Abstract

The Data Organization Core (DOC) of the Mount Sinai's KMC-IDG will collect, process, and maintain attributes about the druggable targets for all proposed families: protein kinases, G-protein coupled receptors, nuclear receptors and ion channels. The emphasis will be to focus on those genes/proteins that are understudied and collect unbiased genome-wide profiling datasets. In addition, the DOC will collect, process and maintain data tables and attributes for all other genes/proteins, drugs/small-molecules and other perturbagens, pheontypes/diseases/side-effects, and clinical as well as genomics datasets from cohorts of patients. This will enable us to identify links between and across genes/proteins networks, drugs/small-molecules and other perturbagens networks, pheontypes/diseases/side-effects networks, and clusters of individual patients with similar profiles. For this, the Core will develop and apply clustering and classification algorithms as well as workflows to make predictions about the potential applicability of targeting the understudied proteins for various translational applications in personalized medicine.

Public Health Relevance

The large amount of data that is accumulating from genome-wide emerging biotechnologies is illuminating new biology about many genes that until recently not much data was available. This new knowledge, integrated with existing databases, can be used to prioritize potential genes/proteins as novel drug targets.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA189201-02
Application #
8934413
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Zenklusen, Jean C
Project Start
Project End
2017-07-31
Budget Start
2015-08-01
Budget End
2016-07-31
Support Year
2
Fiscal Year
2015
Total Cost
Indirect Cost

  Institution

Name
Icahn School of Medicine at Mount Sinai
Department
Type
DUNS #
078861598
City
New York
State
NY
Country
United States
Zip Code
10029

  Publications

Lachmann, Alexander; Torre, Denis; Keenan, Alexandra B et al. (2018) Massive mining of publicly available RNA-seq data from human and mouse. Nat Commun 9:1366
Smith, Milo R; Yevoo, Priscilla; Sadahiro, Masato et al. (2018) Integrative bioinformatics identifies postnatal lead (Pb) exposure disrupts developmental cortical plasticity. Sci Rep 8:16388
Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih et al. (2018) Cell-specific prediction and application of drug-induced gene expression profiles. Pac Symp Biocomput 23:32-43
Shameer, Khader; Glicksberg, Benjamin S; Hodos, Rachel et al. (2018) Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. Brief Bioinform 19:656-678
Torre, Denis; Lachmann, Alexander; Ma'ayan, Avi (2018) BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud. Cell Syst 7:556-561.e3
Torre, Denis; Krawczuk, Patrycja; Jagodnik, Kathleen M et al. (2018) Datasets2Tools, repository and search engine for bioinformatics datasets, tools and canned analyses. Sci Data 5:180023
Oprea, Tudor I; Bologa, Cristian G; Brunak, Søren et al. (2018) Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov 17:317-332
Guo, Yiqing; Pace, Jesse; Li, Zhengzhe et al. (2018) Podocyte-Specific Induction of Krüppel-Like Factor 15 Restores Differentiation Markers and Attenuates Kidney Injury in Proteinuric Kidney Disease. J Am Soc Nephrol 29:2529-2545
Wang, Zichen; Lachmann, Alexander; Keenan, Alexandra B et al. (2018) L1000FWD: fireworks visualization of drug-induced transcriptomic signatures. Bioinformatics 34:2150-2152
Koplev, Simon; Lin, Katie; Dohlman, Anders B et al. (2018) Integration of pan-cancer transcriptomics with RPPA proteomics reveals mechanisms of epithelial-mesenchymal transition. PLoS Comput Biol 14:e1005911

Showing the most recent 10 out of 66 publications