edx.org/es/course/genomic-medicine-gets-personal?index=spanish_product&queryID=d9564fe5378119f8f5af736fc693da8b&position=7","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This course will provide an introduction to genomic medicine and a better understanding of the issues associated with personal genomic information.\n\nWhat you will learn:\n- The basics of genetic abnormalities and disease\n- What we can learn from genetic testing both pre- and post- birth, and in oncology\n- The basic science behind how the genetic tests are done\n- The ethical, legal, and social implications of genomic discoveries; the genetic counseling issues; and the new trends in direct-to-consumer marketing of genetic tests\n- Critical information medical professionals as well as patients and their families need to know to be current in the field\n- Available resources (education, patient support, general information, etc.)\n\nWhile the advances in genomics promise to usher a new era in medical practice and create a major paradigm shift in patient care, the ethical, legal and social impact of genomic medicine will be equally significant. The information and potential use of genomic discoveries are no longer issues left for scientists and medical professionals to handle, but have become ones for the public at large. Rarely a day passes without a genomics-related story reported in the media. By the end of this course, students will be able to better understand the field of genomics; be familiar with various online databases and resources; and understand and appreciate the medical, social, ethical, and legal issues associated with the availability of personal genomic information.\nGiven the diversity of the topics and the specific expertise required to cover each, this is a unique cross-disciplinary course where faculty from different disciplines including genetics, computational sciences, bioinformatics, genetic counseling, bioethics, law, and business will participate in lecturing. We have assembled a team of experts from various departments at Georgetown University and other institutions, to teach this comprehensive online genomics course.\nFor a detailed description of the weekly topics, see the course outline.","Availability":"On demand","Date_or_Duration":"4 weeks","Language":"English","Cost":"FREE","Costs_Details":"GRATIS, Certificado Verificado por 25 US$","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Pharmacology,Epidemiology,Oncology,Biomedicine,Genomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":36,"Resource_Name":"Health Informatics MasterTrack® Certificate","ID":"36","Institution_and/or_Platform":"Coursera / Yale","Main_URL":"https://www.coursera.org/mastertrack/health-informatics-yale","Resource_Type":"MOOC/Specialization,Diploma","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"Health informatics (HI) is an in demand field comprising applied research and the practice of informatics across clinical and public health domains. Informatics researchers develop, introduce, and evaluate new biomedically motivated methods in areas as diverse as data mining, natural language or text processing, cognitive science, human-computer interaction, decision support, databases and algorithms for analyzing large amounts of data generated in public health, clinical research and genomics/proteomics.\nIn this online program, you will develop the skills and knowledge around data, health and informatics needed to attract attention from recruiters and hiring managers in the fast growing healthcare industry.\nYou will gain a thorough understanding of the field of health informatics and its various subfields, including research, laboratory/precision medicine, imaging, and artificial intelligence. You will also explore the themes that serve as the foundation for different areas of biomedical informatics, including clinical and neuro-informatics.\nThroughout four courses and hands-on projects, you will leverage the expertise of faculty from the Division of Health Informatics at the Yale School of Public Health, learning from real-world projects and live sessions with groups of high-caliber peers.\nBy committing 6-8 hours of online study per week for about 7 months, you can earn a Yale-issued Health Informatics MasterTrack Certificate that can help you in your future graduate studies or professional pursuits in both public health departments (e.g. CDC, local government) and the private sector (e.g. organizations that employ computer scientists and mathematicians/statisticians). After successfully completing the MasterTrack, you will also be eligible for conditional credit if accepted into the Yale School of Public Health’s Master of Public Health (MPH) graduate degree program.\nUpon successful completion of this MasterTrack Certificate, you will be eligible to earn 2 conditional credits counting towards the Yale School of Public Health’s MPH program if accepted.\nWant to learn more about this program? Visit the Yale University website.\n\n4 Courses in this MasterTrack® Certificate:\n- Introduction to Health Informatics, Part 1\n- Introduction to Health Informatics, Part 2\n- Clinical Database and Ontology\n- Introduction to Natural Language Processing and Data Mining\n\nIncludes:\n- Pre-recorded videos\n- Live sessions and office hours\n- Real-world projects\n- Peer collaboration\n- Web and mobile access","Availability":"On demand","Date_or_Duration":"7 months","Language":"English,Español,Português","Cost":"US$5000","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Physiology,Neurobiology,Immunology,Pharmacology,Epidemiology,Oncology,Biomedicine","Tools_Addressed":null,"Addittional_URLs":null},{"_id":37,"Resource_Name":"Genomic Data Science Specialization","ID":"37","Institution_and/or_Platform":"Coursera / Johns Hopkins University","Main_URL":"https://www.coursera.org/specializations/genomic-data-science","Resource_Type":"MOOC/Specialization","Modality":"Online","Level":"Basic usage information","Content_Details":"This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy. \nThis Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work.\n\nWhat you will learn:\n- Next generation sequencing experiments\n- Genomic technologies\n- DNA, RNA and epigenetic patterns\n- Genome analysis\n\nWith genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome. \n\nTo audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit.","Availability":"On demand","Date_or_Duration":"10 months","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Genetic Engineering,Transcriptomics,Genomics,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":38,"Resource_Name":"Health Informatics Specialization","ID":"38","Institution_and/or_Platform":"Coursera / Johns Hopkins University","Main_URL":"https://www.coursera.org/specializations/health-informatics","Resource_Type":"MOOC/Specialization","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This Specialization is intended for health professionals, administrators, health IT staff, vendors, startups, and patients who need or want to participate in the health IT/informatics process. Throughout the five courses of this Specialization, you will learn about the social and technical context of health informatics problems, how to successfully implement health informatics interventions, how to design a health informatics solution for decision support, and how to answer a health informatics problem through data retrieval and analysis. \n\nWhat you will learn:\n- Articulate a coherent problem definition of, and a plan for addressing, a health informatics problem. \n- Answer a health informatics problem through data retrieval and analysis.\n- Design a health informatics solution for decision support.\n- Create a change management and deployment plan for a health informatics intervention.","Availability":"On demand","Date_or_Duration":"5 months","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Physiology,Neurobiology,Immunology,Pharmacology,Epidemiology,Oncology,Biomedicine","Tools_Addressed":null,"Addittional_URLs":null},{"_id":39,"Resource_Name":"Systems Biology and Biotechnology Specialization","ID":"39","Institution_and/or_Platform":"Coursera / Mount Sinai","Main_URL":"https://www.coursera.org/specializations/systems-biology","Resource_Type":"MOOC/Specialization","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Design systems-level experiments using appropriate cutting edge techniques, collect big data, and analyze and interpret small and big data sets quantitatively.\nThe Systems Biology Specialization covers the concepts and methodologies used in systems-level analysis of biomedical systems. Successful participants will learn how to use experimental, computational and mathematical methods in systems biology and how to design practical systems-level frameworks to address questions in a variety of biomedical fields. In the final Capstone Project, students will apply the methods they learned in five courses of specialization to work on a research project.\n\nThere are 6 Courses in this Specialization:\n- Introduction to Systems Biology\n- Experimental Methods in Systems Biology\n- Network Analysis in Systems Biology\n- Dynamical Modeling Methods for Systems Biology\n- Integrated Analysis in Systems Biology\n- Systems Biology and Biotechnology Capstone","Availability":"On demand","Date_or_Duration":"10 months","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Cell Biology,Molecular Biology,Microbiology,Biochemistry,Systems Biology,Evolution","Tools_Addressed":"Bioconductor,Python,R Studio,Galaxy","Addittional_URLs":null},{"_id":40,"Resource_Name":"Bioconductor for Genomic Data Science","ID":"40","Institution_and/or_Platform":"Coursera / Johns Hopkins University","Main_URL":"https://www.coursera.org/learn/bioconductor","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.","Availability":"On demand","Date_or_Duration":"9 hours","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Genetic Engineering,Transcriptomics,Genomics,Proteomics,Metabolomics","Tools_Addressed":"Bioconductor","Addittional_URLs":null},{"_id":41,"Resource_Name":"Genomic Data Science with Galaxy","ID":"41","Institution_and/or_Platform":"Coursera / Johns Hopkins University","Main_URL":"https://www.coursera.org/learn/galaxy-project","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Learn to use the tools that are available from the Galaxy Project. This is the second course in the Genomic Big Data Science Specialization.","Availability":"On demand","Date_or_Duration":"7 hours","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Genetic Engineering,Transcriptomics,Genomics,Proteomics,Metabolomics","Tools_Addressed":"Galaxy","Addittional_URLs":null},{"_id":42,"Resource_Name":"Algorithms for DNA Sequencing","ID":"42","Institution_and/or_Platform":"Coursera / Johns Hopkins University","Main_URL":"https://www.coursera.org/learn/dna-sequencing","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used.  We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.","Availability":"On demand","Date_or_Duration":"12 hours","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics","Tools_Addressed":"Python","Addittional_URLs":null},{"_id":43,"Resource_Name":"Bioinformatics Specialization","ID":"43","Institution_and/or_Platform":"Coursera / University of California San Diego","Main_URL":"https://www.coursera.org/specializations/bioinformatics","Resource_Type":"MOOC/Specialization","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"When you complete this Specialization, you will learn how to answer many questions in modern biology that have become inseparable from the computational approaches used to solve them.  You will also obtain a toolkit of existing software resources built on these computational approaches and that are used by thousands of biologists every day in one of the fastest growing fields in science.\nAlthough this Specialization centers on computational topics, you do not need to know how to program in order to complete it. If you are interested in programming, we feature an \"Honors Track\" (called \"hacker track\" in previous runs of the course). The Honors Track allows you to implement the bioinformatics algorithms that you will encounter along the way in dozens of automatically graded coding challenges. By completing the Honors Track, you will be a bioinformatics software professional!\nLearn more about the Bioinformatics Specialization (including why we are wearing these crazy outfits) by watching our introductory video.\nYou can purchase the Specialization's print companion, Bioinformatics Algorithms: An Active Learning Approach, from the textbook website.\nOur first course, \"Finding Hidden Messages in DNA\", was named a top-50 MOOC of all time by Class Central!\n\n> Subtitles available in spanish.\n\n> Courses that are part of the specialization: (7)\n- Finding Hidden Messages in DNA (Bioinformatics I)\n- Genome Sequencing (Bioinformatics II)\n- Comparing Genes, Proteins, and Genomes (Bioinformatics III)\n- Molecular Evolution (Bioinformatics IV)\n- Genomic Data Science and Clustering (Bioinformatics V)\n- Finding Mutations in DNA and Proteins (Bioinformatics VI)\n- Bioinformatics Capstone: Big Data in Biology\n\n> SKILLS YOU WILL GAIN: \nWhole Genome Sequencing\nViterbi Algorithm\nSuffix Tree\nPython Programming\nAlgorithms\nUnweighted Pair Group Method with Arithmetic Mean (UPGMA)\nBioinformatics\nBioinformatics Algorithms\nDynamic Programming\nGraph Theory","Availability":"On demand","Date_or_Duration":"Approx. 9 months, with 4 hours/week dedication.","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":"UNIX & Bash/Awk scripting,NCBI - BLAST,NCBI - GenBank,NCBI,EMBL-EBI - The European Nucleotide Archive (ENA),EMBL-EBI,DNA Databank of Japan (DDBJ)","Addittional_URLs":null},{"_id":44,"Resource_Name":"Biology Meets Programming: Bioinformatics for Beginners","ID":"44","Institution_and/or_Platform":"Coursera / University of California San Diego","Main_URL":"https://www.coursera.org/learn/bioinformatics?","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python.  It offers a gently-paced introduction to our Bioinformatics Specialization (https://www.coursera.org/specializations/bioinformatics), preparing learners to take the first course in the Specialization, \"Finding Hidden Messages in DNA\" (https://www.coursera.org/learn/dna-analysis).\n\nEach of the four weeks in the course will consist of two required components.  First, an interactive textbook provides Python programming challenges that arise from real biological problems.  If you haven't programmed in Python before, not to worry! We provide \"Just-in-Time\" exercises from the Codecademy Python track (https://www.codecademy.com/learn/python). And each page in our interactive textbook has its own discussion forum, where you can interact with other learners. Second, each week will culminate in a summary quiz.\n\nLecture videos are also provided that accompany the material, but these videos are optional.","Availability":"On demand","Date_or_Duration":"19 hours / 4 weeks","Language":"English,Español,Português","Cost":"Free, or $49 USD for certificate.","Costs_Details":null,"Biotech_Fields":"All","Tools_Addressed":"UNIX & Bash/Awk scripting,NCBI - BLAST,NCBI - GenBank,NCBI,EMBL-EBI - The European Nucleotide Archive (ENA),EMBL-EBI,DNA Databank of Japan (DDBJ),Python","Addittional_URLs":null},{"_id":45,"Resource_Name":"Drug Discovery","ID":"45","Institution_and/or_Platform":"Coursera / University of California San Diego","Main_URL":"https://www.coursera.org/learn/drug-discovery","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"The University of California San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences Drug Discovery course brings you lectures from both faculty and industry experts.   With this course, recorded on campus at UCSD, we seek to share our access to top people in the field who bring an unprecedented range of expertise on drug discovery.  \nIn this course you will learn the drug discovery process up to the filing of an Initial New Drug Application or IND. Each week you will learn the steps that a pharmaceutical or biotech company goes through to discover a new therapeutic drug. In this course you will be able to:\n\n  *   Understand the pharmaceutical and biotechnology market a changing landscape\n  *   Learn the major aspects of the drug discovery process, starting with target selection, to compound screening to designing lead candidates.\n  *   Recognize current modern drug discovery based on the lock-and-key theory, which attempts to use one single compound to hit one target to combat the related disease.\n  *   Increase understanding of the various drug discovery tools and methods that are used for finding, identifying and designing a new drug.\n  *   Define and understand the regulatory responsibilities for drug discovery to file an Investigational New Drug Application (IND).\n\nThis course is intended as part 1 of a series: Drug Discovery, Drug Development (https://www.coursera.org/learn/drug-development) and Drug Commercialization (https://www.coursera.org/learn/drug-commercialization).  