A critical challenge in Big Data science is the overall lack of data ahalysis platforms available for transforming Big Data into biological knowledge. To address this challenge, we propose a set of interconnected computational tools capable of organizing and analyzing heterogeneous data to support combined inquiries and to de-convolute complex relationships embedded within large-scale data. We demonstrate its utility with a cardiovascular-centric platform that is easily generalizable to similar efforts in other disciplines. Our Center has designed a federated data architecture of existing resources substantiated by a solid and growing user base, and innovations to elevate functionality. Novel crowdsourcing and text-mining methods will extract the wealth of untapped knowledge embedded in biomedical literature, and novel in-depth proteomics analytical tools will unprecedentedly elucidate dynamic protein features. A key strength of our platform will be the rigorous validation using clinical data from Jackson Heart Study and the Healthy Elderly Active Longevity (HEAL;Wellderly) cohorts. Our proposal includes nine scientific aims that address three main focus areas: (i) we will build a new model platform that amalgamates community-supported Big Data resources, enabling data annotations and collaborative analyses;(ii) we will integrate molecular data with drug and disease information, both structured and unstructured, for knowledge aggregation, and (iii) we will create on-the-cloud analytical and modeling tools to power in-depth protein discoveries. Specifically, we will create a novel distributed query system and cloud-based infrastructure that is capable of providing unified access to multi-omics datasets;we will develop computational and crowdsourcing methods to systematically define relationships between genes, proteins, diseases, and drugs from the literature, emphasizing cardiovascular medicine;we will rally community participation and promote awareness of collaborative research through outreach and educational games;we will create a platform to analyze and visualize multi-scale pathway models of genes, proteins, and metabolites;we will develop tools and algorithms to mechanistically model spatiotemporal protein networks in organelles and to. predict higher physiological phenotypes;and we will correlate individual phenotypes, health histories, and multi-scale molecular profiles to examine cardiovascular disease mechanisms. These tools will be implemented, delivered, and executed on the cloud infrastructure to minimize the computational power required of users.

Public Health Relevance

The challenge of biomedical Big Data are multifaceted. Everyday, biomedical researchers face the daunting task of storing, analyzing, and distributing large-scale genomics and proteomics data, and aggregating all information to discern deeper meanings. Only through a coherent effort can we harness copious amounts of unruly genomics and proteomics data for transformation into testable hypotheses that can dovetail with all of scientific research. This Data Science Research Component is designed to address these challenges.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BST-R (52))
Program Officer
Lyster, Peter
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California Los Angeles
Los Angeles
United States
Zip Code
Buffon, Giseli; Blasi, Édina A R; Adamski, Janete M et al. (2016) Physiological and Molecular Alterations Promoted by Schizotetranychus oryzae Mite Infestation in Rice Leaves. J Proteome Res 15:431-46
Lavallée-Adam, Mathieu; Yates 3rd, John R (2016) Using PSEA-Quant for Protein Set Enrichment Analysis of Quantitative Mass Spectrometry-Based Proteomics. Curr Protoc Bioinformatics 53:13.28.1-16
Wei, Bin; Jin, J-P (2016) TNNT1, TNNT2, and TNNT3: Isoform genes, regulation, and structure-function relationships. Gene 582:1-13
Liu, Rong; Jin, J-P (2016) Calponin isoforms CNN1, CNN2 and CNN3: Regulators for actin cytoskeleton functions in smooth muscle and non-muscle cells. Gene 585:143-53
Scruggs, Sarah B; Wang, Ding; Ping, Peipei (2016) PRKCE gene encoding protein kinase C-epsilon-Dual roles at sarcomeres and mitochondria in cardiomyocytes. Gene 590:90-6
Lindsey, Merry L; Hall, Michael E; Harmancey, Romain et al. (2016) Adapting extracellular matrix proteomics for clinical studies on cardiac remodeling post-myocardial infarction. Clin Proteomics 13:19
Ma, Yonggang (2016) LRP5: A novel anti-inflammatory macrophage marker that positively regulates migration and phagocytosis. J Mol Cell Cardiol 91:61-2
Francis Stuart, Samantha D; De Jesus, Nicole M; Lindsey, Merry L et al. (2016) The crossroads of inflammation, fibrosis, and arrhythmia following myocardial infarction. J Mol Cell Cardiol 91:114-22
Turnham, Rigney E; Scott, John D (2016) Protein kinase A catalytic subunit isoform PRKACA; History, function and physiology. Gene 577:101-8
Lau, Edward; Cao, Quan; Ng, Dominic C M et al. (2016) A large dataset of protein dynamics in the mammalian heart proteome. Sci Data 3:160015

Showing the most recent 10 out of 55 publications