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.
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.
|Tan, Yan; Tamayo, Pablo; Nakaya, Helder et al. (2014) Gene signatures related to B-cell proliferation predict influenza vaccine-induced antibody response. Eur J Immunol 44:285-95|
|Dagley, Laura F; Croft, Nathan P; Isserlin, Ruth et al. (2014) Discovery of novel disease-specific and membrane-associated candidate markers in a mouse model of multiple sclerosis. Mol Cell Proteomics 13:679-700|
|Mack, S C; Witt, H; Piro, R M et al. (2014) Epigenomic alterations define lethal CIMP-positive ependymomas of infancy. Nature 506:445-50|
|Zhu, Zehua; Aref, Amir R; Cohoon, Travis J et al. (2014) Inhibition of KRAS-driven tumorigenicity by interruption of an autocrine cytokine circuit. Cancer Discov 4:452-65|
|Liberzon, Arthur; Subramanian, Aravind; Pinchback, Reid et al. (2011) Molecular signatures database (MSigDB) 3.0. Bioinformatics 27:1739-40|
|Villanueva, Augusto; Hoshida, Yujin; Battiston, Carlo et al. (2011) Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. Gastroenterology 140:1501-12.e2|
|Tamayo, Pablo; Cho, Yoon-Jae; Tsherniak, Aviad et al. (2011) Predicting relapse in patients with medulloblastoma by integrating evidence from clinical and genomic features. J Clin Oncol 29:1415-23|
|Cho, Yoon-Jae; Tsherniak, Aviad; Tamayo, Pablo et al. (2011) Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome. J Clin Oncol 29:1424-30|
|De Jager, Philip L; Rossin, Elizabeth; Pyne, Saumyadipta et al. (2008) Cytometric profiling in multiple sclerosis uncovers patient population structure and a reduction of CD8low cells. Brain 131:1701-11|
|Mishra, Pravin J; Mishra, Prasun J; Humeniuk, Rita et al. (2008) Carcinoma-associated fibroblast-like differentiation of human mesenchymal stem cells. Cancer Res 68:4331-9|
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