Gene Set Enrichment Analysis (GSEA), which we introduced in 2003, is now standard practice for analyzing genome-wide expression data. GSEA derives its power from identifying the activation/repression of sets of genes that share common biological function, chromosomal location or regulation and differentiate biological phenotypes or cellular states. This knowledge-based approach is effective in elucidating underlying biological mechanisms and generating hypotheses for further study and experimental validation. Since 2005, we have developed, distributed and supported a freely available version of the GSEA software along with a database of annotated gene sets - the Molecular Signatures Database (MSigDB). This popular resource has over 29,300 registered users and 3,100 citations in the literature, and the MSigDB has over 6,700 fully annotated sets. The goal of this renewal is to add significant value to the GSEA/MSigDB resource while maintaining the same level of professional software quality and strong support that investigators have come to expect. We plan to expand the GSEA method embodied in the software, develop additional tools for output interpretation, and refine and enrich the MSigDB database to further accelerate the pace of genomic research.
Our specific aims are:
Aim 1 : Extend the power of the GSEA software from comparing groups of samples to assessing activation of biological processes and pathways in individual samples.
Aim 2 : Provide enhanced visualization and tools for interpretation of GSEA results.
Aim 3 : Extend the scope and specificity of the Molecular Signatures Database (MSigDB).
Aim 3. 1: We will create a new """"""""hallmark"""""""" MSigDB collection, capturing major biological processes and generated from consolidation and refinement of existing gene sets, to reduce redundancy and increase the """"""""specificity"""""""" of enrichment results.
Aim 3. 2: We will add important new collections to MSigDB capturing a wide range of biological conditions and targeted cellular perturbations.
Aim 4 : Support and maintain the Molecular Signatures Database (MSigDB) and Gene Set Enrichment Analysis (GSEA) software. Our progress over the previous funding period;our extensive experience in developing computational methods for genomics research and delivering them as user-friendly, high quality software;our significant user base and many citations;our large repository of gene sets;and our successful delivery of documentation and training for users make us well poised to carry out the aims of this proposal.

Public Health Relevance

Gene set-based enrichment analysis is now standard practice for interpreting genome-wide expression data and elucidating the biological mechanisms associated with disease or other cellular states. The combination of the Gene Set Enrichment Analysis software and Molecular Signatures Database of gene sets representing biological processes, pathways, phenotypes, and cellular perturbations make these sophisticated knowledge- based analyses accessible to any biomedical researcher. The work in this project will significantly increase the power and value of both the analysis software and the gene set collection to better derive hypotheses for further investigation and validation and thereby accelerate and facilitate the study of important questions in biomedical research.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA121941-08
Application #
8658010
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Li, Jerry
Project Start
2006-08-01
Project End
2017-04-30
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
8
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Broad Institute, Inc.
Department
Type
DUNS #
City
Cambridge
State
MA
Country
United States
Zip Code
02142
Lechman, Eric R; Gentner, Bernhard; Ng, Stanley W K et al. (2016) miR-126 Regulates Distinct Self-Renewal Outcomes in Normal and Malignant Hematopoietic Stem Cells. Cancer Cell 29:214-28
Zhu, Xiaodong; Girardo, David; Govek, Eve-Ellen et al. (2016) Role of Tet1/3 Genes and Chromatin Remodeling Genes in Cerebellar Circuit Formation. Neuron 89:100-12
Godec, Jernej; Tan, Yan; Liberzon, Arthur et al. (2016) Compendium of Immune Signatures Identifies Conserved and Species-Specific Biology in Response to Inflammation. Immunity 44:194-206
Kim, Jong Wook; Botvinnik, Olga B; Abudayyeh, Omar et al. (2016) Characterizing genomic alterations in cancer by complementary functional associations. Nat Biotechnol 34:539-46
Tamayo, Pablo; Steinhardt, George; Liberzon, Arthur et al. (2016) The limitations of simple gene set enrichment analysis assuming gene independence. Stat Methods Med Res 25:472-87
Noyan, Hossein; El-Mounayri, Omar; Isserlin, Ruth et al. (2015) Cardioprotective Signature of Short-Term Caloric Restriction. PLoS One 10:e0130658
Liberzon, Arthur; Birger, Chet; Thorvaldsdóttir, Helga et al. (2015) The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1:417-425
Kang, Hee-Bum; Fan, Jun; Lin, Ruiting et al. (2015) Metabolic Rewiring by Oncogenic BRAF V600E Links Ketogenesis Pathway to BRAF-MEK1 Signaling. Mol Cell 59:345-58
Stewart, Michelle L; Tamayo, Pablo; Wilson, Andrew J et al. (2015) KRAS Genomic Status Predicts the Sensitivity of Ovarian Cancer Cells to Decitabine. Cancer Res 75:2897-906
Boudil, Amine; Matei, Irina R; Shih, Han-Yu et al. (2015) IL-7 coordinates proliferation, differentiation and Tcra recombination during thymocyte β-selection. Nat Immunol 16:397-405

Showing the most recent 10 out of 39 publications