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

A hierarchical framework for state space matrix inference and clustering. Zuo C, Chen K, Hewitt K, Bresnick EH, Keles S. Annals of Applied Statistics, 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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A MAD-Bayes algorithm for state-space inference and clustering with application to querying large collections of ChIP-seq data sets. Zuo C, Chen K, Keles S. Proceedings of the 20th Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2016

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A predictive modeling approach for cell line-specific long-range regulatory interactions. Roy S, Siahpirani AF, Chasman D, Knaack S, Ay F, Stewart R, Wilson M, Sridharan R. Nucleic Acids Research 43(18):8694-8712, 2015

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atSNP: transcription factor binding affinity testing for regulatory SNP detection. Zuo C, Shin S, Keles S. Bioinformatics 31(20):3353-5, 2015

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Lead

Sunduz Keles

Investigators

Sushmita Roy

Deborah Chasman

Kailei Chen

Chang Wang

Resources

MBASIC software

atSNP software