Molecular signatures database (MSigDB) 3.0
ABSTRACT Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets.
We report the availability of a new version of the database, MSigDB 3.0, with over 6700 gene sets, a complete revision of the collection of canonical pathways and experimental signatures from publications, enhanced annotations and upgrades to the web site.
MSigDB is freely available for non-commercial use at http://www.broadinstitute.org/msigdb.
Full-textDOI: · Available from: Pablo Tamayo, Jul 26, 2015
- SourceAvailable from: Sudharsana Rajan
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- "Molecular signature database (MSigDB) (Liberzon et al., 2011) was used to obtain the biological pathways for the initially screened dataset (Ponzoni et al., 2014). We chose canonical pathways which include pathways from BioCarta, KEGG and Reactome databases. "
ABSTRACT: Psoriasis is a chronic disease of the skin characterized by hyper proliferation and inflammation of the epidermis and dermal components of the skin. T-cell-dependent inflammatory process in skin governs the pathogenesis of psoriasis. An insilico search strategy was utilized to identify psoriatic therapeutic drug targets. The gene expression profiling of psoriatic skin identified a total of 427 differentially expressed genes (DEGs). Gene ontology investigation of DEGs identified genes involved in calcium binding, apoptosis, keratinisation, lipid transportation and homeostasis apart from immune mediated processes. The protein interaction networks identified proteins involved in various signaling mechanisms with high degree of interconnections. The gene modules derived from the main network were enriched with rich kinome. These sub-networks were dominated by the presence of non-receptor kinase family members which are major signal transmitters in immune response. The computational approach has aided in the identification of non-receptor kinases as potential targets for psoriasis drug development. Copyright © 2015. Published by Elsevier B.V.Gene 04/2015; 566(2). DOI:10.1016/j.gene.2015.04.030 · 2.08 Impact Factor
AMIA summit 2015; 03/2015
- "COMMON GENES SHARED BY OBESITY, CRC AND OSTEOPOROSIS, AND PLAUSIBLE EVIDENCE SUPPORTING THEIR RELATIONSHIPS WITH THE THREE DISEASES. GENES OBESITY CRC OSTEOPOROSIS PPP1R15A* In the bone morphogenetic protein (BMP) signaling pathway, which regulates appetite  Mutations in the BMP pathway are related with colorectal carcinogenesis  In the bone morphogenetic protein signaling pathway, which are associated with bone-related diseases, such as osteoporosis  FOS diet-induced obesity is accompanied by alteration of FOS expression  Proto-oncogene, in the KEGG pathway of colorectal cancer  Mice lacking c-fos develop severe osteopetrosis  FOSB positive association between maternal obesity  Oncogene, regulators of cell proliferation, has a debatable impact on CRC patient survival  Overexpression of FosB increases bone formation  HADHA* Associated with multiple fatty acid metabolism pathways  Unknown. Associated with breast cancer  Unknown. "
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- "GSEA not only can detect group-wise statistically-significant genes and proteins, but also enriched pathway gene sets against a large database of gene sets previously characterized in functional genomic studies. To support GSEA, databases such as the Molecular Signature Database (MSigDB) (Liberzon et al., 2011), GeneSigDB (Culhane et al., 2010), and PAGED (Huang et al., 2012) have been developed to integrate GO categories, pathways from KEGG (Kanehisa and Goto, 2000), gene regulatory targets from TRANSFAC (Wingender et al., 2000), micro-RNA targets, and curated gene sets that are co-expression signatures from literature. GSEA and comprehensive databases populated pathway modules can help streamline statistical and Fig. 2. Conceptual plot of different pathway analysis tools according to the utilization of functional information and/or topological information (positions are NOT absolute). "
ABSTRACT: Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results being sensitive to data preparation methods, sample condition, instrument types, and analytical method. To address this challenge in Proteomics data analysis, we review common approaches developed to incorporate biological function and network topological information. We categorize existing tools into four categories: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We review the general application potential of these tools to Proteomics. In addition, we also review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.Journal of Theoretical Biology 06/2014; 362. DOI:10.1016/j.jtbi.2014.05.031 · 2.30 Impact Factor