We would highly recommend that you take the courses in order since it will give you a better understanding on how a drug is discovered in the lab before being tested in clinical trials and then launched in the market place.","Availability":"On demand","Date_or_Duration":"9 hours","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Immunology,Pharmacology,Oncology,Biomedicine","Tools_Addressed":null,"Addittional_URLs":null},{"_id":46,"Resource_Name":"Introduction to Genomic Data Science","ID":"46","Institution_and/or_Platform":"edX / UC San Diego","Main_URL":"https://www.edx.org/es/course/introduction-to-genomic-data-science?index=spanish_product&queryID=d9564fe5378119f8f5af736fc693da8b&position=2","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Join us on the frontier of bioinformatics and learn how to look for hidden messages in DNA without ever needing to put on a lab coat.\n\nIn the first half of this course, we'll investigate DNA replication, and ask the question, where in the genome does DNA replication begin? You will learn how to answer this question for many bacteria using straightforward algorithms to look for hidden messages in the genome.\nIn the second half of the course, we'll examine a different biological question, and ask which DNA patterns play the role of molecular clocks. The cells in your body manage to maintain a circadian rhythm, but how is this achieved on the level of DNA? Once again, we will see that by knowing which hidden messages to look for, we can start to understand the amazingly complex language of DNA. Perhaps surprisingly, we will apply randomized algorithms to solve problems.\nFinally, you will get your hands dirty and apply existing software tools to find recurring biological motifs within genes that are responsible for helping Mycobacterium tuberculosis go \"dormant\" within a host for many years before causing an active infection.\nThis course begins a series of classes illustrating the power of computing in modern biology.\n\nWhat you will learn:\n- Write Python programs to solve various tasks you may encounter\n- Formulate a formal computational problem from an informal biological problem\n- Develop algorithms for solving computational problems\n- Evaluate the effectiveness of algorithms\n- Apply existing software to actual biological datasets","Availability":"On demand","Date_or_Duration":null,"Language":"English","Cost":"FREE","Costs_Details":"GRATIS, Certificado Verificado por 49 US$","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics","Tools_Addressed":"Python","Addittional_URLs":null},{"_id":47,"Resource_Name":"Plant Bioinformatic Methods Specialization","ID":"47","Institution_and/or_Platform":"Coursera / University of Toronto","Main_URL":"https://www.coursera.org/specializations/plant-bioinformatic-methods","Resource_Type":"MOOC/Specialization","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"The Plant Bioinformatics Specialization on Coursera introduces core bioinformatic competencies and resources, such as NCBI's Genbank, Blast, multiple sequence alignments, phylogenetics in Bioinformatic Methods I, followed by protein-protein interaction, structural bioinformatics and RNA-seq analysis in Bioinformatic Methods II. In Plant Bioinformatics we cover 33 plant-specific online tools from genome browsers to transcriptomic data mining to promoter/network analyses and others. Last, a Plant Bioinformatics Capstone uses these tools to hypothesize a biological role for a gene of unknown function, summarized in a written lab report.This specialization is useful to any modern plant molecular biologist wanting to get a feeling for the incredible scope of data available to researchers. A small amount of R programming is introduced in Bioinformatic Methods II, but most of the tools are web applications. It is recommended that you have access to a laptop or desktop computer for running these as they may not work as mobile applications on your phone or tablet.\n\nThere are 4 Courses in this Specialization:\n- Bioinformatic Methods I\n- Bioinformatic Methods II\n- Plant Bioinformatics\n- Plant Bioinformatics Capstone\n\nThe past 15 years have been exciting ones in plant biology. Hundreds of plant genomes have been sequenced, RNA-seq has enabled transcriptome-wide expression profiling, and a proliferation of \"-seq\"-based methods has permitted protein-protein and protein-DNA interactions to be determined cheaply and in a high-throughput manner. These data sets in turn allow us to generate hypotheses at the click of a mouse or tap of a finger.","Availability":"On demand","Date_or_Duration":"8 months","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":48,"Resource_Name":"Network Analysis in Systems Biology","ID":"48","Institution_and/or_Platform":"Coursera / Mount Sinai","Main_URL":"https://www.coursera.org/learn/network-biology","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma’ayan Laboratory (http://labs.icahn.mssm.edu/maayanlab/) from the Icahn School of Medicine at Mount Sinai, but also other freely available data analysis and visualization tools. The ultimate aim of the course is to enable participants to utilize the methods presented in this course for analyzing their own data for their own projects. For those participants that do not work in the field, the course introduces the current research challenges faced in the field of computational systems biology.","Availability":"On demand","Date_or_Duration":"30 hours","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Cell Biology,Molecular Biology,Microbiology,Biochemistry,Systems Biology,Evolution","Tools_Addressed":"DrugPairSeeker,Expression2Kinases,GEO2Enrichr,Enrichr,UNIX & Bash/Awk scripting,R Studio,Matlab","Addittional_URLs":null},{"_id":49,"Resource_Name":"Bacterial Bioinformatics","ID":"49","Institution_and/or_Platform":"Coursera / University of Virginia","Main_URL":"https://www.coursera.org/learn/informatics","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This course provides demonstrations and exercises for performing common genomics-based analysis tasks of bacterial sequence data.  It uses PATRIC, the PathoSystems Resource Integration Center, as the platform for analysis. PATRIC is the NIH/NIAID-funded bacterial Bioinformatics Resource Center, providing comprehensive bacterial genomic data with integrated analysis tools and visualizations. PATRIC also provides a private workspace where users can upload and analyze their own data. \nCourse participants will gain skills needed to do comparative analysis of bacterial genomes, starting with raw sequence data.The lessons in the first module cover genome assembly, annotation, phylogenetic tree construction, and protein family / proteome comparisons. Each lesson builds on the previous, creating a complete baseline analysis workflow.\n\nWhat you will learn:\n- Learn how to assemble a bacterial genome\n- Learn how to annotate a bacterial genome\n- Learn how to generate a phylogenetic tree from a set of bacterial genomes\n- Learn how to perform basic comparative analyses of a set of bacterial genomes","Availability":"On demand","Date_or_Duration":"14 hours","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Cell Biology,Molecular Biology,Microbiology,Proteomics,Metabolomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":50,"Resource_Name":"Bioinformatics: Introduction and Methods","ID":"50","Institution_and/or_Platform":"Coursera / Peking University","Main_URL":"https://www.coursera.org/learn/bioinformatics-pku","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This course covers concepts and computational methods in the exciting interdisciplinary field of bioinformatics and their applications in biology, the knowledge and skills in bioinformatics you acquired will help you in your future study and research.\nTopics include: \n- Introduction and History of Bioinformatics\n- Sequence Alignment\n- Sequence Database Search\n- Markov Model\n- Next Generation Sequencing (NGS): Mapping of Reads From Resequencing and Calling of Genetic Variants\n- Functional Prediction of Genetic Variants\n- Next Generation Sequencing: Transcriptome Analysis, and RNA-Seq\n- Prediction and Analysis of Noncoding RNA\n- Ontology and Identification of Molecular Pathways\n- Bioinformatics Database and Software Resources\n- Origination of New Genes\n- Evolution function analysis of DNA methyltransferase","Availability":"On demand","Date_or_Duration":"25 hours","Language":"English,Español,Português","Cost":"Free, or $49 USD for certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":"UNIX & Bash/Awk scripting,NCBI - BLAST,NCBI - GenBank,NCBI,EMBL-EBI - The European Nucleotide Archive (ENA),EMBL-EBI,DNA Databank of Japan (DDBJ)","Addittional_URLs":null},{"_id":51,"Resource_Name":"Whole genome sequencing of bacterial genomes - tools and applications","ID":"51","Institution_and/or_Platform":"Coursera / Technical University of Denmark (DTU)","Main_URL":"https://www.coursera.org/learn/wgs-bacteria","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This course will cover the topic of Whole genome sequencing (WGS) of bacterial genomes which is becoming more and more relevant for the medical sector. WGS technology and applications are high on international political agenda, as the classical methods are being replaced by WGS technology and therefore bioinformatic tools are extremely important for allowing the people working in this sector to be able to analyze the data and obtain results that can be interpreted and used for different purposes. The course will give the learners a basis to understand and be acquainted with WGS applications in surveillance of bacteria including species identification, typing and characterization of antimicrobial resistance and virulence traits as well as plasmid characterization. It will also give the opportunity to learners to learn about online tools and what they can be used for through demonstrations on how to use some of these tools and exercises to be solved by learners with use of freely available WGS analysis tools .\n\nBy the end of this course you should be able to:\n\n1. Describe the general Principles in typing of Bacteria \n2. Give examples of the applications of Whole Genome Sequencing to Surveillance of bacterial pathogens and antimicrobial resistance \n3. Apply genomic tools for sub-typing and surveillance\n4. Define the concept of Next-Generation Sequencing and describe the sequencing data from NGS\n5. Describe how to do de novo assembly from raw reads to contigs\n6. Enumerate the methods behind the tools for species identification, MLST typing and resistance gene detection\n7. Apply the tools for species identification, MLST typing and resistance gene detection in real cases of other bacterial and pathogen genomes.\n8. Describe the methods behind the tools for Salmonella and E.coli typing, plasmid replicon detection and plasmid typing\n9. Utilize the tools for Salmonella and E.coli typing, plasmid replicon detection and plasmid typing in real cases of other bacterial and pathogen genomes.\n10. Explain the concept and be able to use the integrated bacterial analysis pipeline for batch analysis and typing of genomic data\n11. Demonstrate how to construct phylogenetic tree based on SNPs\n12. Apply the phylogenetic tool to construct phylogenetic trees and explain the relatedness of bacterial or pathogen strains\n13. Describe how to create your own sequence database\n14. Utilize the MyDbFinder tool to detect genetic markers of interest from whole genome sequencing\n\n> SKILLS YOU WILL GAIN\nNucleotide\nAntimicrobial\nGenome\nMicrobiology","Availability":"On demand","Date_or_Duration":"7 hours","Language":"English,Español,Português","Cost":"Free, or $49 USD for certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Molecular Biology,Microbiology","Tools_Addressed":"UNIX & Bash/Awk scripting,NCBI - BLAST,NCBI - GenBank,NCBI,EMBL-EBI - The European Nucleotide Archive (ENA),EMBL-EBI,DNA Databank of Japan (DDBJ)","Addittional_URLs":null},{"_id":52,"Resource_Name":"Whole genome sequencing of bacterial genomes - tools and applications","ID":"52","Institution_and/or_Platform":"Coursera / Technical University of Denmark (DTU)","Main_URL":"https://www.coursera.org/learn/wgs-bacteria","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This course will cover the topic of  Whole genome sequencing (WGS)  of bacterial genomes which is becoming more and more relevant for the medical sector.  WGS technology and applications are high on international political agenda, as the classical methods are being replaced by WGS technology and therefore bioinformatic tools are extremely important for allowing the people working in this sector to be able to analyze the data and obtain results that can be interpreted and used for different purposes. The course will give the learners a basis to understand and be acquainted with WGS applications in surveillance of bacteria including species identification, typing and characterization of antimicrobial resistance and virulence traits as well as plasmid characterization. It will also give the opportunity to learners to learn about online tools and what they can be used for through demonstrations on how to use some of these tools and exercises to be solved by learners with use of freely available WGS analysis tools .\nBy the end of this course you should be able to:\n\n1. Describe the general Principles in typing of Bacteria \n2. Give examples of the applications of Whole Genome Sequencing to Surveillance of bacterial pathogens and antimicrobial resistance \n3. Apply genomic tools for sub-typing and surveillance\n4. Define the concept of Next-Generation Sequencing and describe the sequencing data from NGS\n5. Describe how to do de novo assembly from raw reads to contigs\n6. Enumerate the methods behind the tools for species identification, MLST typing and resistance gene detection\n7. Apply the tools for species identification, MLST typing and resistance gene detection in real cases of other bacterial and pathogen genomes.\n8. Describe the methods behind the tools for Salmonella and E.coli typing, plasmid replicon detection and plasmid typing\n9. Utilize the tools for Salmonella and E.coli typing, plasmid replicon detection and plasmid typing in real cases of other bacterial and pathogen genomes.\n10. Explain the concept and be able to use the integrated bacterial analysis pipeline for batch analysis and typing of genomic data\n11. Demonstrate how to construct phylogenetic tree based on SNPs\n12. Apply the phylogenetic tool to construct phylogenetic trees and explain the relatedness of bacterial or pathogen strains\n13. Describe how to create your own sequence database\n14. Utilize the MyDbFinder tool to detect genetic markers of interest from whole genome sequencing","Availability":"On demand","Date_or_Duration":"7 hours","Language":"English,Español,Português","Cost":"Free audit, $49 USD per month to get certificate.","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Cell Biology,Molecular Biology,Microbiology","Tools_Addressed":null,"Addittional_URLs":null},{"_id":53,"Resource_Name":"Access Bioinformatics Databases with Biopython","ID":"53","Institution_and/or_Platform":"Coursera","Main_URL":"https://www.coursera.org/projects/access-bioinformatics-databases-with-biopython","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"In this 1-hour long project-based course, you will learn how to access, parse, and visualize data from various bioinformatics sequence and structural online databases such as ENTREZ, PDB, KEGG and NCBI using Biopython.\nYou will also interact with various bioinformatics file formats such as FASTA, PDB, GENBANK and XML along with various parsers to read and modify these files using Biopython.\n\nNote: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.\n\nSkill you will develop:\nBioinformatics\nPython Programming\naccess biological database\n\nIn a video that plays in a split-screen with your work area, your instructor will walk you through these steps:\n1. Sequence alignment using NCBI-BLAST\n2. Fetch PUBMED & Nucleotide sequence using ENTREZ\n3. Fetch proteins from PDB\n4. PROSITE & SCANPROSITE from EXPASY\n5. Access KEGG database","Availability":"On demand","Date_or_Duration":"1 hour","Language":"English,Español,Português","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"All","Tools_Addressed":"NCBI,NCBI - GenBank,\"PDBePISA (Proteins, Interfaces, Structures and Assemblies) / PDBeFold\",EMBL-EBI - The European Nucleotide Archive (ENA),KEGG: Kyoto Encyclopedia of Genes and Genomes,Pubmed,Python","Addittional_URLs":null},{"_id":54,"Resource_Name":"Genetics and Next Generation Sequencing for Bioinformatics","ID":"54","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/genetics-and-next-generation-sequencing-for-bioinformatics/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Do you want to enter the field of Bioinformatics, but don't know enough about DNA, RNA, and Genetics?