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Prioritizing Gene Lists: Utilizing GEO and BioMart for Effective Gene Candidate Identification

Analyzing extensive gene lists can be overwhelming, especially with limited resources. This guide focuses on prioritizing candidate genes that are promising based on related processes, known protein features, and high-throughput experiment overlaps. We delve into leveraging GEO datasets for expression queries and obtaining information through BioMart for homologs and protein domains. By utilizing GEO profiles and filtering options within BioMart, researchers can effectively identify and retrieve vital data about their genes of interest, facilitating informed follow-up studies.

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Prioritizing Gene Lists: Utilizing GEO and BioMart for Effective Gene Candidate Identification

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  1. Working with gene lists:Finding data using GEO& BioMart June 5, 2014

  2. Analyzing a gene list • With hundreds of genes but a limited budget and lab personnel, you need to prioritize the gene list to candidate genes for follow-up • Pick ones that are “interesting” • Known to be involved in other related processes but not (yet) in your process of interest • Has protein features which suggest a function in your process, but it has not been characterized • No known function or domain, but it shows up in other, related high-throughput experiments suggesting a key role in your process of interest

  3. Our approach Analyzing gene lists by: • Finding overlap with other high-throughput experiments • Finding additional information using BioMart • Mouse/human homologs • Protein domain content • GO classification

  4. GEO (gene expression omnibus) • GEO Datasets • Curated gene expression datasets • i.e. there is backlog of experiments that haven’t made it into the database • Can search for experiments and conduct differential gene expression queries on some datasets • Can download datasets & do offline analyses • GEO Profiles • Profiles of expression data for genes

  5. Why search GEO? • What other experiments have been done that are similar to yours? • GEO datasets • How do my genes of interest behave in other large scale experiments • GEO profiles

  6. GEO Profile search Search on a gene name (C04F5.7):

  7. GEO Dataset search “C. elegans”: 4434

  8. GEO Dataset searches

  9. Once dataset identified • Download data • SOFT format: tab-delimited data • Issues: • Not necessarily processed such that they have the ratios of experiment/control • If starting with raw data, may not be able to replicate exactly what authors did or lack expertise/software to generate a list of DE genes • Look for supplementary data from publication • Usually they provide a list of all DE genes

  10. Choice of dataset for comparison In class demo

  11. Biomart – EBI Ensembl • Use series of menus • Data source – organism (genes, variation, ect) • Filters -- reduce the number of results • Attributes – what data to return • Can set up very precise and multilayered queries • Can query across multiple organisms • Simple query: • Given a list of gene IDs, you can obtain attributes or sequences for the entire list • Tools • ID converter – very useful, easy to use

  12. Two sites for BioMart access www.biomart.org

  13. Database journal issue on BioMart

  14. Filtering in BioMart

  15. Attributes in BioMart

  16. Biomart • Filters • C. elegans genes with a human homolog • Specify only genes with >= # isoforms • protein coding genes with a transmembrane domain • Attributes • Entrez Gene IDs, WormBase IDs, Affy IDs • Sequence data • transcript, protein, UTRs, flanking regions, ect.

  17. BioMart • In class demo

  18. Today’s exercise • Compare current dataset from PLoS Pathogens paper to data from a different dataset • Identify & retrieve additional information about C. elegans genes using BioMart

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