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• • Content provider
  • European Bioinformatics Institute (EBI)14
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• • Keyword
  • DNA & RNA (dna-rna)3
  • ArrayExpress Archive of Functional Genomics Data1
  • Ensembl1
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• • Scientific topic
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• • Event type
  • Workshops and courses14
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• • Venue
  • European Bioinformatics Institute, Hinxton9
  • EMBL-EBI, Wellcome Genome Campus1
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• • Country
  • United Kingdom10
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• • Target audience
  • The course is aimed at PhD students and post-doctoral researchers who are starting to use high-throughput sequencing technologies and bioinformatics methods in their research. The content is most applicable for those working with eukaryotic genomes, especially in the area of human genetics and rare-disease research. Participants will require a basic knowledge of the Unix command line and the Ubuntu 18 operating system. We recommend these free tutorials: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Please note: participants without basic knowledge of these resources will have difficulty in completing the practical sessions.1
  • The course is aimed at individuals working in immunology research who have minimal experience in bioinformatics. Applicants are expected to be at an early stage of using bioinformatics in their research with the need to develop their skills and knowledge further. Participants will require a basic knowledge of the Unix command line, and the R statistical package. We recommend these free tutorials: Introduction to the Unix environment – http://www.ee.surrey.ac.uk/Teaching/Unix/ Basic R concepts – http://www.r-tutor.com/r-introduction1
  • The 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. The 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 peptides and finally to 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 once it has been generated.1
  • The course is aimed at researchers interested in core bioinformatics techniques, sequence searching and alignment. No prior experience of bioinformatics is required, but familiarity with biological databases and web tools would help. An undergraduate level understanding of molecular biology would be of benefit to those attending the workshop.1
  • The workshop is aimed at PhD students and researchers at The Open University. Participants would benefit from an undergraduate knowledge of the biological or chemical life sciences, or a background in human health and disease.1
  • This course is aimed at PhD students and post-doctoral researchers who are using high-throughput sequencing technologies and bioinformatics methods in their research. The content is most applicable for those working with eukaryotic genomes, human genetics and in rare disease research. Participants will require a basic knowledge of the Unix command line, the Ubuntu 16 operating system and the R statistical packages. We recommend these free tutorials: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Basic R concept tutorials: www.r-tutor.com/r-introduction Please note: participants without basic knowledge of these resources will have difficulty in completing the practical sessions.1
  • This course is aimed at life science researchers needing to learn more about the basic processing of raw RNA-Seq data and downstream analysis. It will help those wanting to learn how to interpret gene expression data and explore results of biological significance from processed data. Participants will require a basic knowledge of the Unix command line, the Ubuntu 18 operating system and the R statistical packages. We recommend these free tutorials: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Basic R concept tutorials: www.r-tutor.com/r-introduction Regardless of your current knowledge we encourage successful participants to use these, and other materials, to prepare for attending the course and future work in this area.1
  • This course is aimed at life science researchers wanting to learn more about processing RNA-Seq data and later downstream analysis. It will help those wanting a basic introduction to handling RNA-Seq data, guiding them through several common approaches that can be applied to their own datasets. It features taught and practical sessions that cover how to interpret gene expression data and learn more about the biological significance of certain results. Participants will require a basic knowledge of the Unix command line, the Ubuntu 18 operating system and the R statistical packages. We recommend these free tutorials:

 Basic introduction to the Unix environment:
 www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux:
 https://training.linuxfoundation.org/free-linux-training
 Basic R concept tutorials: 
www.r-tutor.com/r-introduction Regardless of your current knowledge we encourage successful participants to use these, and other materials, to prepare for attending the course and future work in this area.1
  • This course is aimed at life science researchers wanting to learn more about processing RNA-seq data and later downstream analysis. It will help those wanting a basic introduction to handling RNA-seq data, guiding them through several common approaches that can be applied to their own datasets. It features taught and practical sessions that cover how to interpret gene expression data and learn more about the biological significance of certain results. Participants will require a basic knowledge of the Unix command line, the Ubuntu 18 operating system, and the R statistical packages. We recommend these free tutorials: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Basic R concept tutorials: www.r-tutor.com/r-introduction Regardless of your current knowledge, we encourage successful participants to use these, and other materials, to prepare for attending the course and future work in this area.1