\n\nAre you curious about the recent advances in DNA sequencing technology, and how it can be applied to Personalized Cancer Therapy and Disease Research?\nDo you want to use Bioinformatics tools to analyze data generated by Next Generation Sequencing?\n\nBy the end of this course:\n- You will have a strong foundation in DNA, RNA, and Genetics\n- You will have a thorough understanding of Next Generation DNA Sequencing Analysis\n- You will use a cloud-based platform called Galaxy for the analysis of large datasets\n- You will assess the quality of raw data\n- You will use FastQC and Trimmomatic to improve data quality\nThis course is a starting point in NGS. It covers Biology prerequisites and quality control. Future courses will cover data analysis in more detail.\n\nProject:\nThis course includes a step-by-step guided project.\nThis project will assess the quality of raw data from an Illumina sequencer.\nYou will then use FastQC and Trimmomatic to improve the quality of this data.\n\nPrerequisites:\n- Biology and Chemistry at high school 10th grade level\n- Elementary Statistics such as interpreting charts, histograms, and box-and-whisker plots\n- You must enjoy Biology\nThis is an introductory course ideal for those with no prior experience in Next Generation Sequencing Analysis.","Availability":"On demand","Date_or_Duration":"3 hours","Language":"English","Cost":"Starts from CA$14.99","Costs_Details":"Discount CA$14.99\nOriginal Price CA$44.99","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics,Genetic Engineering","Tools_Addressed":null,"Addittional_URLs":null},{"_id":55,"Resource_Name":"Bioinformatics with Python","ID":"55","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/bioinformatics-with-python/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Whether you are a student or a researcher, data scientist or bioinformatics engineer,computational biologist, this course will serve as a helpful guide when doing bioinformatics in Python.\nWe will be exploring bioinformatics with BioPython, Biotite, Scikit-Bio, BioJulia and more.\nData is everywhere, biological data is in every living organism.Let us analyse it for useful insights\n\nWe will learn\n- how to do sequence analysis with BioPython,Biotite,etc\n- how to perform sequence alignment with code.\n- how to create our own custom functions for analyzing DNA,RNA and Proteins.\n- how to do some bioinformatics with Python.\n- how to analysis the DNA sequence of Covid 19, MERS and more.\n\nNOTE: This is an introductory course structured like a reference material for anyone interested in doing bioinformatics with python.\n\nWho this course is for:\n- Beginner Python Programmers curious about doing Bioinformatics with programming\n- Students and Researchers\n- Beginners to Bioinformatics,Computational Biology and Genomics\n\nDo you know that the human genomic sequence if printed out in a normal text font, would stretch for 5000 km, which is like the distance from London to Montreal, Los Angeles to Panama, Accra to Cape Town, Tokyo to Calcutta.\n\nThis same sequence would fill about 3000 books the size of a normal book.\nUnderstanding and analyzing this sequence is clearly going to be a huge task.\nBut with the advent of powerful tools and databases we can be able to grabs a simple understanding of some aspect of it.\nIn this introductory course we will explore the various Python tools and libraries used in analyzing DNA,RNA and genome sequence.\nHence if you are interested in analyzing large sum of biological data or are curious about DNA sequence,protein synthesis,and how vaccines are designed. Then this course is for you.","Availability":"On demand","Date_or_Duration":"13.5 hours","Language":"English","Cost":"Starts from CA$13.99","Costs_Details":"Discount: CA$13.99\nOriginal Price: CA$64.99","Biotech_Fields":"All","Tools_Addressed":"Python,Jupyter","Addittional_URLs":null},{"_id":56,"Resource_Name":"Learn Bioinformatics in 6 Days","ID":"56","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/learn-bioinformatics-in-6-days/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This program runs through 6 chapters. This is a self-paced course that suits to your own daily busy schedules. Self-paced gives you the freedom to complete each topic based on your own available time before proceeding to the next chapter without hurry. Basically, I will teach about 6 fundamental topics in Bioinformatics to you. Just to let you know, I don't intend to cover a basic programming class here. Udemy has a lot of great programming courses that can be applied in a Bioinformatics settings. So please well equip yourself with at least Python, Perl, PHP, Java, SQL and R programming. That will really help you to take off faster as a Bioinformatician in the near future. In 6 days you will learn through video lectures and tutorials about:\n\nDay 1 - Introduction to Bioinformatics\n- We will start this course with a brief definition of Bioinformatics - what exactly it constitutes of, and the importance of this field in the scientific world. Also, I will share with you the history of how Bioinformatics came into being - the reason why it was coined.\nDay 2 - Bioinformatics Databases\n- One of the reasons why Bioinformatics exists is due to the increased amount of biological datasets. These are huge scientific research outputs that need to be managed. In this chapter you will learn first of all how data is stored and manipulated. Subsequently I will begin to introduce to you about nucleotide databases. Bioinformatics is all about managing data and providing scientific knowledge. In this chapter we will also focus on protein databases. These two types of databases compliment each other and both are important for your studies. An example of database that we will be covering here is UniProt.\nDay 3 - Sequence Alignment\n- This is one of my favorite chapters. Have you ever wonder how two sequences are being compared using two different sequence alignment algorithms? Think about aligning 100 DNA sequences at one time - is there a way to perform the multiple sequence alignment? In this chapter I will teach you how to perform Global and Local sequence alignments - it's all about basic concepts of scoring matrices. Let's get serious with Bioinformatics from here onward!\nDay 4 - BLAST programs\n- Yeaps! Also my favorite! I will teach you how to perform a Local sequence alignment using a Bioinformatics tool known as BLAST (Basic Local Alignment Search Tool). We will look into both nucleotide and protein sequence BLAST programs and learn how to read and interpret BLAST results in-details.\nDay 5 - Molecular Phylogenetics\n- Let's get straight to the point. You are uniquely different and special! If you are a big fan of X-Men and other Sci-Fi Hollywood movies talking about aliens, superhuman, man with extraordinary powers, or even just about how diversified we are - this is the time to pay attention to this module. I will teach you about how species is identified and classified using genetic data. To do that we will also be looking into several types of phylogeny analysis algorithms and construct a tree of life from a specific set of sequences. Don't skip this session!\nDay 6 - Genome Analysis\n- In this module you will learn about a genome. We will look closely into both structural and comparative genomics. The detail parts begin with the sequencing technologies and followed with genome assembly and annotation.\n\nRequirements:\n- An interest in learning Bioinformatics and maintaining the same interest constantly throughout the period of study.\n- A knowledge in molecular biology, basic genetics and other life science related courses would be an advantage but not necessary.\n- Some chapters require you to donwload and install certain softwares for the analysis tutorial. You need to have at least a personal laptop or a desktop with a minimum 4GiB RAM and enough storage capasity for data storing.\n- Good qualitative (semantic) and quantitative (mathemathics/statistics) reasoning.\n\nI have prepared several sets of quizzes and assignments for you to solve. Based on previous experiences with other students, I am aware that some of you might need a one-to-one discussion therefore you are welcome to request for an online meeting / discussion session with me. Just to help you progress in this course. By the way, the first chapter is F.O.C. for preview! Register now to start learning about what Bioinformatics can do to help you, be it in research routines or for career development or simply just for personal interest. You have a lifetime access to this course and the course materials will be updated periodically. Do check for updates from time to time.","Availability":"On demand","Date_or_Duration":"4.5 hours","Language":"English","Cost":"Starts from CA$13.99","Costs_Details":"Discount: CA$13.99\nOriginal Price: CA$24.99","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics,Molecular Biology","Tools_Addressed":null,"Addittional_URLs":null},{"_id":57,"Resource_Name":"NCBI Mastery- A Beginner's Guide to Bioinformatics","ID":"57","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/ncbi-mastery-beginners-guide-to-bioinformatics/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"A Beginner's Guide to Bioinformatics Course, Udemy’s most popular introductory bioinformatics curriculum. Aren’t you curious to know recent advances in bioinformatics? Do you wish to use bioinformatics tools and databases in your research? If yes, then you have landed in the right place.\nThis course talks about NCBI, The National Center for Biotechnology Information (NCBI) which is a public and primary database maintained by the National Library of Medicine (NLM), a government organization that is under the control of the US govt. It contains genes, genetic information, proteins, and many more. Each data is stored in a different database like gene is stored in gene database which is hosted by NCBI. There are multiple databases in NCBI which is nothing but for categorizing the data.\nAs the graphical user interface of various databases of NCBI and features gets updated in NCBI, we will update our courses accordingly which is an added advantage for you. So, we are way different from the YouTube videos which usually have an older version of the NCBI databases. Therefore, think of it as a subscription to a never-ending supply of bioinformatics knowledge regarding NCBI.\nThroughout the course, we will cover tons of tools and databases of NCBI like:\n· mRNA Sequence Retrieval\n· mRNA Sequence Analysis\n· Gene Database\n· FASTA Format\n· Genbank Format\n· Genbank Database\n· RefSeq Database\n· HomoloGene Database\n· ORF Finder\n· Genome Database\n· Genome Data Viewer\n· Genome Assembly\n· Genome Reference Consortium\n· SNP Database (dbSNP)\n· dbVar Database\n· ClinVar Database\n· OMIM Database\n· MedGen Database\n· GTR Database\n· BioSystems Database\n· Bio Project Database\n· BioSample Database\n· SRA Database\n· Geo Database\n· Geo Dataset\n· Geo Profiles\n· Geo Samples\n· Protein Database\n· MMDB\n· Cd-search\n· Protein Blast\n· Nucleotide Blast\n· Blastx\n· tblastn\n· Primer Blast\n· MSA Using Cobalt\n· Phylogeny Tree\n· PubChem\n· PubMed\n· Taxonomy\n\nWho this course is for:\n- Students and teachers interested in bioinformatics\n- Students and teachers interested in genomics\n- Students and teachers interested in proteomics\n- Students and teachers interested in biology\n- Students and teachers interested in zoology\n- Students and teachers interested in botany\n- Students and teachers interested in biotechnology\n- Students and teachers interested in biochemistry\n- Students and teachers interested in genetics\n- Students and teachers interested in molecular biologyy\n- Students and teachers interested in immunology\n- Students and teachers interested in microbiology\n\nRequirements:\n- Basic biology knowledge\n- Basic biochemistry knowledge\n- Basic genetics knowledge\n- Basic molecular biology knowledge\n- Computer\n- Internet","Availability":"On demand","Date_or_Duration":"14 hours","Language":"English","Cost":"Starts from CA$13.99","Costs_Details":"Discount: CA$13.99\nOriginal Price: CA$24.99","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":"NCBI,NCBI - BLAST,NCBI - GenBank,UC Santa Cruz - Genome,Nucleic Acid Database Project (NDB),Protein Data Bank (PDB),EMBL-EBI - The European Nucleotide Archive (ENA),EMBL-EBI,KEGG: Kyoto Encyclopedia of Genes and Genomes","Addittional_URLs":null},{"_id":58,"Resource_Name":"Differential Gene Expression Analysis - Your Complete A to Z","ID":"58","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/differential-gene-expression-analysis/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Become a bioinformatic analysis master: qPCR, RNAseq, Functional Genomics, Transcriptomics, R, RStudio, TUXEDO pipeline.\n\nThis is a comprehensive and all-in-one-place course that will teach you differential gene expression analysis with focus on next-generation sequencing, RNAseq and quantitative PCR (qPCR)\n\nIn this course we'll learn together one of the most popular sub-specialities in bioinformatics: differential gene expression analysis. By the end of this course you'll be able to undertake both RNAseq and qPCR based differential gene expression analysis, independently and by yourself, in R programming language. The RNAseq section of the course is the most comprehensive and includes everything you need to have the skills required to take FASTQ library of next-generation sequencing reads and end up with complete differential expression analysis. Although the course focuses on R as a biological analysis environment of choice, you'll also have the opportunity not only to learn about UNIX terminal based TUXEDO pipeline, but also online tools. Moreover you'll become well grounded in the statistical and modelling methods so you can explain and use them effectively to address bioinformatic differential gene expression analysis problems. The course has been made such that you can get a blend of hands-on analysis and experimental design experience - the practical side will allow you to do your analysis, while theoretical side will help you face unexpected problems.\n\nSummary of what will be taught and what you'll be able to do after:\n- You'll learn and be able to do a complete end-to-end RNAseq analysis in R and TUXEDO pipelines: starting with FASTQ library through doing alignment, transcriptome assembly, genome annotation, read counting and differential assessment\n- You'll learn and be able to do a qPCR analysis in R: delta-Ct method, delta-delta-Ct method, experimental design and data interpretation\n- You'll learn how to apply the knowledge of molecular biology to solve problems in differential gene expression analysis specifically, and bioinformatics generally\n- You'll learn the technical foundations of qPCR, microarray, sequencing and RNAseq so that you can confidently deal with differential gene expression data by understanding what the numbers mean\n- You'll learn and be able to use two main modelling methods in R used for differential gene expression: the general linear model as well as non-parametric rank product frameworks\n- You'll learn about pathway analysis methods and how they can be used for hypothesis generation\n- You'll learn and be able to visualise gene expression data from your experiments\n\nWho this course is for:\n- STEM graduates who don't have a sufficient grasp of molecular biology and want to start a career in bioinformatics\n- Anybody who needs a refresher in biological foundations of bioinformatics and differential gene expression analysis\n- Students who want to start a higher degree (Bachelor, Masters or PhD) project related to bioinformatics\n- People working at pharmaceutical companies or at university and who want to learn about differential gene expression analysis\n- Curious learners that want to gauge bioinformatics and differential gene expression analysis","Availability":"On demand","Date_or_Duration":"8 hours","Language":"English","Cost":"Starts from CA$13.99","Costs_Details":"Discount: CA$13.99\nOriginal Price: CA$54.99","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics","Tools_Addressed":"R Studio,FastQC","Addittional_URLs":null},{"_id":59,"Resource_Name":"Complete Victory on Bioinformatics Databases","ID":"59","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/complete-victory-on-bioinformatics-databases/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"In this course, you will get a detailed explanation of each terminology, practical exposure to each database, merits, and demerits of databases, lecture notes and many other useful materials which will not disappoint you.\n\nThe course is divided into 6 sections:\nIntroduction:-\nThe purpose of the section is to create the initial concrete background for the bioinformatics and database for you. The concepts are deep and it will test you. Do not worry you will pass through it, it is necessary for every budding scientist like you.