Genome sequencing has revealed thousands of mutations associated with various types of cancer. The vast majority of the identified variants are noncoding. A number of seminal studies have revealed that noncoding mutations can disrupt 3D genome architecture and cause cancer. To accelerate discovery in this emerging area of investigation, bioinformatics tools for integrative analysis of mutation and 3D genome organization data is critically needed. In this project, we will develop a suite of bioinformatics tools to predict the hierarchy of 3D genome organization and use such information to interpret and identify causal noncoding mutations.
In aim 1, we will develop a method for identifying mutations that disrupt chromatin domain and subdomain boundaries in cancers.
In aim 2, we will use disease-relevant enhancer- promoter network for prioritizing mutations that disrupt enhancer function.
In aim 3, we will develop a 3D cancer genome database for curating, querying and visualizing chromatin interaction data together with transcriptomic, epigenomic, and mutation data.
Disruption of the three-dimensional genome is an emerging hallmark of many tumor types. Computational methods are critical for discovering mutations that affect three- dimensional genome and cause cancer. ! !
