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GeneSigDB--a curated database of gene expression signatures.

Nucleic acids research | Jan 22, 2010

The primary objective of most gene expression studies is the identification of one or more gene signatures; lists of genes whose transcriptional levels are uniquely associated with a specific biological phenotype. Whilst thousands of experimentally derived gene signatures are published, their potential value to the community is limited by their computational inaccessibility. Gene signatures are embedded in published article figures, tables or in supplementary materials, and are frequently presented using non-standard gene or probeset nomenclature. We present GeneSigDB (http://compbio.dfci.harvard.edu/genesigdb) a manually curated database of gene expression signatures. GeneSigDB release 1.0 focuses on cancer and stem cells gene signatures and was constructed from more than 850 publications from which we manually transcribed 575 gene signatures. Most gene signatures (n = 560) were successfully mapped to the genome to extract standardized lists of EnsEMBL gene identifiers. GeneSigDB provides the original gene signature, the standardized gene list and a fully traceable gene mapping history for each gene from the original transcribed data table through to the standardized list of genes. The GeneSigDB web portal is easy to search, allows users to compare their own gene list to those in the database, and download gene signatures in most common gene identifier formats.

Pubmed ID: 19934259 RIS Download

Mesh terms: Algorithms | Computational Biology | Databases, Genetic | Databases, Nucleic Acid | Databases, Protein | Gene Expression Profiling | Gene Expression Regulation, Neoplastic | Humans | Information Storage and Retrieval | Internet | Neoplasms | Oligonucleotide Array Sequence Analysis | Software | Stem Cells

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This is a list of tools and resources that we have found mentioned in this publication.

List Of Lists Annotated

LOLA is a web driven database of published and manually curated (public), and user-specific (private) gene lists derived from genome-wide approaches such as expression profiling and proteomics. LOLA allows researchers to measure the similarity of gene lists in order to identify genes that are robustly changed across laboratories, studies, and platforms, and within and between species. Furthermore, LOLA compares lists at a gene-level so that probes mapping to the same gene are not considered different.


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