Title |
ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data
|
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Published in |
Genome Biology, April 2016
|
DOI | 10.1186/s13059-016-0925-0 |
Pubmed ID | |
Authors |
Scott M. Lundberg, William B. Tu, Brian Raught, Linda Z. Penn, Michael M. Hoffman, Su-In Lee |
Abstract |
A cell's epigenome arises from interactions among regulatory factors-transcription factors and histone modifications-co-localized at particular genomic regions. We developed a novel statistical method, ChromNet, to infer a network of these interactions, the chromatin network, by inferring conditional-dependence relationships among a large number of ChIP-seq data sets. We applied ChromNet to all available 1451 ChIP-seq data sets from the ENCODE Project, and showed that ChromNet revealed previously known physical interactions better than alternative approaches. We experimentally validated one of the previously unreported interactions, MYC-HCFC1. An interactive visualization tool is available at http://chromnet.cs.washington.edu . |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 9 | 23% |
United Kingdom | 5 | 13% |
Canada | 2 | 5% |
France | 2 | 5% |
Netherlands | 1 | 3% |
Nigeria | 1 | 3% |
Spain | 1 | 3% |
Australia | 1 | 3% |
Belgium | 1 | 3% |
Other | 4 | 10% |
Unknown | 12 | 31% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 21 | 54% |
Scientists | 17 | 44% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 2% |
Canada | 2 | 1% |
United States | 2 | 1% |
Italy | 1 | <1% |
Germany | 1 | <1% |
Denmark | 1 | <1% |
Mexico | 1 | <1% |
Spain | 1 | <1% |
Luxembourg | 1 | <1% |
Other | 0 | 0% |
Unknown | 145 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 42 | 27% |
Student > Ph. D. Student | 40 | 25% |
Student > Bachelor | 14 | 9% |
Student > Master | 11 | 7% |
Student > Doctoral Student | 8 | 5% |
Other | 20 | 13% |
Unknown | 23 | 15% |
Readers by discipline | Count | As % |
---|---|---|
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Biochemistry, Genetics and Molecular Biology | 41 | 26% |
Computer Science | 28 | 18% |
Mathematics | 3 | 2% |
Medicine and Dentistry | 3 | 2% |
Other | 4 | 3% |
Unknown | 27 | 17% |