Jill Mesirov Laboratory at UC San Diego
Research in the Mesirov lab focuses on applying machine-learning and statistical methods to functional genomics data with the goal of better understanding the underlying biological mechanisms of disease, improving stratification of patients with respect to different treatment outcomes, and identifying novel drug targets. Our lab is committed to the development of practical, accessible, open source software tools to bring these methods to the general biomedical research community. These tools support the research of hundreds of thousands of investigators worldwide. Thus, to achieve its goals, the lab includes both computational biologists and software engineers working together to further our understanding and treatment of disease.
The work of the lab includes:
New statistical and machine learning techniques for cancer genomics: We develop and apply new machine learning and statistical techniques to find molecular markers to aid in the diagnosis, prognosis, and treatment of cancer as well as to understand the underlying biological processes. While we apply these methods to a wide range of tumor types, much of our work has focused on medulloblastoma, a pediatric brain tumor.
New statistical and mathematical techniques for interpreting genomic data: From a biological perspective, we know it is not individual genes but rather sets of genes, representing pathways or states of the cell, that are the real features, by which we might predict the character of a particular patient sample. We made a major contribution in this area by the introduction of a statistical methodology called
Gene Set Enrichment Analysis (GSEA), which is now standard practice for interpreting global transcription profiling data and has thousands of citations. This was the first of a number of signature-based approaches we developed and applied to study genomic data. More recently, we have introduced information theoretic approaches to help elucidate the genomic alterations associated with biological phenotypes and identify novel therapeutics to treat disease.
Identifying states related to infectious disease: We studied the in vivo transcriptional profiles of the malaria parasite
Plasmodium falciparum, i.e., from samples taken from the human hosts. Our computational methods enabled us to use signatures of known states in yeast (Saccharomyces cerevisiae) to identify previously unknown physiological diversity in
P. falciparum. We used our signature-based approaches on pre-vaccination molecular profiles to identify vaccinees who will have a strong vs. weak immune response.
Software and reproducible research: Our lab is dedicated to disseminating the mathematical and computational methods we develop as accessible software tools. These tools serve in aggregate over 300,000 investigators world-wide and include the GSEA software package and our comprehensive repository of gene sets, the Molecular Signatures Database (MSigDB) used with GSEA to interpret genomic data; an integrative computational genomics software environment,
Integrative Genomics Viewer (IGV), a high-performance visualization tool for interactive exploration of large, integrated genomic datasets; and the
GenomeSpace platform which supports transparent interoperability between popular bioinformatics tools and data resources.
- Kim JW, Botvinnik OB, Abudayyeh O, Birger C, Rosenbluh J, Shrestha Y, Abazeed ME, Hammerman PS, DiCara D, Konieczkowski DJ, Johannessen CM, Liberzon A, Alizad-Rahvar AR, Alexe G, Aguirre A, Ghandi M, Greulich H, Vazquez F, Weir BA, Van Allen EM, Tsherniak A, Shao DD, Zack TI, Noble M, Getz G, Beroukhim R, Garraway LA, Ardakani M, Romualdi C, Sales G, Barbie DA, Boehm JS, Hahn WC,
Mesirov JP, Tamayo P. Characterizing genomic alterations in cancer by complementary functional associations. Nat Biotechnol. 2016 May;34(5):539-46. doi: 10.1038/nbt.3527.
- Hanaford AR, Archer TC, Price A, Kahlert UD, Maciaczyk J, Nikkhah G, Kim JW, Ehrenberger T, Clemons PA, Dančík V, Seashore-Ludlow B, Viswanathan V, Stewart ML, Rees M, Shamji AF, Schreiber SL, Fraenkel E, Pomeroy SL,
Mesirov JP, Tamayo P, Eberhart CG, Raabe EH. DiSCoVERing innovative therapies for rare tumors: combining genetically accurate disease models with in silico analysis to identify novel therapeutic targets. Clin Cancer Res. 2016 Mar 24. pii: clincanres.3011.2015.
- Tamayo PT, Cho YJ, Tsherniak A, Greulich H, Ambrogio L, Schouten-van Meeteren N, Zhou T, Buxton A, Kool M, Meyerson M, Pomeroy SL*,
Mesirov JP, Predicting relapse in medulloblastoma patients by integrating evidence from clinical and genomic features. J Clin Oncol. 2011 Apr 10;29(11):1415-23.
- Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M,
Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999 Oct 15;286(5439):531-7.
- Subramanian A, Tamayo P, Mootha V, Mukherjee S, Ebert BL, Gillette MA, Pomeroy S, Golub TR, Lander ES,
Mesirov JP. Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-wide Expression Profiles. Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50.
- Reich M, Liefeld T, Gould J, Lerner J, Tamayo P,
Mesirov JP. GenePattern 2.0. Nat Genet. 2006 May;38(5):500-1.
- Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G,
Mesirov JP, Integrative Genomics Viewer, Nat Biotechnol. 2011 Jan;29(1):24-6. PMCID: PMC3346182
- Qu K*, Garamszegi S*, Wu F*, Thorvaldsdottir H, Liefeld T, Borges-Rivera D, Pochet N, Demchak B, Hull T, Ben-Artzi G, Blankenberg D, Barber GP, Lee BT, Kuhn RM, Nekrutenko A, Segal E, Ideker T, Reich M, Regev A, Chang HY,
Mesirov JP. Integrative genomic analysis interoperation of bioinformatics tools in GenomeSpace. Nat Methods. 2016 Mar;13(3):245-7. doi: 10.1038/nmeth.3732.
- Daily JP, Scanfeld D, Pochet N, Le Roch4 K, Plouffe D, Kamal M, Sarr O, Mboup S, Ndir O, Wypij D, Levasseur K, Thomas E, Tamayo P, Dong C, Zhou Y, Lander ES, Ndiaye D, Wirth D, Winzeler E,
Mesirov JP, Regev A. Distinct physiological states of the parasite Plasmodium falciparum in malaria infected patients. Proc Natl Acad Sci U S A. 2009 Jul 7;106(27).
- Batzoglou S, Jaffe DB, Stanley K, Butler J, Gnerre S, Mauceli E, Berger B,
Mesirov JP, Lander ES. ARACHNE: a whole-genome shotgun assembler. Genome Res. 2002 Jan;12(1):177-89. PMCID: PMC155255
- International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001 Feb 15;409(6822):860-921. Erratum in: Nature 2001 Aug 2;412(6846):565. Nature 2001 Jun 7;411(6838):720. PMID:11237011.
Full List of Publications from PubMed