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

Integrative genomic analysis predicts causative cis-regulatory mechanisms of the breast cancer-associated genetic variant rs4415084. Zhang Y, Manjunath M, Zhang S, Chasman D, Roy S, Song JS. Cancer Research , 2018

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An empirical Bayes test for allelic imbalance detection in ChIP-seq. Zhang Q, Keles S. Biostatistics, 2017

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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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Lead

Sunduz Keles

Investigators

Sushmita Roy

Deborah Chasman

Kailei Chen

Chang Wang

Resources

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