lntegration and Visualization of Diverse Biological Data

点击翻译按钮获取中文摘要

PROJECT SUMMARY The onset of most human disease involves numerous molecular-level changes to the complex system of interacting genes and pathways that function differently in specific cell-lineage, pathway, and treatment contexts. This system is probed by thousands of functional genomics and quantitative genetic studies, and integrative analysis of these data can generate testable hypotheses identifying causal genetic variants and linking them to network level changes in cells to disease phenotypes. This can enable deeper molecular-level understanding of pathophysiology, paving the way to genome-based precision medicine. The long term goal of this project is to enable such discoveries through integrative analysis of high- throughput biological data in a disease context. In the previous funding periods, we developed accurate data integration methods, created algorithms for the prediction of disease genes through context-specific and mechanistic network models and analysis of quantitative genetics data, and made novel insights into important biological processes and diseases. We further enabled experimental biological discovery by building public interactive systems capable of real-time user-driven integration that are popular among experimental biologists. We now propose to connect these gene-level functional network approaches with the underlying genomic variation by deciphering how genomic variants lead to specific transcriptional and posttranscriptional effects. We propose to develop ab initio sequence-level models capable of predicting biochemical effects of any genomic variant (including rare or never observed) on chromatin state and RNA regulation, then link these effects with gene-level regulatory consequences (including tissue-specific transcription and RNA splicing), and finally put genomic sequence directly into the network context via a statistical approach for detecting genes and network neighborhoods with a significantly elevated mutational burden in disease. Our key deliverable will be a user- friendly, interactive web-based framework enabling systems-level variant impact analysis in a network context and an open source library for computational scientists. In addition to systematic analysis across contexts and diseases, we will collaborate with experimentalists to apply our methods to Alzheimer’s, autism spectrum disorders, chronic kidney disease, immune diseases, and congenital heart defects as case studies for the iterative improvement of our methods and to directly contribute to better understanding of these diseases.