Epigenome-based Phenotyping Project

This project is developing methods that integrate multiple regulatory and epigenomic data sources in order to identify phenotypes that characterize sequence variants, and to predict the extent to which the variants modulate target genes identified in diverse disease and developmental states of cells.

Related CPCP Publications

Chromatin module inference on cellular trajectories identifies key transition points and poised epigenetic states in diverse developmental processes. Roy S, Sridharan R. Genome Research, 2017

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Annotation regression for genome-wide association studies with an application to psychiatric genomic consortium data. Shin S, Keles S. Statistics in Biosciences, 2016

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Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq. Liu P, Sanalkumar R, Bresnick E, Keles S, Dewey C. Genome Research 26:1124–1133, 2016

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A hierarchical framework for state space matrix inference and clustering. Zuo C, Chen K, Hewitt K, Bresnick EH, Keles S. Annals of Applied Statistics 10(3):1348-1372, 2016

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A multi-task graph-clustering approach for chromosome conformation capture data sets identifies conserved modules of chromosomal interactions. Siahpirani A, Ay F, Roy S. Genome Biology 17:114, 2016

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Sunduz Keles


Sushmita Roy

Deborah Chasman

Kailei Chen

Chang Wang


atSNP Search software

CPCP 2017 Retreat: Chromatin State and Long-Range Interaction Dynamics in Development and Disease Symposium Video

RIPPLE software

CMINT software

MBASIC software

atSNP software