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Published December 2013 | Supplemental Material + Published
Journal Article Open

Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps


We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq data sets from six biologically diverse cell lines studied by The ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional data sets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell-type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type-specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.

Additional Information

© 2013 Mortazavi et al. This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported), as described at http://creativecommons.org/licenses/by-nc/3.0/. Published by Cold Spring Harbor Press. Received March 29, 2013; accepted in revised form October 7, 2013. Published in Advance October 29, 2013. We gratefully acknowledge Ewan Birney, Ian Dunham, Eric Mjolsness, and Paul Sternberg for general discussion of SOMs and their applications to functional genomics; and Diane Trout, Henry Amrhein, and Anna Abelin for computational assistance. At Caltech this work was supported by grants to B.J.W. from the Beckman Foundation, the Donald Bren Endowment, the Gordon Moore Cell Center at Caltech, NIH U54HG004576, NIH U54HG006998, and RC2HG005573; at Hudson Alpha Institute by grant NIH U54HG004576 and NIH U54HG006998 to R.M.M.; and at Penn State University by NIH R01DK065806, U54HG006998, and RC2HG005573 to R.C.H. A.M. was partly supported as a Caltech Beckman Fellow and a Moore Cell Center Fellow while at Caltech. The Mortazavi laboratory at UC Irvine was supported by grant NIH U54HG006998 as well as EU-FP7 project STATegra (306000) to A.M.

Attached Files

Published - Genome_Res.-2013-Mortazavi-2136-48.pdf

Supplemental Material - Supplemental_FigureS14.pdf

Supplemental Material - Supplemental_FiguresS1_S13.pdf

Supplemental Material - Supplemental_Legends.docx

Supplemental Material - Supplemental_Tables.tgz


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