  • This 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. The practical sessions in the course require a basic understanding of the Unix command line and the R statistics package. Participants should ensure that they complete these free tutorials before attending the course: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Basic R concept tutorials: www.r-tutor.com/r-introduction1
  • This workshop is aimed at bioinformaticians with experience of analysing data from the PDB, either by processing archive files or via API access. We encourage applications from individuals with specific questions relating to PDB data that are difficult to solve using existing data queries. Programming experience is required, with a preference for those familiar with Python, although  this is not an absolute requirement. An example use case might involve research into a specific drug molecule, where protein structure is relevant to drug specificity. The graph database would allow the analysis of all common interaction sites in PDB at the residue level, with the potential to expand this search across ligands containing similar fragments. Additional searches could analyse the protein-protein interaction sites between different isoforms of the same protein, and cross-reference them to sequence conservation data and predicted functional annotations. Researchers should submit a 200-word abstract when they apply that describes their work and potential queries related to PDB data. This should include details on how PDB data has been accessed previously and the types of questions trying to be answered.1
  • This workshop is aimed at researchers and bioinformaticians from across industry and academia who are looking to leverage machine learning approaches in protein function prediction. It will guide participants through the use of big data to build analytical workflows on publically-available biological data. Participants will require prior experience in the use of the command line interface and confidence in a programming language to fully benefit from the workshop. Please contact us if you have any questions about the course's suitability before you apply.1
  • This workshop is for a cohort of early-stage researchers working on the EU-funded NEUcrest Innovative Training Network.1
  • This workshop is aimed at life science researchers, who are interested in extracting data and evidence from research literature. It will help those who want to identify cited datasets for reuse, further analyses, background research, or as supporting data for own hypotheses. The workshop would also be of interest to those who are applying or planning to apply literature analysis/text-mining in their own research projects. Participants will benefit from an undergraduate level knowledge of biology. Participants should ideally have some bioinformatics experience and/or basic understanding of programmatic access. Please note that this workshop requires no prior knowledge of text analytics or computer programming skills. Regardless of your current knowledge, we encourage participants to explore this short series of recorded webinars on an introduction to programmatic access.1
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• • Node
  • EMBL-EBI14
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• • Instructor
  • Tom Hancocks
  • Janick Mathys143
  • Bruna Piereck72
  • Alexander Botzki67
  • Wandrille Duchemin57
  • True Colours48
  • Christof De Bo45
  • Matheus Lourenço42
  • Benjamin Pavie38
  • Elien Vandermarliere34
  • Lieve Ongena34
  • Geert van Geest33
  • Aleena Mushtaq29
  • Robin Engler29
  • René Custers23
  • Amanda Gonçalves19
  • Benjamin Moore18
  • Gert Van Isterdael18
  • Joris De Wolf18
  • Niels Vandamme18
  • Agnes Uhereczky17
  • David Armstrong17
  • Liesbeth De Milde17
  • Michal Szpak17
  • Nancy George17
  • Robin Lefebvre17
  • Deepak Tanwar16
  • Lucia Smit16
  • Birgit Meldal15
  • James Collier14
  • Kobe Lavaerts14
  • Sarah Morgan14
  • Wendi Bacon14
  • Alexandra Holinski13
  • Baron Koylass13
  • Emily Perry13
  • Sebastian Munck13
  • Anna Swan12
  • Dayane Rodrigues Araujo12
  • Sam Holt12
  • Tania Wyss Lozano12
  • Tatiana Woller12
  • David Wishart11
  • Kalpana Panneerselvam11
  • Lee Larcombe11
  • Nicolas Vannieuwkerke11
  • Ricard Argelaguet11
  • Alex Bateman10
  • Alexandre Bonvin10
  • Barbara Baert10
  • Gustavo Ruiz Buendia10
  • Krishna Kumar Tiwari10
  • Lorna Richardson10
  • Malachi Griffith10
  • Niels Geudens10
  • Rossana Zaru10
  • Veronique Voisin10
  • An Staes9
  • Bob Asselbergh9
  • Frédéric Burdet9
  • Gary Bader9
  • Jolan Heyse9
  • Lennart Martens9
  • Lieven Clement9
  • Louisse Paola Mirabueno9
  • Mathieu Bourgey9
  • Obi Griffith9
  • Osman Salih9
  • Rahuman Sheriff9
  • Silvie Fexova9
  • Tallulah Andrews9
  • Thawfeek Mohamed Varusai9
  • Thuong Van Du Tran9
  • Typhaine Paysan-Lafosse9
  • Ajay Mishra8
  • Alexey Larionov8
  • Andrew Hercules8
  • Bart Mesuere8
  • Ben Verhoeven8
  • Cath Brooksbank8
  • Eliot Ragueneau8
  • Fabio Madeira8
  • Frank Vernaillen8
  • Geert Vermaerke8
  • Hema Bye-A-Jee8
  • Jared Simpson8
  • Jean-luc Doumont8
  • Koen Van den Eeckhout8
  • Nicolas Peredo8
  • Pavankumar Videm8
  • Rachel Marcone8
  • Tania Wyss8
  • Trevor Pugh8
  • Alyona Ivanova7
  • Arne Defauw7
  • Bart Ghesquière7
  • Emily Bowler-Barnett7
  • Engy Nasr7
  • Evelien Van Hamme7
  • Gerard Kleywegt7
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