\n\nPrimary Sequence Database:-\nIt is the primary place where raw data is stored. Is it the right place for us to retrieve the data for our valuable research? Or are we making a grave mistake of selecting the data from it? Do not worry; the course will help you answer all these questions.\n\nSecondary Sequence Database:-\nA secondary or derived database is a major source of curated data with different types of accession code. But which accession code will be most useful for our research? You will find the answer in this course.\n\nPrimary Structure Database:-\nThe database provides us with the structural information of nucleic acids and proteins, but how to select the best quality data?\n\nSecondary Structure Database:-\nA range of information like structures, binding sites, metabolic interactions, molecular action, functional relationships, protein families, motifs and homologous are part of the secondary structure database. Searching for the required information from the haystack is not an easy task.\n\nComposite Database:-\nComposite databases are formed by the merging of the primary and secondary databases for special needs. Are these database valuable to our research community, or were they created solely to win trophies?\n\nWho this course is for:\n- Anyone who wants to learn Bioinformatics.\n- Beginners in Bioinformatics and Computational Biology\n- Researchers from Genomics\n- Researchers from Proteomics\n- Researchers from Biotechnology\n- Researchers from Genetics\n- Researchers from Molecular Biology\n\nRequirements:\n- Basic Understanding of Biology\n- Willingness to learn Bioinformatics with constant zeal\n- Computer / Laptop\n- Internet Connection","Availability":"On demand","Date_or_Duration":"2.5 hours","Language":"English","Cost":"Starts from CA$13.99","Costs_Details":"Discount: CA$13.99\nOriginal Price: CA$24.99","Biotech_Fields":"All","Tools_Addressed":null,"Addittional_URLs":null},{"_id":60,"Resource_Name":"Bioinformatics tools for covid research","ID":"60","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/bioinformatics-tools-for-covid-19-research/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"The covid-19 pandemic is of utmost concern. In almost a year and a half since the virus broke out in Wuhan, China, over 100,000 articles related to covid19 have been published in scientific journals because of the pandemic. In this course you can learn about practical bioinformatics tools which you can use in your own covid19 research. These include getting data from covid19 databases, live covid19 tracking software and sequence analysis tools such as whole genome assembly and variant calling. Databases include resources on the National Center for Biotechnology Information Website and the GISAID database. Tools include Nextstrain and Nextclade as well as Exatype. This course is tailored towards students and researchers of all levels who are interested in studying coronavirus to help eradicate it. The tools shown during the course are fairly easy to use and do not presuppose too much prior knowledge of Windows or Linux. We will also cover primer design, because PCR (poly chain reaction) is a tool that is use in covid19 diagnosis. For this, we will learn how to use a tool called primer3 in Windows. This itself is a generally useful tool used in many other areas of research. Thanks for joining, and I do hope that you will greatly enjoy the course and learn new things to help win the battle against covid.","Availability":"On demand","Date_or_Duration":"1.5 hours","Language":"English","Cost":"Starts from CA$13.99","Costs_Details":"Discount: CA$13.99\nOriginal Price: CA$24.99","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Molecular Biology,Microbiology,Immunology,Pharmacology,Epidemiology,Biomedicine","Tools_Addressed":null,"Addittional_URLs":null},{"_id":61,"Resource_Name":"DNA Research using Biopython","ID":"61","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/biopython/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"DNA Research using Biopython, An Introduction To Bioinformatics, is a crash hacker course that will teach you Hybrid Developer skills. You will use your existing OOPL development skills to fly through python code effortlessly. You will first learn what is deoxyribonucleic acid (DNA) and how to work with it to simultaneously implement medical research and python code to work toward and infer a solution. You will learn how to use biopython and its libraries to help you research \n- Statistics\n- Datasets\n- Genomes\n- Neucleotides\n- Chromosomes\n- mRNA \n- DNA sequences.  \n\nWho this course is for:\n- Students that aspire a career in Bioinformatics with an emphasis on DNA Research.","Availability":"On demand","Date_or_Duration":"1 hour","Language":"English","Cost":"Starts from CA$13.99","Costs_Details":"Discount: CA$13.99\nOriginal Price: CA$139.99","Biotech_Fields":"All","Tools_Addressed":"Python,Jupyter","Addittional_URLs":null},{"_id":62,"Resource_Name":"Structural Bioinformatics Basics","ID":"62","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/structural-bioinformatics-basics/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"In this course you will learn what is structural bioinformatics all about and get an introduction of all the major areas of structural bioinformatics. Structural Bioinformatics is an interdisciplinary field that deals with the three dimensional structures of bio-molecules. It attempts to model and discover the basic principles underlying biological machinery at the molecular level. It is based on the assumption that 3D structural information of a biological system is the core to understanding its mechanism of action and function. Structural bioinformatics combines applications of physical and chemical principles with algorithms from computational science.\n\nWho this course is for:\n- Those who are fascinated by structural biology and are intrigued to know how molecules in a living organism function at an atomic level.\n\nRequirements:\n- Should have a basic understanding of molecular biology and bioinformatics.\n- A Windows/Linux Desktop/Laptop","Availability":"On demand","Date_or_Duration":"32 mins","Language":"English","Cost":"CA$149.99","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Proteomics,Metabolomics,Molecular Biology,Biophysics,Genomics,Transcriptomics","Tools_Addressed":null,"Addittional_URLs":null},{"_id":63,"Resource_Name":"Drug Design and Molecular Docking by using computation Tools","ID":"63","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/learn-molecular-docking-become-competent-in-drug-design/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"In the filed molecular orientation modeling, Molecular Docking the perfect binding of two molecules, like prediction of ligand binding on the active size of the protein. On the basic knowledge of computer you learn ligand based Computer Aided Drug design (CADD) approach involves the analysis of ligands known to interact with a target of interest. One such course is particularly designed to maintain knowledge at the beginner level of computer Drug Discovery applications for science students. Most easily Docking Software than the AutoDock. This short course will help students get a good start in becoming proficient in the field of docking and drug development simulation studies before they become familiar with the use of MOE software and dive into lab validation studies. A real problem of today's world was taken as an example in this course and a drug agent called lutein which is present in papaya for quad \"protein resistance and possibly drug agent capabilities. Was tested.\nBy the use of this software, we have performed the molecular docking studies of various naturally occurring compounds, anti virus, anti fungals, anti-nematodes and anti-protozoal drug by the pharmaceutical industry.\nThroughout this course, you will discover Molecular Docking from scratch, including\n- Install Molecular Docking Environment (MOE)\n- Retire Ligand from Bioinformatics Database\n-  Get Protein sequence form Protein Data Bank (PDB)\n-  Performed Molecular Docking\n- 2D & 3D Molecules Interaction\n\nWhat you'll learn:\n- Drug Retrieval\n- Single Software used for docking\n- Prediction to inhibit Viral Protein\n- Compound used as Drug Agent\n- Molecule Operating Environment (MOE)\n- Ligand and Protein molecules interaction\n- Visualization 2D&3D Molecules interaction\n- How to generate publication quality figures from the docking output\n\nWho this course is for:\n- Entry - level users looking at setting up Ones own simulation of molecular dynamics with applications\n- Undergraduate Student\n- Structural biology and the ability to know how molecules in a living organism function at the atomic level are intrigued.\n- Master Student\n- Post Graduate Students\n- Drug designer\n- Biotechnology and Bioinformatics\n\nRequirements\n- Basics in Biology\n- No Experience Need\n- Basic knowledge of Computers Applications and Internet access","Availability":"On demand","Date_or_Duration":"1 hour","Language":"English","Cost":"Starts from CA$13.99","Costs_Details":"Discount: CA$13.99\nOriginal Price: CA$24.99","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Proteomics,Metabolomics,Molecular Biology,Immunology,Biophysics,Pharmacology,Biomedicine","Tools_Addressed":"Protein Data Bank (PDB)","Addittional_URLs":null},{"_id":64,"Resource_Name":"Whole Genome Variant Calling @ Galaxy","ID":"64","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/gatk4-pipeline-for-sarsncov2-galaxy/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"GATK4 pipeline using Galaxy is a scientific workflow especially for the students not having expertise in Linux operating system and other command-line interfaces (CLI) for NGS data analysis. This course will cater to undergraduate and postgraduate students from a wide range of life sciences disciplines to work independently from data retrieval to publishing their articles. A plethora of NGS data tools are available on a single platform, otherwise its very difficult to manage all the latest releases of software/tools on your personal computers, if yes, we have other space, storage and expertise issues to work on CLI being a biology student at an individual level. No doubt there are cloud computing and commercial high performance computing cluster facilities are available in the world but this course will provide you the insight that how to analyze multi-omics data with limited financial resources without having much expertise in programming and knowhow to computer languages.\n\nWhat you'll learn:\n- GATK4 pipeline using Galaxy Platform\n\nWho this course is for:\n- Undergraduate and postgraduate students in the field of Biotechnology, Microbiology, Genetics, Bioinformatics and other related disciplines in life sciences.\n\nRequirements\n- Basics knowledge of genomics and sequencing technologies.","Availability":"On demand","Date_or_Duration":"3 hours","Language":"English","Cost":"Starts from CA$13.99","Costs_Details":"Discount: CA$13.99\nOriginal Price: CA$39.99","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics,Systems Biology","Tools_Addressed":"Galaxy,GATK","Addittional_URLs":null},{"_id":65,"Resource_Name":"Molecular Dynamic Simulations for Drug Discovery","ID":"65","Institution_and/or_Platform":"Udemy","Main_URL":"https://www.udemy.com/course/molecular-dynamic-simulations-for-drug-discovery/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"A perfect course for Bachelors' / Masters' / PhD students who are getting started into computational drug discovery and aware of the In silico drug discovery basics. By the time you complete this course, you will be equipped with the knowledge required to execute molecular dynamic simulations on your own starting from setting up the software to analyzing results.\n\nWhat you'll learn\n- Introduction to Molecular dynamic simulations\n- What is OPLS force field\n- How to setup the simulation for execution\n- How to execute the simulation step-by-step\n- How to analyze the simulation output\n- How to interpret the simulation output graphs\n\nWho this course is for:\n- Beginner level Molecular Dynamic Simulations learning enthusiasts\n\nRequirements\n- Basic understanding of In silico drug discovery\n- Basics of Biology and drug discovery","Availability":"On demand","Date_or_Duration":"2.5 hours","Language":"English","Cost":"Starts from CA$13.99","Costs_Details":"Discount: CA$13.99\nOriginal Price: CA$59.99","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Proteomics,Metabolomics,Molecular Biology,Immunology,Biophysics,Pharmacology,Biomedicine","Tools_Addressed":null,"Addittional_URLs":null},{"_id":66,"Resource_Name":"Mathematics of life: Modelling molecular mechanisms","ID":"66","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/mathematics-life-virtual/","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"This course will provide participants with an introduction and hands-on training on modelling approaches, tools and resources used in systems biology as well as touch on network analysis.\n\nComputer models are increasingly used to understand the essential processes of biology. Researchers in academic institutions as well as the pharmaceutical industry use mathematical models to generate hypotheses on how complex biomolecular systems work. Modelling of biochemical pathways deregulated in disease conditions can offer mechanistic insights into the pathology, help to elucidate mechanisms behind drug action, and predict the dose required for treatment thus facilitating fundamental research and drug discovery. This course will provide a helpful introduction to tools and resources used in this scientific field.\n\nThe course will involve participants learning via pre-recorded lectures, live presentations, trainer Q&A sessions and hands-on tutorials. The content will be delivered over Zoom, with additional text communication over Slack. In order to make the most out of the course, you should make sure to have a stable internet connection throughout the week.\n\nCourse content:\n- Network Analysis and Pathway Enrichment\n- Qualitative (e.g. logic) modelling and quantitative (e.g. chemical kinetics, constraint based) modelling\n- Data resources for modelling, pathways and molecular interaction: BioModels, Reactome, IntAct, ComplexPortal etc.\n- Model sharing: how to encode, annotate and distribute models\n- Several tools will be used during the course, including accessing IntAct data from Cytoscape, COPASI, MORPHEUS and CellCollective\n- Group challenge on model curation","Availability":"Sporadic","Date_or_Duration":"27 September - 01 October 2021","Language":"English","Cost":"£200","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Biostatistics,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Cell Biology,Molecular Biology,Microbiology,Biochemistry,Systems Biology","Tools_Addressed":"Copasi,BioModels,Morpheus (GERMANY),Cell Collective,Complex Portal,IntAct Molecular Interaction Database (EMBL-EBI),Reactome,EMBL-EBI","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":67,"Resource_Name":"Structural bioinformatics","ID":"67","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/structural-bioinformatics2021/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This course explores bioinformatics data resources and tools for the investigation, analysis, and interpretation of biomacromolecular structures. It will focus on how best to analyse and interpret available structural data to gain useful information given specific research contexts. The course content will also cover predicting protein structure and function, and exploring interactions with other macromolecules as well as with low-MW compounds.\n\nThis course will be a virtual event delivered via a mixture of live-streamed sessions, pre-recorded lectures, and tutorials with live support. We will be using Zoom to run the live stream sessions (all fully password protected with automated English closed captioning and transcription) with support and networking opportunities provided by Slack. In order to make the most out of the course, you should make sure to have a stable internet connection throughout the week. There will also be networking and short social activities throughout the course and efforts will be made to make these accessible at different time zones across the week.\n\nWho is this course for:\nThis course is aimed at scientists generating structural data or scientists utilising structural data in their analysis and/or interpretation. No previous experience in the field of structural bioinformatics is required, however a basic knowledge of protein structure would be of benefit.