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  • Applicants are expected to be at an early stage of using bioinformatics in their research with the need to develop their knowledge and skills further. No previous knowledge of programming is required for this course; group projects may give you the opportunity to learn basic programming, but participants will be supported in this by their mentors. Depending on your chosen project, an introductory programming tutorial may be given as homework prior to attending the course. Though programming skills are not a prerequisite for attending the course, we will ask participants to specify their current level of programming skills in the applications. This will allow the mentors to target the group projects better to the skills and needs of the final course participants.1
  • Applicants should be researchers who are using large multi-omics datasets to infer systems biology models. This is an advanced-level course, and so we will select applicants who already have some experience (ideally 1-2 years) of working with systems biology modelling or related large-scale multi-omics data analysis. Additionally, applicants will be expected to have a working knowledge of using Linux commands, and experience of using a programming language (e.g. Python or Perl).1
  • Bioinformaticians and wet-lab biologists who can program in Perl, Python or R. The Ensembl browser workshop is a pre-requisite for this course.1
  • Experience from previous years has led to preference being given to candidates who: are doctoral candidates in the early to middle stages of their thesis research already have some familiarity with phylogenetic methods (i.e. have already used some of the relevant tools) have already collected/assembled a molecular sequence dataset to analyze in their work have experience of working in a Unix/Linux command-line environment We will also select a small number of participants that already work in bioinformatics labs, to intensify collaboration between early career stage biologists and bioinformaticians. Applicants from labs with a strong focus on computational molecular evolution methodology need to carefully outline their motivation for attending the course in this context, since they have ready access to expert supervision and are likely to be very skilled already in the topics we teach, or are in the course of becoming very skilled therein. The course is also suitable for established researchers who would like to refresh their memory of modern statistical methods for phylogenetic analysis of genomic sequence data and to interact with developers of such methods.1
  • No prior experience of bioinformatics is required, but an interest in finding out more about genetic variation resources and an undergraduate level understanding of biology would be of benefit. Experience with command line usage would be very beneficial. This workshop will focus specifically on human genetic variation. Prerequisite We encourage the audience to go through our online course on human genetic variation prior to attending the workshop - www.ebi.ac.uk/training/online/course/human-genetic-variation-i-introduction-2019 .1
  • No prior experience of bioinformatics is required, but an interest in finding out more about variation resources and an undergraduate level understanding of biology would be of benefit. This workshop will focus specifically on human variation.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 and quantifying peptides, and finally to obtaining 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.1
  • 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 course is aimed at wet-lab scientists working in the field of immunology who wish to learn more about available data and tools that can help them in their research. 15 places are reserved for early-stage researchers from the ENLIGHT-TEN+ project.  1
  • The workshop is aimed at a general audience of biologists and bioinformaticians working with non-coding RNA who are interested in using RNAcentral and Rfam, or for users who would like to bring their projects and discuss them with the RNAcentral and Rfam teams. A basic familiarity with web browsing and an undergraduate level knowledge of molecular biology is recommended for this course. Familiarity with Unix/Linux command line is desirable for part of the course. 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/resources/free-courses/introduction...1
  • This course is aimed at 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. Familiarity with the technology and biological use cases of high throughput sequencing (HTS) 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.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 PhD students and postdoctoral researchers needing to learn about methods and approaches for manipulating and analysing livestock genomic data. It will help those wanting to start basic identification of genetic variation, annotating function to genomic data, and using public data to interpret new findings. 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 advanced PhD students and post-doctoral researchers who are applying or planning to apply high throughput sequencing technologies and bioinformatics methods in their research. Familiarity with the technology and biological use cases of high throughput sequencing is required, as is some experience with R/Bioconductor.1
  • This 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. Familiarity 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.1
  • This course is aimed at advanced PhD students, post-doctoral researchers, and non-academic scientists 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. Applicants 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. 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 Python tutorial: https://www.w3schools.com/python/ R tutorial: https://www.datacamp.com/courses/free-introduction-to-r Regardless of your current knowledge we encourage successful participants to use these to prepare for attending the course and future work in this area. Selected participants will also be sent materials prior to the course. These might include pre-recorded talks and required reading that will be essential to fully understand the course.1
  • This course is aimed at anyone interested in finding out more about protein biology. No prior experience of bioinformatics is required, but participants should have an undergraduate level understanding of biology. For those who wish to attend the session on programmatic access a prior knowledge of coding/programming would be of benefit. For an introduction to the concept of web services and how you can use them to access the tools and data available at EMBL-EBI please watch our webinar.1
  • This course is aimed at bench biologists working in the area of discovery science who want to learn more about bioinformatics tools and resources. No prior knowledge of bioinformatics is required and no experience of programming or the use of Unix / Linux is necessary.1
  • This 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.1
  • This 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.1
