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Thu 12 May 2016
09:30 - 17:30

Venue: Bioinformatics Training Room, Craik-Marshall Building

Provided by: Bioinformatics


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BIIT web-tools for high-throughput data analysis from ELIXIR-Estonia

Thu 12 May 2016


In this course we will introduce web-based, open source tools to analyse and interpret high-throughput biological data.

The main focus will be g:Profiler - a toolset for finding most significant functional groups for a given gene or protein list; MEM - a query engine allowing to mine hundreds of public gene expression datasets to find most co-expressed genes based on a query gene; and ClustVis - a web tool for visualizing clustering of multivariate data using Principal Component Analysis (PCA) plot and heatmap.

MEM and g:Profiler are ELIXIR-Estonia node services.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book by linking here.

Target audience
  • Biologists and bioinformaticians who are dealing with high-throughput gene expression data or other high-throughput data and would like to learn state-of-the-art methods for mining and analysing such data.
  • Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
  • Further details regarding eligibility criteria are available here
  • Further details regarding the charging policy are available here

Common understanding of high-throughput technologies does help to follow the lectures.


Number of sessions: 1

# Date Time Venue Trainer
1 Thu 12 May 2016   09:30 - 17:30 09:30 - 17:30 Bioinformatics Training Room, Craik-Marshall Building map Priit Adler

After this course you should be able to:

  • Characterise a list of genes by their common biological functions.
  • Convert gene names and IDs from and to any of the most common namespaces.
  • Given a gene of interest find other genes that behave in similar manner across large number of gene expression datasets.
  • Create PCA plot and heatmap using exploratory data analysis tool ClustVis, know when to use these types of visualizations and how to interpret them.

During this course you will learn about:

  • g:Profiler - learn how to perform gene set enrichment analysis and find what are the most significant functional groups in your gene or protein list (for example interesting genes/proteins from Q-RT-PCR or RNA-seq experiment results). To learn how to convert gene and protein IDs from one namespace into another or find corresponding gene/protein IDs from another organism.
  • MEM - learn to perform and interpret MEM co-expression queries. Given a query gene, MEM performs co-expression analysis across hundreds of public datasets and returns ordered list of globally similar genes. We’ll learn how MEM can be used to infer potential function for a gene based on other genes that are globally similar. For a gene pair we’ll learn how to identify the datasets and conditions where they behave similarly and where they do not.
  • ClustVis - learn how to make exploratory data analysis plots using ClustVis web tool. How to prepare a dataset for uploading the data or search among publicly available datasets. We learn how to filter a chosen dataset using ClustVis and how to choose pre-processing options. We will learn how PCA plot and heatmap can be modified and how to interpret and export the results.

Presentations, demonstrations and practicals




A number of times per year


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