\n\nCourse content:\n- Public repositories of structural data: Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB), and tools to search and analyse information in these repositories from PDBe (Protein Data Bank in Europe) including PDBe-KB\n- UniProt and basic Sequence alignment tools\n- Protein structure analysis and classification: HMMER, InterPro, Pfam, CATH, PDBeFold, PDBePISA\n- Structure validation and assessment tools and strategies\n- Tools and resources for drug discovery: ChEMBL","Availability":"Sporadic","Date_or_Duration":"11 - 15 October 2021","Language":"English","Cost":"£200","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Transcriptomics,Proteomics,Metabolomics,Cell Biology,Molecular Biology,Microbiology,Biochemistry,Biophysics,Immunology,Pharmacology,Chemistry","Tools_Addressed":"Electron Microscopy Data Bank (EMDB),Protein Data Bank (PDB),UniProt,Interpro,Pfam,Clustal + MUSCLE + MAFFT,EMBL-EBI,SCOP: Structural Classification of Proteins,ChEMBL,\"PDBePISA (Proteins, Interfaces, Structures and Assemblies) / PDBeFold\",HMMER,CATH","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":68,"Resource_Name":"BioExcel Summer School on Biomolecular Simulations 2021","ID":"68","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/bioexcel-summer-school-biomolecular-simulations-2021/","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"The summer school will include lectures and hands-on sessions on the following topics:\n- Molecular Dynamics simulations\n- Biomolecular Docking\n- Free energy calculations\n- Advanced sampling methods (Metadynamics)\n- BioExcel Building Blocks (BioBB)\n- Quantum mechanics/molecular mechanics (QM/MM)\n\nDuring the hands-on computer practicals you will make use of the BioExcel flagship software and tools: e.g. GROMACS, HADDOCK, PMX, BioBB, and CP2K. The trainers, developers and/or experts in the use of the software, will provide guidance and support.\n\nWho is this course for:\nThe summer school is intended for researchers (primarily PhD and post-docs) using or planning to use biomolecular modeling and simulation in their everyday research. Familiarity with Linux and basic knowledge of molecular modelling software is a requirement.\n\nLearning outcomes:\n- Use a range of biomolecular modeling and simulation software (GROMACS, HADDOCK, PMX, CP2K)\n- Discuss current trends and challenges in biomolecular simulation","Availability":"Sporadic","Date_or_Duration":"04 - 11 June 2021","Language":"English","Cost":"50 €","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Transcriptomics,Proteomics,Metabolomics,Cell Biology,Molecular Biology,Microbiology,Biochemistry,Biophysics,Immunology,Pharmacology,Chemistry","Tools_Addressed":"Gromacs,Biobb (BioExcel building blocks),PMX,HADDOCK (High Ambiguity Driven protein-protein DOCKing),CP2K","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":69,"Resource_Name":"Metagenomics bioinformatics","ID":"69","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/metagenomics-bioinformatics-virtual-2021/","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"Learn about the tools, processes and analysis approaches used in the field of metagenomics.\nThis course will cover the use of publicly available resources to manage, share, analyse and interpret metagenomics data; including marker gene, whole gene shotgun (WGS) and assembly-based approaches.\nThe virtually-delivered content will involve participants learning via pre-recorded lectures and live presentations, followed by live Q&As with the trainers. Practical experience will be developed in group activities and in computational exercises run in Docker containers on our virtual training infrastructure.\nParticipants will need to be available between the hours of 09:00-17:00 GMT each day of the course.\n\nWho is this course for:\nThis course is aimed at life scientists who are working in the field of metagenomics and are currently in the early stages of data analysis. Participants should have some prior experience of using bioinformatics in their research.\nThe practical sessions in the course require a basic understanding of the Unix command line and the R statistics package. If you are not already familiar with these then please ensure that you complete these free tutorials before you attend the course:\n- Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix\n- Basic R concept tutorials: www.r-tutor.com/r-introduction\n\nCourse content:\n- Different types of microbiome data:\n    - Amplicon approaches (ribosomal RNA)\n    - Whole genome shotgun (WGS) approaches\n- Assembly and metagenome assembled genomes (MAGs)\n- Data analysis: MGnify, HMMER, InterPro, GO, FASTQC, and pathway analyses\n- Data standards and submission:\n    - European Nucleotide Archive (ENA)\n    - Genomic Standards Consortium (GSC)\n    - SRA\n    - Webin\n- Microbiome data analysis workflow\n\nLearning outcomes:\n- Conduct appropriate quality control and decontamination of metagenomic data and run simple assembly pipelines on short read data\n- Utilise public datasets and resources to identify relevant data for analysis\n- Apply relevant tools in the analysis of metagenomic data\n- Submit metagenomics data to online repositories for sharing and future analysis\n- Apply knowledge in the areas of strain resolution and additional functional analysis","Availability":"Sporadic","Date_or_Duration":"08 - 12 November 2021","Language":"English","Cost":"£200","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Biostatistics,Transcriptomics,Proteomics,Metabolomics,Molecular Biology,Cell Biology,Oncology,Biomedicine,Marine Biology,Ecology,Environmental Biology,Systems Biology","Tools_Addressed":"EMBL-EBI,HMMER,MGnify,Interpro,FastQC,Gene Ontology Annotation (GOA),EMBL-EBI - The European Nucleotide Archive (ENA),Sequence Read Archive (SRA)","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":70,"Resource_Name":"Bioinformatics for Immunologists","ID":"70","Institution_and/or_Platform":"EMBL-EBI Training / Wellcome Genome Campus","Main_URL":"https://www.ebi.ac.uk/training/events/bioinformatics-immunologists2021/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"The course will provide participants with an overview of best-practice methods in applying bioinformatics approaches and enable them to become confident users of their own and public domain data. The resources introduced during the course will cover a variety of data types, from genomic and proteomic data; to computational models, biological pathways and reaction information. Participants will gain experience of the analysis pipelines for NGS experiments relevant to immunology, and will be led through an exploration of this data to identify information of interest. Additionally participants will be introduced to how data from a number of sources can be integrated to provide a wider view of their research.","Availability":"Sporadic","Date_or_Duration":"27 September - 01 October 2021","Language":"English","Cost":"(unknown)","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Microbiology,Molecular Biology,Immunology,Pharmacology,Biomedicine","Tools_Addressed":"EMBL-EBI","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":71,"Resource_Name":"Summer school in bioinformatics","ID":"71","Institution_and/or_Platform":"EMBL-EBI Training / Wellcome Genome Campus","Main_URL":"https://www.ebi.ac.uk/training/events/summer-school-bioinformatics-2021/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This virtual course, organised in association with Wellcome Genome Campus, Scientific Conferences and Advanced Courses, provides an introduction to the use of bioinformatics in biological research, giving participants guidance for using bioinformatics in their work whilst also providing hands-on training in tools and resources appropriate to their research.\nParticipants will initially be introduced to bioinformatics theory and practice, including best practices for undertaking bioinformatics analysis, data management and reproducibility. To enable specific exploration of resources in their particular field of interest, participants will be divided into focused groups to work on a small project set by EMBL-EBI resource and research staff, ending in a presentation from each group on the final day of the course to bring together learnings from all participants.\nThe course includes training and mentoring by experts from EMBL-EBI and external institutes.\n\nCourse content:\n- Bioinformatics as a science\n- Designing bioinformatics studies\n- Data management and reproducibility\n- Basic tools and resources for bioinformatics\n\nLearning outcomes:\n- Discuss applications of bioinformatics in biological research\n- Browse, search, and retrieve biological data from public repositories\n- Use appropriate bioinformatics tools to explore biological data\n- Comprehend ways that biological data can be stored, organised and integrated","Availability":"Sporadic","Date_or_Duration":"28 June - 02 July 2021","Language":"English","Cost":"£200","Costs_Details":null,"Biotech_Fields":"All,Biomathematics/Bioinformatics/Computational Biology","Tools_Addressed":"EMBL-EBI","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":72,"Resource_Name":"Microscopy data analysis: Machine learning and the BioImage Archive","ID":"72","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/microscopy-data-analysis/","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"Course oriented to develop programmatic skills for the analysis of bioimage data.\n\nThis course will introduce programmatic approaches used in the analysis of bioimage data via the BioImage Archive. The content will explore a variety of data types including electron microscopy, cell and tissue microscopy, and miscellaneous or multi-modal imaging data. Participants will cover contemporary biological image analysis with an emphasis on machine learning and advanced image analysis. Further instruction will be offered using applications such as ZeroCostDL4Mic, ilastik, the BioImage Model Zoo, and CellProfiler.\n\nThe organisers welcome applications from scientists who have specific research questions that look to use publicly available data held in the BioImage Archive and can demonstrate use cases for the resource.\n\nWho is this course for:\nThis course is aimed at scientists working with bioimage data across the life sciences. It is suitable for those involved in creating bioimages or taking their first steps in analysis. The content would also be suitable for those wanting to learn more about the BioImage Archive and gain experience with machine learning approaches for image analysis. The programme will be of particular interest to bio-image analysts with questions relating to the use of ‘big data’ and using the wealth of publically available data curated in the BioImageArchive.\nThe course should be accessible to members of the bioimaging community and does not require prior experience with machine learning methods or use of the BioImage Archive is necessary, but applicants are encouraged to explore the resources below before starting their application. Applicants should be comfortable with basic programming tasks and have experience working with Python.\n\nPrerequisite reading:\n- Nature: BioImage Archive: A call for public archives for biological image data\n- biorxiv: ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy\n- Nucleic Acids Research: The BioStudies database—one stop shop for all data supporting a life sciences study\n- Nature Methods: EMPIAR: a public archive for raw electron microscopy image data\n- Nature: Image Data Resource: a bioimage data integration and publication platform\n- BioModelZoo\n\nCourse content:\n- Data repositories\n    - BioImage Archive\n    - Image Data Resource\n    - EMPIAR\n    - BioStudies\n- Analysis tools\n    - Binder\n    - Colaboratory\n    - Bioimage Model Zoo\n    - ZeroCostDL4Mic\n    - CellProfiler\n\n\nLearning outcomes: \n- Interact programmatically with the BioImage Archive and other data resources\n- Apply pre-built machine learning models to image data\n- Train and retrain machine learning models on image data\n- Utilise machine learning approaches for object detection, image segmentation and de-noising\n- Generate quantitative conclusions from images","Availability":"Sporadic","Date_or_Duration":"12 - 16 July 2021","Language":"English","Cost":"£100","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Cell Biology,Molecular Biology,Physiology,Neurobiology,Oncology,Biomedicine","Tools_Addressed":"EMBL-EBI,CellProfiler,ZeroCostDL4Mic,Bioimage Model Zoo,Google Colaboratory,Binder,BioStudies,EMPIAR (Electron Microscopy Public Image Archive),The Image Data Resource (IDR),BioImage Archive","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":73,"Resource_Name":"Curso Internacional: Estrategias bioinformáticas para el estudio de enfermedades tropicales desatendidas (ETDs)","ID":"73","Institution_and/or_Platform":"EMBL-EBI Training / CABANA","Main_URL":"https://www.ebi.ac.uk/training/events/curso-internacional-estrategias-bioinformaticas-para-el-estudio-de-enfermedades-tropicales/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"Este curso ofrecerá entrenamiento en herramientas y estrategias bioinformáticas para el estudio de enfermedades tropicales desde una perspectiva molecular. Las sesiones cubrirán el análisis de datos provenientes de múltiples repositorios ómicos con información relacionada sobre los patógenos causantes de las enfermedades. El objetivo principal es explorar metodologías para el descubrimiento de blancos moleculares e inhibidores que sirvan para modularlos, usando una perspectiva traslacional de los hallazgos computacionales. Información estructural de las proteínas y redes de interacción proteína-proteína serán cubiertas en detalle, además de otros recursos disponibles en la web. \n\nEl curso está dirigido para académicos de países latinoamericanos, que incluyen estudiantes de posgrados, postdocs e investigadores iniciando su carrera científica. En todos los casos, se prefiere que estén trabajando en metodologías moleculares para el estudio de enfermedades tropicales. Conocimiento en biología molecular es requerido, al igual que estar familiarizados con conceptos básicos en Bioinformática.\n\n--> Tenga en cuenta que este curso se impartirá en español. Sin embargo, algunos de los formadores dominan el inglés y/o el portugués, por lo que se ofrecerá apoyo lingüístico cuando sea posible.\n\nDurante este curso se aprenderá sobre: \n- Recursos bioinformáticos y bases de datos sobre patógenos\n- Bases de datos sobre blancos moleculares y compuestos\n- Herramientas para el estudio de péptidos antimicrobianos\n- Análisis de interacción de proteínas\n- Simulación de interacciones con docking molecular y herramientas de virtual screening\n- Uso de unix y comandos base\n\n\nLuego del curso podrán realizar las siguientes actividades:\n- Acceder a repositorios con información de patógenos con el fin de analizar información sobre sus proteínas y las rutas moleculares en las que están involucradas\n- Validar potenciales inhibidores y blancos de varias enfermedades con datos disponibles y herramientas de predicción.\n- Predecir y analizar propiedades básicas de péptidos antimicrobianos y sus efectos con ciertas enfermedades.\n- Construir y analizar interacciones proteína-proteína para estudiar propiedades de potenciales blancos moleculares.\n- Aplicar herramientas de bioinformática estructural para predecir interacciones con otras proteínas y moléculas pequeñas.","Availability":"Sporadic","Date_or_Duration":"21 - 25 June 2021","Language":"Español","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Transcriptomics,Proteomics,Metabolomics,Microbiology,Immunology,Pharmacology,Epidemiology,Biomedicine,Molecular Biology","Tools_Addressed":"EMBL-EBI,UNIX & Bash/Awk scripting,Autodock,SwissDock,HADDOCK (High Ambiguity Driven protein-protein DOCKing),Target Pathogen","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":74,"Resource_Name":"Proteomics bioinformatics","ID":"74","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/proteomics-bioinformatics-2021/","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"Hands-on training in the basics of mass spectrometry, proteomics bioinformatics, and related methods and resources\nRun jointly with Wellcome Genome Campus Advanced Courses and Scientific Conferences, this popular course provides hands-on training in the basics of mass spectrometry (MS) and proteomics bioinformatics approaches, search engines and post-processing software, quantitative proteomics approaches and related statistical concepts, MS proteomics data repositories and public data re-use, how to employ existing databases for protein analysis, perform the annotation of subsequent protein lists and the incorporation of information from molecular interaction and pathway databases.\n\nWho is this course for?\nThe course is aimed at research scientists with a minimum of a degree in a biological discipline, including laboratory and clinical staff, as well as specialists in related fields.\nThe practical elements of the course will take raw data from a proteomics experiment and analyse it. Participants will be able to go from MS spectra to identifying and quantify peptides and finally to obtain lists of protein identifiers that can be analysed further using a wide range of resources. The final aim is to provide attendees with the practical bioinformatics knowledge they need to go back to the lab and process their own data when collected.