  • This course is aimed at experimental biologists, bioinformaticians and mathematicians who have just started in systems biology, are familiar with the basic terminology in this field and who are now keen on gaining a better knowledge of systems biology modelling approaches to understand biological and biomedical problems. An experience of using a programming language (e.g Python, R, Matlab) would be a benefit but is not mandatory. An undergraduate knowledge of molecular and cellular biology or some background in mathematics is highly beneficial.1
  • This course is aimed at individuals working across biological sciences who have little or no 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. No previous knowledge of programming / coding is required for this course.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 scientists who are working in the field of metagenomics, in the early stages of their data analysis, and who may already have some prior experience in using bioinformatics in their research. Prerequisites Some 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: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Basic R concept tutorials: www.r-tutor.com/r-introduction1
  • This course is aimed at researchers currently working with zebrafish and generating genomic and functional data. Graduate students, postdoctoral fellows, research scientists and faculty are encouraged to apply.  Little to no experience with RNA-seq analysis is required, however, applicants who have already generated an RNA-seq dataset from zebrafish samples relevant to their project will gain the most benefit from this course.  Some experience with R is beneficial.  1
  • This course is aimed at researchers who are generating, planning on generating, or working with single cell RNA sequencing data. Prerequisites Participants will be using a Galaxy resource in-depth. Participants may also be asked to do brief coding in R. Please ensure that you complete the free tutorials before you attend the course: Introduction to Galaxy: https://galaxyproject.org/tutorials/g101/ Basic R concept tutorials: www.r-tutor.com/r-introduction There are other tutorials here, although they are not required: https://galaxyproject.org/learn/1
  • This course is aimed at researchers who are generating, planning on generating, or working with single cell RNA sequencing or image-based transcriptomics data. This course will not cover any aspects of data analysis, therefore no prior computational knowledge is required.1
  • This course is aimed at researchers who are new to the field of metabolomics and wish to learn about the process of conducting a metabolomics study. Attendees will gain an understanding of a standardised workflow, from experimental design to data acquisition and analysis, and will benefit those who are planning to integrate metabolomics into their work, either moving into the field or as an investigator from other omics. We will primarily focus on a basic introduction to metabolomics with worked examples using a predesigned LC-MS analysis workflow. The course assumes little prior knowledge of using bioinformatics tools.1
  • This course is aimed at researchers who are new to the field of metabolomics and wish to learn about the process of developing a metabolomics study. Attendees will gain an understanding of the workflow process and some of the analysis techniques and so should benefit those who are planning to include metabolite profiling in their work, either moving into the field or as an addition to other omics. We will primarily focus on a basic introduction to metabolomics with a worked example using a predesigned LC-MS analysis workflow. The course assumes little prior knowledge of using bioinformatics tools.1
  • This course is for biological researchers who want to learn more about the application of structural information in their work and how to use some of the key bioinformatics resources that are available. No previous experience in the field of structural bioinformatics is required, however a basic knowledge of protein structure would be of benefit. Participants should be familiar with basic Linux operations - http://www.ee.surrey.ac.uk/Teaching/Unix/ - and have some experience of bioinformatics tools and databases.1
  • This course is targeted at biologists who want to explore, and gain further insight, into their own data through the use of visualisation and design approaches. Some prior experience in programming would be beneficial, but pre-reading and exercises will be sent out to all successful applicants prior to the start of the course. Example datasets will be provided which include: gene-gene interaction data gene-disorder links phylogeny of transcription factors ChIP-Seq and RNA-Seq1
  • This introductory course is aimed at bench-based biologists, who are involved in, or embarking on projects that will use network and pathway analysis or protein interaction data. For example, you may be using these tools in the interpretation of biological datasets or as part of a systems biology approach. The course requires no prior knowledge of pathway analysis or computer programming skills. Preference will be given to those actively involved in or commencing interaction/pathway-based projects.1
  • This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists / bioinformaticians who wish to gain a better understanding of the biological challenges when working with integrated datasets. No programming or command line experience is required to attend this course. Please note this course does not cover statistical approaches for data integration. For advanced-level training in using large-scale multiomics data and machine learning to infer biological models you may wish to consider our course on Systems Biology: From large datasets to biological insight.1
  • This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists / bioinformaticians who wish to gain knowledge of the biological challenges when working with integrated datasets. Some 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: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Basic R concept tutorials: www.r-tutor.com/r-introduction For advanced-level training in using large-scale multiomics data and machine learning to infer biological models you may wish to consider our course on Systems Biology: From large datasets to biological insight.1
  • This workshop is aimed at anyone interested in finding out more about protein biology. No prior experience of bioinformatics is required, but an undergraduate level understanding of biology would be of benefit.1
  • 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 members and collaborators of the 3D-BioInfo Community.1
  • This workshop is aimed at new and experienced managers of bioinformatics core facilities, or other facilities that support their users to analyse and interpret large biomolecular data sets. This course will not provide a platform for teaching hands-on bioinformatics analysis.1
  • This workshop is aimed at new and experienced managers of bioinformatics core facilities, or other facilities that support their users to analyse and interpret large biomolecular data sets.  This course will not provide a platform for teaching hands-on bioinformatics analysis.  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 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
  • Wet-lab researchers and bioinformaticians1
  • wet-lab researchers and bioinformaticians1
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