\n\nCourse content:\n- Mass Spectrometry Basics\n- Proteomics Bioinformatics Basics\n- Quantitative proteomics\n- Introduction to Data Independent Acquisition approaches\n- Standardisation of proteomics data\n- MS proteomics repositories, including PRIDE and PRIDE-related tools and ProteomeXchange\n- Introduction to Proteogenomics\n- Protein interaction data through IntAct and IMEX resources\n- Functional analysis of proteins using Cytoscape and Reactome\n\nLearning outcomes:\n- Use and understand bioinformatics tools to analyse shotgun proteomics data, involving identification and quantification approaches\n- Evaluate the strengths and weaknesses of several experimental and bioinformatics analysis approaches\n- Browse, search, submit, retrieve and re-use proteomics data from widely used public proteomics data repositories\n- Use tools to perform functional annotation of lists of proteins","Availability":"Sporadic","Date_or_Duration":"12 - 16 July 2021","Language":"English","Cost":"£200","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Transcriptomics,Proteomics,Metabolomics,Molecular Biology,Biochemistry,Biophysics","Tools_Addressed":"EMBL-EBI","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":75,"Resource_Name":"Systems biology: From large datasets to biological insight","ID":"75","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/systems-biology-large-datasets-biological-insight/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"This course covers the use of multi-omics data and methodologies in systems biology. The content will explore a range of approaches - ranging from network inference to machine learning - that can be used to extract biological insights from varied data types. Together these techniques will provide participants with a useful toolkit for designing new strategies to extract relevant information and understanding from large-scale biological data.\nThe motivation for running this course is a result of advances in computer science and high-performance computing that have led to groundbreaking developments in systems biology model inference. With the comparable increase of publicly-available, large-scale biological data, the challenge now lies in interpreting them in a biologically valuable manner. \nParticipants will learn via a mix of pre-recorded lectures, live presentations, and trainer Q&A sessions. Practical experience will be developed through group activities and trainer-led computational exercises. \n\nWho is this course for:\nThis course is aimed at advanced PhD students and post-doctoral researchers who are currently working with large-scale omics datasets with the aim of discerning biological function and processes. Ideal applicants should already have some experience (ideally 1-2 years) working with systems biology or related large-scale multi-omics data analyses.\nApplicants are expected to have a working knowledge of the Linux operating system and the ability to use the command line. Experience of using a programming language (i.e. Python) is highly desirable, and while the course will make use of simple coding or streamlined approaches such as Python notebooks, higher levels of competency will allow participants to focus on the scientific methodologies rather than the practical aspects of coding and how they can be applied in their own research.\nWe recommend these free tutorials:\n- Basic introduction to the Unix environment:\n    - www.ee.surrey.ac.uk/Teaching/Unix\n- Introduction and exercises for Linux:\n    - https://training.linuxfoundation.org/free-linux-training\n- Python turorial:\n    - https://www.w3schools.com/python/\n- R tutorial:\n    - https://www.datacamp.com/courses/free-introduction-to-r\n\nLearning outcomes: \n- Discuss and apply a range of data integration and reduction approaches for large-scale omics data\n- Apply different approaches to explore omics data at the network level\n- Describe principles behind different machine learning methods and apply them on omics datasets to extract biological knowledge\n- Infer biological models using statistical methods\n- Identify strengths and weaknesses of different inference approaches\n- Compare signal propagation through logic modelling vs diffusion-based approaches\n\nCourse content:\nThe course will include lectures, discussions, and practical computational exercises covering the following topics:\n- Data reduction and data integration methods – including comparisons of major approaches through lectures and practical exercises\n- Machine and deep learning – practical exercises on supervised machine learning, including classification and regression, and deep learning\n- Functional inference from omics data – approaches to extract signatures of cell state from omics data including transcription factor activation and kinase activity states. Extraction of upstream signaling pathways from transcriptomics datasets\n- Network inference and signal propagation – network inference approaches from omics data\n- Introduction to executable modeling – including how to fit omics data to executable and predictive logic models","Availability":"Sporadic","Date_or_Duration":"21 - 25 June 2021","Language":"English","Cost":"£200","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Molecular Biology,Biochemistry,Microbiology,Cell Biology,Systems Biology","Tools_Addressed":"EMBL-EBI,Jupyter,R Studio,Python,Cytoscape,MOFA (Multi‐Omics Factor Analysis),JIVE (Joint and individual variation explained)","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":76,"Resource_Name":">> Bioinformatics for Principal Investigators","ID":"76","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/bioinformatics-principal-investigators-virtual/","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Basic usage information","Content_Details":"This course has been designed to provide Principal Investigators (PI's) working in the life sciences with an introduction to the challenges of working with biological data as a research leader.\nStarting with a look at where bioinformatics and data science fits into their research teams, the course will enable PI’s to take a strategic look at the requirements for undertaking such work and how bioinformatics capacity can be developed from personnel through to computational needs. It will additionally provide guidance on strategies for managing data and the importance of data sharing; how to work with computational collaborators and what resources are available across the life sciences to support such work.\n--> During this course we will not be teaching hands-on bioinformatics analysis, but we will be signposting appropriate training resources to upskill you and your team.\n\nWho is this course for:\nThis course is aimed at both new and established investigators who lead a research team which currently uses bioinformatics, or where bioinformatics will be a component in future research. No prior knowledge of bioinformatics, or experience of analysis is required for this course. Applications are invited from investigators working in all settings, including academic, clinical and industrial organisations.\n\nLearning outcomes:\nAfter this course you should be able to:\n- Define the potential and pitfalls of bioinformatics use in your research\n- Identify your team needs in terms of bioinformatics use and support\n- Develop appropriate plans for the management and sharing of data\n- Support the development of bioinformatics skills within your team","Availability":"Sporadic","Date_or_Duration":"15 - 17 June 2021","Language":"English","Cost":"£120.00","Costs_Details":null,"Biotech_Fields":"All","Tools_Addressed":"EMBL-EBI","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":77,"Resource_Name":"Cancer genomics","ID":"77","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/cancer-genomics-virtual/","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"This course will focus on the analysis of data from genomic studies of cancer. Lectures and interactive sessions will give an insight into the bioinformatic concepts required to analyse such data, whilst practical sessions will enable the participants to apply statistical methods to the analysis of cancer genomics data under the guidance of the lecturers.\nVirtual course\nThe course will involve participants learning via pre-recorded lectures, live presentations, and trainer Q&A sessions. The content will be delivered over Zoom, with additional text communication over Slack.\nComputational practicals will be run on EMBL-EBI's virtual training infrastructure; this means there is no need to have a powerful computer to run exercises or a requirement to install complex software before the course. Trainers will be available to provide support, answer questions, and further explain the analysis during these practicals.\nParticipants will need to be available between the hours of 09:30-18:00 BST each day of the course.\n\nWho is this course for:\nThis course is aimed at advanced PhD students and post-doctoral researchers who are applying or planning to apply high throughput sequencing technologies in cancer research and wish to familiarise themselves with bioinformatics tools and data analysis methodologies specific to cancer data.\nFamiliarity with the technology and biological use cases of high throughput sequencing is required, as is some experience with R/Bioconductor (basic understanding of the R syntax and ability to manipulate R objects) and the Unix/Linux operating system.\n\nCourse content:\n- Application of high throughput sequencing (HTS) in cancer\n- Introduction to cancer genomics and epigenetics\n- Structural variation, SNV and CNV analysis and data visualisation\n- Application of CRISPR-Cas9 genome editing in studying cancer\n- RNA-seq analysis\n\nLearning outcomes:\n- Evaluate the applications and challenges of HTS in the study of cancer genomics\n- Detect, visualise and annotate copy number variation\n- Interpret complex genomic rearrangements such as structural variants\n- Indicate the principles of tumour purity, heterogeneity and evolution and how these influence/impact upon bioinformatics analysis\n- Perform alignment and quantification of expression of RNA-seq datasets","Availability":"Sporadic","Date_or_Duration":"17 - 21 May 2021","Language":"English","Cost":"£200","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics,Molecular Biology,Transcriptomics,Oncology,Pharmacology,Immunology,Biomedicine","Tools_Addressed":"EMBL-EBI","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":78,"Resource_Name":"Open Virtual Ensembl Browser Workshop","ID":"78","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/open-virtual-ensembl-browser-workshop/","Resource_Type":"Course","Modality":"Online","Level":"Basic usage information","Content_Details":"Work with the Ensembl Outreach team to get to grips with the Ensembl browser, accessing gene, variation, comparative genomics and regulation data, and mine these data with BioMart.\n\nWho is this course for:\nWet-lab researchers and bioinformaticians\n\nLearning outcomes:\n- view genomic regions and manipulate the view to add features they are interested in.\n- explore information about genes and their sequences, and gene data in bulk using BioMart.\n- analyse genomic variants and associated phenotypes and their own variation data using the VEP.\n- view homologous genes and genomic regions, functional elements involved in gene regulation and their activity in different cell types.\n\nCourse content:\nThe Ensembl project at www.ensembl.org provides a comprehensive and integrated source of annotation of mainly vertebrate genome sequences. This workshop offers participants the possibility of gaining hands-on experience in the use of the Ensembl genome browser but also provides them with the necessary background information. Our sister project at www.ensemblgenomes.org can also be covered if participants are working with bacteria, plants, fungi, protists or (invertebrate) metazoa.\nThe workshop is primarily targeted at wetlab researchers, and we customise the course for species of interest and to include total beginners to our browser up through frequent users.","Availability":"Sporadic","Date_or_Duration":"18 - 20 May 2021","Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"All","Tools_Addressed":"EMBL-EBI,EMBL-EBI - The European Nucleotide Archive (ENA),EMBL-EBI - ArrayExpress,ChEMBL,IntAct Molecular Interaction Database (EMBL-EBI),\"PDBePISA (Proteins, Interfaces, Structures and Assemblies) / PDBeFold\"","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":79,"Resource_Name":"A guide to identifying and prioritising drug targets with the Open Targets Platform","ID":"79","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/events/guide-identifying-and-prioritising-drug-targets-open-targets-platform/","Resource_Type":"Webinar/Video Tutorial","Modality":"Online","Level":"Basic usage information","Content_Details":"This webinar will provide an overview of the Open Targets Platform which is a freely available resource that is actively maintained with bi-monthly data updates. All data is available through an intuitive user interface, a GraphQL API, and data downloads. Likewise, the pipeline and infrastructure codebases are open-source and can be used to create a self-hosted private instance of the Platform with custom data.\n\nWho is this course for:\nThis webinar is suitable for lab-based and computational research scientists, graduate students, and post-doctoral research fellows working in early stage drug discovery.\n\nBy the end of the webinar you will be able to:\n- Identify the types of ‘omics data integrated into the Platform \n- Explore target-disease associations and supporting evidence available in the Platform\n- List different ways to access the Platform data","Availability":"Sporadic","Date_or_Duration":"19 May 2021","Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"Pharmacology,Immunology,Biomedicine","Tools_Addressed":"Open Targets Platform,EMBL-EBI","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":80,"Resource_Name":"Bringing data to life - Data management for the biomolecular sciences","ID":"80","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/online/courses/bringing-data-life-data-management-biomolecular-sciences/","Resource_Type":"Webinar/Video Tutorial","Modality":"Online","Level":"Basic usage information","Content_Details":"This course is aimed at biological researchers who are working with, or managing data and those who need to create data management plans. No prior knowledge of bioinformatics is required but an understanding of biology and biological research would be an advantage.\nThis online tutorial is part of the Introductory bioinformatics pathway: A curated set of EMBL-EBI online courses, which provides an introduction to bioinformatics, a brief tour of the resources available from EMBL-EBI and more details about some of those resources, including Ensembl, UniProt and Expression Atlas.\n\nBy the end of the course you will be able to:\n- Describe the data management cycle\n- Discuss benefits and challenges of data sharing\n- Explain what happens to data after it is shared\n- Identify appropriate data repositories and their submission requirements\n- Describe how ontologies and standards are used to annotate biological data\n- Identify appropriate resources and tools for data management","Availability":"On demand","Date_or_Duration":"+3 hrs","Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"All","Tools_Addressed":"EMBL-EBI,UniProt,EMBL-EBI - ArrayExpress","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":81,"Resource_Name":"EMBL-EBI, programmatically","ID":"81","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/online/courses/embl-ebi-programmatically/","Resource_Type":"Webinar/Video Tutorial","Modality":"Online","Level":"Basic usage information","Content_Details":"This course consists of a series of recorded webinars as well as documentation, example scripts and live examples. You do not need to watch all of the webinars; you can dip in and out and watch only the webinars you are interested in.\nFor the introductory webinar all you need is an undergraduate understanding of biology. The resource focused webinars are more computationally advanced and you will need prior knowledge of programmatic access and common programming languages. If you are new to programmatic access we recommend that you watch the introductory webinar first.\n\nBy the end of the course you will be able to:\n- List available EMBL-EBI webservices\n- Describe processes that can be performed using EMBL-EBI RESTful APIs\n- Know where to find help, documentation and get support\n- Access example scripts","Availability":"On demand","Date_or_Duration":"+3 hrs","Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"All","Tools_Addressed":"EMBL-EBI","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":82,"Resource_Name":"Introductory bioinformatics pathway","ID":"82","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/online/courses/introductory-bioinformatics-pathway/","Resource_Type":"Webinar/Video Tutorial","Modality":"Online","Level":"Basic usage information","Content_Details":"This pathway is for anyone with an interest in learning about bioinformatics and EMBL-EBI resources. No prior skills are required, but undergraduate level knowledge of life sciences would be useful. \n\nBy the end of the course you will be able to:\n- Outline what bioinformatics is\n- Describe the importance of data management\n- Recall which resources are available from EMBL-EBI\n- Know where to find information about genes\n- View information on gene expression\n- Search for protein information\n- Know where to find out more about EMBL-EBI resources","Availability":"On demand","Date_or_Duration":"+3 hrs","Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"All","Tools_Addressed":"EMBL-EBI","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":83,"Resource_Name":"ResOps - Cloud-native tools and technology for researchers","ID":"83","Institution_and/or_Platform":"EMBL-EBI Training","Main_URL":"https://www.ebi.ac.uk/training/online/courses/resops-cloud-native-tools-and-technology-for-researchers/","Resource_Type":"Webinar/Video Tutorial,Course","Modality":"Online","Level":"Basic usage information","Content_Details":"This course will provide an introduction to cloud computing and some practical experience in building, deploying and running applications in cloud platforms - OpenStack, Google, Amazon and Azure.\n\nThis course is for those in the research community who would like to develop their cloud native skills, you will need knowledge of Linux commands and GIT repository commands. This course was developed as part of the EOSC-Life project; EOSC-Life has received funding from the European Union’s Horizon 2020 programme under grant agreement number 824087.\n \nBy the end of the course you will be able to:\n- Discuss user-centric hybrid cloud strategy\n- Describe architectural considerations around porting applications to a cloud\n- Deploy across different clouds\n\nResources needed:\n- Installation of Minikube and GIT, on your own laptop or your VM for the hands-on exercises\n- If you are using a VM, please use at least 1vCPU, RAM 4GB and storage as per your choice\n- An account should be created on Public GitLab CI/CD practical with GitLab\n- A simple editor to be able to edit files during the exercises","Availability":"On demand","Date_or_Duration":"+3 hrs","Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology","Tools_Addressed":"EMBL-EBI,UNIX & Bash/Awk scripting","Addittional_URLs":"For more training from the EMBL-EBI: https://www.ebi.ac.uk/training/"},{"_id":84,"Resource_Name":"A guide to identifying and prioritising drug targets with the Open Targets Platform","ID":"84","Institution_and/or_Platform":"EMBL-EBI","Main_URL":"https://www.ebi.ac.uk/training/events/guide-identifying-and-prioritising-drug-targets-open-targets-platform/","Resource_Type":"Webinar/Video Tutorial","Modality":"Online","Level":"Basic usage information,Introductory to the field/topic","Content_Details":"The Open Targets Platform - https://platform.opentargets.org/ - is a comprehensive research tool that supports systematic identification and prioritisation of potential therapeutic drug targets.‌ By integrating publicly available datasets along with data generated by the Open Targets consortium, the Platform builds and scores target-disease associations. Users can also explore relevant annotation information about targets, diseases, phenotypes, and drugs, as well as their most relevant relationships.\nThis webinar will provide an overview of the Open Targets Platform which is a freely available resource that is actively maintained with bi-monthly data updates. All data is available through an intuitive user interface, a GraphQL API, and data downloads. Likewise, the pipeline and infrastructure codebases are open-source and can be used to create a self-hosted private instance of the Platform with custom data.\nWho is this course for?\nThis webinar is suitable for lab-based and computational research scientists, graduate students, and post-doctoral research fellows working in early stage drug discovery.\nOutcomes\nBy the end of the webinar you will be able to:\n- Identify the types of ‘omics data integrated into the Platform \n- Explore target-disease associations and supporting evidence available in the Platform\n- List different ways to access the Platform data","Availability":"On demand","Date_or_Duration":null,"Language":"English","Cost":"FREE","Costs_Details":null,"Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Molecular Biology,Immunology,Pharmacology,Oncology,Biomedicine","Tools_Addressed":"Open Targets Platform","Addittional_URLs":null},{"_id":85,"Resource_Name":"Long-read Bioinformatics","ID":"85","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/long-read-bioinformatics","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"This exciting new course aims to introduce the principles and practice using long-read data analysis with focus on Oxford Nanopore data. We will present the cutting edge software and best practices tried and tested by our expert bioinformaticians here at Edinburgh Genomics. \n\nWhole genome sequencing (WGS) has been revolutionised by the development of long-read sequencing technologies in the last few years. Driven in no small part by Oxford Nanopore technologies (https://nanoporetech.com/), we now have the ability to sequence long (mb+) single-molecule DNA fragments. Although these developments are expected to alleviate numerous computational challenges surrounding genome analysis they also bring some interesting bioinformatics challenges to which we have to adapt in order to get the most from this powerful technology.\n\nWho this course is for:  Aside from a basic understanding of molecular biology, attendees must have a working knowledge of how to use the Linux BASH command line - our 1-day \"Linux for bioinformatics\" course is a suitable background.\n\nTopics covered:\n- Introduction to long read sequencing technologies\n- Long read data pre-processing (Nanopore)\n- Mapping to a reference genome (minimap2)\n- Structural variant detection using long reads (NGMLR, sniffles, cuteSV, SURVIVOR, IGV)\n- De novo genome assembly using long reads (flye, Redbean)\n- Assessment of genome assembly (QUAST/BUSCO)\n- Polishing of genome assembly (MarginPolish/HELEN)","Availability":"Sporadic","Date_or_Duration":"17 - 19 May","Language":"English","Cost":"General: £195","Costs_Details":"Uof Edinburgh/ Academic/ other: £150/£158/£195","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genomics","Tools_Addressed":"UNIX & Bash/Awk scripting,cuteSV,Oxford Nanopore Tools,SURVIVOR,Sniffles,NGMLR,Minimap2,Flye,Redbean,QUAST – Quality Assessment Tool for Genome Assemblies,BUSCO,POLCA (POLishing by Calling Alternatives),H.E.L.E.N. (Homopolymer Encoded Long-read Error-corrector for Nanopore)","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":86,"Resource_Name":"R for Biologists","ID":"86","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/r-biologists","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"The aim of this course is to introduce participants to the statistical computing language 'R' using examples and skills relevant to biological data science. This online workshop is taught by experienced Edinburgh Genomics’ trainers. By the end of the workshop, you will be comfortable with the basics of the R and R studio environments, learning about the rules of the language and how R works with different data types and structures. We then move on to using functions, introducing a selection of packages for biological data science including the 'tidyverse' family of packages. Finally, we will learn how to visualise your data to generate publication-ready plots using the package ggplot2.\nThis is an introductory level course: no prior experience of R is necessary before starting the workshop.","Availability":"Sporadic","Date_or_Duration":"24 - 28 May","Language":"English","Cost":"General: £325","Costs_Details":"Uof Edinburgh/ Academic/ other: £250/£263/£325","Biotech_Fields":"All","Tools_Addressed":"R Studio","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":87,"Resource_Name":"Introduction to Metagenomic Data Analysis","ID":"87","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/introduction-metagenomic-data-analysis","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"Requirements:\n- A general understanding of molecular biology and genomics.\n- A working knowledge of Linux at the level of the Edinburgh Genomics Linux for Genomics workshop.\n\nContent: \n- Data QC and Preprocessing of short reads\n- Taxonomic profiling using MetaPhlAn3\n- Functional profiling using HUMAnN3\n- Metagenome assembly using short reads using megahit\n- Contigs binning and generation of metagenome assembled genomes (MAGs)\n- Quality assessment of MAGs using CheckM\n- De-replication of MAGs\n- Taxonomic classification with GTDB-Tk\n- Data QC and Preprocessing of long reads\n- Metagenome assembly using long reads (Oxford Nanopore) using metaFlye\n- Polishing long read assembly with Marginpolish , HELEN and Racon\n- Assess the quality of assemblies using QUAST and IDEEL","Availability":"Sporadic","Date_or_Duration":"31 May - 2June","Language":"English","Cost":"General: £325","Costs_Details":"Uof Edinburgh/ Academic/ other: £250/£263/£325","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Transcriptomics,Metabolomics,Molecular Biology,Ecology,Microbiology,Environmental Biology","Tools_Addressed":"FastQC,MetaPhlAn,HUMAnN,Megahit,CheckM,Flye,Oxford Nanopore Tools,H.E.L.E.N. (Homopolymer Encoded Long-read Error-corrector for Nanopore),POLCA (POLishing by Calling Alternatives),QUAST – Quality Assessment Tool for Genome Assemblies,Genome Taxonomy Database (GTDB),GTDB-tx,Racon,More metagenomic tools","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":88,"Resource_Name":"Introduction to Python for Biologists","ID":"88","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/introduction-python-biologists-0","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This workshop is aimed at complete beginners and assumes no prior programming experience. It gives an overview of the language with an emphasis on practical problem-solving, using examples and exercises drawn from various aspects of bioinformatics work. The workshop is structured so that the parts of the language most useful for bioinformatics are introduced as early as possible, and that students can start writing plausibly-useful programs after the first few sessions. After completing the workshop, students should be in a position to (1) apply the skills they have learned to tackling problems in their own research and (2) continue their Python education in a self-directed way.\n\nWorkshop format\nThe workshop is delivered over five taught days plus a final workshop day, running 10 - 4pm with a break for lunch at 12:30. After an introductory lecture for each module the time will mostly be devoted to practical exercises. Each session uses examples and exercises that build on material from the previous one, so it’s important that students attend all sessions. A description of the sessions can be found at the bottom of this page.\nWho should attend\nThis workshop is aimed at researchers and technical workers with a background in biology who want to learn programming. The syllabus has been planned with complete beginners in mind; people with previous programming experience are welcome to attend as a refresher but may find the pace a bit slow. If in doubt, take a look at the detailed session content below.\nRequirements\nStudents should have enough biological/bioinformatics background to appreciate the examples and exercise problems (i.e. they should know what a protein accession number, BLAST report, and FASTA sequence is). No previous programming experience or computer skills (beyond the ability to use a text editor) are necessary. The workshop uses Jupyter Notebooks for Python3, which we'll ask you to install on your own computer prior to the course. Full instructions and support will be provided.","Availability":"Sporadic","Date_or_Duration":"2021 TBC","Language":"English","Cost":"General: £585","Costs_Details":"Uof Edinburgh/ Academic/ other: £450/473/585","Biotech_Fields":"All","Tools_Addressed":"NCBI - BLAST,Jupyter,Python","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":89,"Resource_Name":"Introduction to Linux for Genomics","ID":"89","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/introduction-linux-genomics-virtual-edition-0","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"The Linux command-line enables one to view, filter and manipulate large text files that are difficult or impossible to handle with applications like Word or Excel, write pipelines to perform certain tasks, and run bioinformatics software for which no web interface is available. In this workshop we will first cover the most used Linux commands, followed by a short introduction to several popular command-line tools that were especially developed for genomics as well as file formats commonly used in genomics (BED, FASTA, FASTQ, GFF/GTF, SAM/BAM, VCF).\n\nRequirements:\nA general understanding of molecular biology and genomics, and elementary skills in computer usage are required.\nA computer with stable internet connection and a VNC viewer (download instructions included)\n \nTopics covered: \n- The shell and commands\n- Getting help\n- Files and directories\n- Navigating the file system\n- File management\n- Permissions\n- Accessing files\n- Downloading remote files\n- Zipping and unzipping files\n- Pipes and redirects\n- Filtering / manipulating file content\n- Shell scripts\n- Process management\n- Command-line tools for genomics (seqtk, bioawk, samtools, bedtools, tabix)","Availability":"Sporadic","Date_or_Duration":"2021 TBC","Language":"English","Cost":"General: £95","Costs_Details":"Uof Edinburgh/ Academic/ other: £75/£79/£95","Biotech_Fields":"All,Biomathematics/Bioinformatics/Computational Biology,Genomics","Tools_Addressed":"UNIX & Bash/Awk scripting","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":90,"Resource_Name":"RNA-seq Data Analysis","ID":"90","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/rna-seq-data-analysis","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"RNA sequencing (RNA-seq) is quickly becoming the method of choice for transcriptome profiling. Nevertheless, it is a non-trivial task to transform the vast amount of data obtained with high-throughput sequencers into useful information. Thus, RNA-seq data analysis is still a major bottleneck for most researchers in this field. The ability of correctly interpreting RNA-seq results, as well as knowledge on the intrinsic properties of these data, are essential to avoid incorrect experimental designs and the application of inappropriate analysis methodologies. The aim of this workshop is to familiarise researchers with RNA-seq data and to initiate them in the analysis by providing lectures and practicals on analysis methodologies. In the practicals Illumina-generated sequencing data and various widely used software programs will be used.\n\nRequirements\n- A general understanding of molecular biology and genomics.\n- A working knowledge of Linux at the level of the Edinburgh Genomics Linux for Genomics workshop.\n- A rudementary knowledge of R. Although we go through some introductory R, it would be very beneficial to have some experience with the R environment before the start of the course. \n \nCovered topics (and software)\n- Introduction to Next Generation Sequencing\n- Quality control and data pre-processing (FastQC, cutadapt)\n- Mapping to a reference genome (STAR, SAMtools)\n- Visualisation of mapped reads (SAMtools, IGV)\n- Introduction to R (R)\n- Estimating gene count (featureCounts)\n- Differential expression analysis (R, RStudio, edgeR, rtracklayer, ggplot2, pheatmap)\n- Functional analysis (GSEABase)","Availability":"Sporadic","Date_or_Duration":"2021 TBC","Language":"English","Cost":"General: £325","Costs_Details":"Uof Edinburgh/ Academic/ other: £250/£263/£325","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Molecular Biology","Tools_Addressed":"SAMseq,FastQC,StAR,Integrative Genomics Viewer (IGV),R Studio,EdgeR,Bioconductor,Samtools","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":91,"Resource_Name":"Variant Analysis","ID":"91","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/variant-analysis-online-course","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"This course aims to provide an introduction to the principles of short variant discovery (both germline and somatic) from short read data. We will look at a complete workflow, from data QC to functional interpretation of variant calls. The practical sessions will focus on running the GATK pipeline from the Broad institute.\nhttps://gatk.broadinstitute.org/hc/en-us\n\nWho this course is for:\nThis course is intended for researchers who need to analyse genomic data in order to call genomic variants. Aside from a basic understanding of molecular biology, attendees must have a working knowledge of how to use the Linux BASH command line - our 1-day \"Linux for bioinformatics\" course is a suitable background.\n\nTopics covered:\n- Introduction to short read data\n- Whole genome sequencing (WGS) data QC\n- Data preprocessing\n- Short variant discovery\n- Germline joint variant calling\n- Genotype refinement\n- Variant filtering and evaluation\n- Variant annotation and interpretation\n- Somatic short variant discovery\n- Somatic copy number variant discovery","Availability":"Sporadic","Date_or_Duration":"2021 TBC","Language":"English","Cost":"General: £325","Costs_Details":"Uof Edinburgh/ Academic/ other: £250/£263/£325","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Molecular Biology","Tools_Addressed":"GATK,NCBI,NCBI - GenBank,EMBL-EBI,EMBL-EBI - The European Nucleotide Archive (ENA),UNIX & Bash/Awk scripting","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":92,"Resource_Name":"Long-read Transcriptomic Data Analysis","ID":"92","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/long-read-transcriptomics","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"The recent development of high throughput long read RNA sequencing promises a new age of transcriptome exploration. Accurate high throughput long read RNA sequencing now has the potential to investigate genes that were previously undetectable.\nThis exciting new course aims to introduce the principles and practice of long-read transcriptomic anlysis using the cutting edge software TAMA (https://github.com/GenomeRIK/tama). \n\nWho this course is for:\nAside from a basic understanding of molecular biology, attendees must have a working knowledge of how to use the Linux BASH command line - our 1-day \"Linux for bioinformatics\" course is a suitable background.\n\nTopics covered: \n- Introduction to Pacific Bioscience and Oxford Nanopore Technologies RNA sequencing\n- Long read data pre-processing (Pacbio and Nanopore)\n- Transcriptome assembly (TAMA collapse)\n- Merging Transcriptomes (TAMA Merge)\n- Linking gene models to known proteins (TAMA ORF/NMD Pipeline)\n- Transcriptome filtering","Availability":"Sporadic","Date_or_Duration":"2021 TBC","Language":"English","Cost":"General: £195","Costs_Details":"Uof Edinburgh/ Academic/ other: £150/£158/£195","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Molecular Biology","Tools_Addressed":"UNIX & Bash/Awk scripting,TAMA","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":93,"Resource_Name":"Advanced Python for Biologists","ID":"93","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/advanced-python-biologists","Resource_Type":"Course","Modality":"Online","Level":"Advanced training","Content_Details":"This workshop is aimed at people who already have a basic knowledge of Python and are interested in using the language to tackle larger problems. In it, we will look in detail at the parts of the language which are particularly useful in scientific programming, and at the tools Python offers for making development faster and easier. The workshop will use examples and exercises drawn from various aspects of bioinformatics work. After completing the workshop, students should be in a position to (1) take advantage of the advanced language features in their own programs and (2) use appropriate tools when developing software programs.\n\nRequirements:\nStudents should have enough biological/bioinformatics background to appreciate the examples and exercise problems (i.e. they should know what a protein accession number, BLAST report, and FASTA sequence is). They should also have basic Python experience (the Edinburgh Genomics Introduction to Python for Biologists course will fulfil these requirements). Students should be familiar with the use of lists, loops, functions and conditions in Python and have written at least a few small programs from scratch. During the workshop students will use their own laptops. Instructions for any software to be installed will be sent out prior to the course.","Availability":"Sporadic","Date_or_Duration":"2021 TBC","Language":"English","Cost":"(unknown)","Costs_Details":"Uof Edinburgh/ Academic/ other: £TBC","Biotech_Fields":"All,Biomathematics/Bioinformatics/Computational Biology","Tools_Addressed":"Jupyter,NCBI - BLAST,Python","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":94,"Resource_Name":"Data Science for Everyone","ID":"94","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/data-science-everyone","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This course is an excellent introduction to data handling and analysis using Python, ragardless of what field you work in or what coding experience you have.\nPython is a dynamic, readable language that is a popular platform for all types of data analysis work, from simple one-off scripts to large, complex software projects. One of the strengths of the Python language is the availability of mature, high-quality libraries for working with scientific data. Integration between the most popular libraries has lead to the concept of a \"scientific Python stack\": a collection of packages which are designed to work well together.\nThis workshop is split into two sections. In the first two days we will introduce the basics of the Python language for those new to programming. For students with previous experience of Python or other languages, this will serve as a refresher and a chance to discuss best practice and focus on the parts of the language that we will need later.\nIn the second two days we will see how to leverage the libraries in the scientific Python stack to efficiently work with and visualise large volumes of data. \n\nSpecifically, we will cover:\n   • pandas for reading, cleaning and manipulating tabular data\n   • numpy for efficiently working with arrays of data\n   • scipy for basic statistics\n   • seaborn and matplotlib for data visualization\n\nWho should attend: \nThis workshop is aimed at complete beginners and assumes no prior programming experience. Rather than attempting to give a comprehensive overview of Python, we will instead concentrate on how best to use existing libraries to accomplish a lot while writing a very small amount of code.","Availability":"Sporadic","Date_or_Duration":"2021 TBC","Language":"English","Cost":"(unknown)","Costs_Details":"Uof Edinburgh/ Academic/ other: £TBC","Biotech_Fields":"All","Tools_Addressed":"Python,Jupyter","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":95,"Resource_Name":"Investigating the structure and function of Proteins, RNA and DNA using Jalview","ID":"95","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"http://genomics.ed.ac.uk/services/investigating-structure-and-function-proteins-rna-and-dna-using-jalview#","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"Jalview (www.jalview.org) is one of the most widely used applications for visualising and analysing DNA, RNA and protein multiple sequence alignments. In a new online workshop format you will learn key Jalview techniques for the creation, analysis and communication of multiple sequence alignments, prediction of protein secondary structure and disorder, and visualisation of genomic variation in coding regions and mapping them onto 3D structure.\n\nWho should attend: The course is aimed at MRes/PhD students, researchers and educators with little or no previous experience with Jalview but with a basic understanding of molecular biology and biochemistry. Experienced users are welcome to join later sessions to learn about newly added capabilities.","Availability":"Sporadic","Date_or_Duration":"2021 TBC","Language":"English","Cost":"(unknown)","Costs_Details":"Uof Edinburgh/ Academic/ other: £TBC","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Molecular Biology","Tools_Addressed":"Jalview","Addittional_URLs":"Link to main training page from the University of Edinburgh for more updated information: \nhttps://genomics.ed.ac.uk/services/training"},{"_id":96,"Resource_Name":"Data Manipulation and Visualisation with Python","ID":"96","Institution_and/or_Platform":"The University of Edinburgh","Main_URL":"https://genomics.ed.ac.uk/services/data-manipulation-and-visualisation-python","Resource_Type":"Course","Modality":"Presential","Level":"Advanced training","Content_Details":"The Advanced course is aimed at people who want to develop bigger or more complicated programs in Python, or to learn more about the language, or to explore different approaches (object-oriented, functional) to programming. The material covered is very general purpose and can be applied to any kind of problem. The Dataviz course is about using a set of Python libraries that are specifically designed for data exploration and visualisation. In the Dataviz course, we're going to concentrate on using these tools to explore patterns in data, but the actual code that we write will be very simple - mostly we will be calling the functions and methods in these libraries.\nTo summarise: if you want to learn more about the language, build more complicated programs, or need to work with existing complicated programs, attend the Advanced course. If you want to explore datasets, find patterns, produce figures and charts, attend the Dataviz course.\n \nRequirements:\nStudents should have enough biological/bioinformatics background to appreciate the example datasets. They should also have some basic Python experience (the Edinburgh Genomics Introduction to Python for Biologists course will fulfil these requirements). Students should be familiar with the use of lists, loops, functions and conditions in Python and have written at least a few small programs from scratch. During the workshop students will use their own laptops. Instructions for any software to be installed will be sent out prior to the course.","Availability":"Sporadic","Date_or_Duration":"TBC","Language":"English","Cost":"£550","Costs_Details":null,"Biotech_Fields":"All","Tools_Addressed":"Jupyter,NCBI - BLAST","Addittional_URLs":null},{"_id":97,"Resource_Name":"An Introduction to Solving Biological Problems with Python","ID":"97","Institution_and/or_Platform":"University of Cambridge","Main_URL":"https://training.csx.cam.ac.uk/bioinformatics/event/3894072","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core concepts of Python including Python syntax, data structures and reading/writing files. These are illustrated by a series of example programs. Upon completion of the course, participants will be able to write simple Python programs.\n\nTarget audience\n- Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals\nPrerequisites\n- The course is aimed for beginners and assumes no prior programming experience.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"10 Jun - 11 Jun 2021 (multiple times a year)","Language":"English","Cost":"£ 50/day","Costs_Details":"Free for registered University of Cambridge students\n£ 50/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level\nIt remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.\n£ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration\n£ 100/day for all Industry participants. These charges must be paid at registration","Biotech_Fields":"All","Tools_Addressed":"Python,Jupyter","Addittional_URLs":"More courses by the University of Cambridge: https://training.csx.cam.ac.uk/bioinformatics/search"},{"_id":98,"Resource_Name":"Complex Network Analysis for Biologists","ID":"98","Institution_and/or_Platform":"University of Cambridge","Main_URL":"https://training.csx.cam.ac.uk/bioinformatics/event/3879479","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic,Advanced training","Content_Details":"Complex natural systems permeate many aspects of everyday life—including human intelligence, social media, biomedicine, agriculture, economics, even our personal and professional relationships. The past decade has seen intensification of research into structural and dynamical properties of complex networks. This course will introduce the basic principles of network theory, and hands-on DIY Network analysis using Cytoscape, one of the most widely used global platforms for construction and analysis of biomolecular networks such as gene regulatory interactions, protein complexes, hydrogen-bonding meshwork in active sites and neuronal networks. The aim is to conceptualize your own textual, tabular or genomic datasets as networks, and to understand how simple topological features can help to decipher complex properties of systems and processes.\n\nTarget audience\n- This tutorial is basic and requires no prior knowledge of any coding language or software.\n- Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"7 jun 2021 (yearly)","Language":"English","Cost":"£ 50/day","Costs_Details":"Free for registered University of Cambridge students\n£ 50/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level\nIt remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.\n£ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration\n£ 100/day for all Industry participants. These charges must be paid at registration","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Molecular Biology,Cell Biology,Biomedicine,Systems Biology","Tools_Addressed":"Cytoscape","Addittional_URLs":"More courses by the University of Cambridge: https://training.csx.cam.ac.uk/bioinformatics/search"},{"_id":99,"Resource_Name":"Next Generation Sequencing Platforms and Bioinformatics Analysis","ID":"99","Institution_and/or_Platform":"University of Cambridge","Main_URL":"https://training.csx.cam.ac.uk/bioinformatics/event/3894975","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"Day 1 will introduce you to next generation sequencing technologies (NGS) and how they work, providers, common bioinformatics workflows, standardised file types, quality control. This session will include an introduction to Galaxy. Galaxy is an open, web-based platform for data-intensive life science research that enables non-bioinformaticians to create, run, tune, and share their own bioinformatic analyses.\nDay 2 will be hands-on practicals on using Galaxy to explore sequencing quality control, before and after removal of low quality samples. This forms the core of all NGS analyses and this day will conclude with how this data pipes into gene expression studies, variant calling and genome assemblies.\n\nTarget audience\n- Biologists, wet-lab scientists, bioinformatics and other life scientists planning to work with next-generation sequencing data.\n- Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals.\n\nPrerequisites\n- An undergraduate level understanding of molecular biology is required.\n- No prior experience with using Galaxy is required.","Availability":"Timed (yearly/monthy/etc)","Date_or_Duration":"8 Jun - 9 Jun 2021 (yearly)","Language":"English","Cost":"£ 50/day","Costs_Details":"Free for registered University of Cambridge students\n£ 50/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level\nIt remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.\n£ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration\n£ 100/day for all Industry participants. These charges must be paid at registration","Biotech_Fields":"Biomathematics/Bioinformatics/Computational Biology,Genetic Engineering,Genomics,Transcriptomics,Proteomics,Metabolomics,Molecular Biology,Cell Biology,Biomedicine,Systems Biology","Tools_Addressed":"Galaxy","Addittional_URLs":"More courses by the University of Cambridge: https://training.csx.cam.ac.uk/bioinformatics/search"},{"_id":100,"Resource_Name":"An Introduction to MATLAB for biologists","ID":"100","Institution_and/or_Platform":"Elixir / University of Cambridge","Main_URL":"https://tess.elixir-europe.org/events/an-introduction-to-matlab-for-biologists-online-live-training","Resource_Type":"Course","Modality":"Online","Level":"Introductory to the field/topic","Content_Details":"This course aims to give you an introduction to the basics of Matlab. During the two day course we will use a practical based approach to give you the confidence to start using Matlab in your own work. In particular we will show you how to write your own scripts and functions and how to use pre-written functions. We will also explore the many ways in which help is available to Matlab users. In addition we will cover basic computer programming in Matlab to enable you to write more efficient scripts.\n\nTarget audience:\n- Graduate students\n- Postdocs and Staff members from the University of Cambridge\n- Institutions and other external Institutions or individuals\n\n--> University of Cambridge link: https://training.csx.cam.ac.uk/bioinformatics/event/3924477","Availability":"Sporadic","Date_or_Duration":"12-13 July 2021","Language":"English","Cost":"£ 50/day","Costs_Details":"Free for registered University of Cambridge students\n£ 50/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level\nIt remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.\n£ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration\n£ 100/day for all Industry participants. 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