Title |
Inferring regulatory element landscapes and transcription factor networks from cancer methylomes
|
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Published in |
Genome Biology, May 2015
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DOI | 10.1186/s13059-015-0668-3 |
Pubmed ID | |
Authors |
Lijing Yao, Hui Shen, Peter W Laird, Peggy J Farnham, Benjamin P Berman |
Abstract |
Recent studies indicate that DNA methylation can be used to identify transcriptional enhancers, but no systematic approach has been developed for genome-wide identification and analysis of enhancers based on DNA methylation. We describe ELMER (Enhancer Linking by Methylation/Expression Relationships), an R-based tool that uses DNA methylation to identify enhancers and correlates enhancer state with expression of nearby genes to identify transcriptional targets. Transcription factor motif analysis of enhancers is coupled with expression analysis of transcription factors to infer upstream regulators. Using ELMER, we investigated more than 2,000 tumor samples from The Cancer Genome Atlas. We identified networks regulated by known cancer drivers such as GATA3 and FOXA1 (breast cancer), SOX17 and FOXA2 (endometrial cancer), and NFE2L2, SOX2 and TP63 (squamous cell lung cancer). We also identified novel networks with prognostic associations, including RUNX1 in kidney cancer. We propose ELMER as a powerful new paradigm for understanding the cis-regulatory interface between cancer-associated transcription factors and their functional target genes. |
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Saudi Arabia | 1 | 7% |
Israel | 1 | 7% |
United Kingdom | 1 | 7% |
Belgium | 1 | 7% |
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Unknown | 4 | 27% |
Demographic breakdown
Type | Count | As % |
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Scientists | 10 | 67% |
Members of the public | 4 | 27% |
Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 3 | 1% |
China | 2 | <1% |
Norway | 1 | <1% |
Germany | 1 | <1% |
Netherlands | 1 | <1% |
Unknown | 215 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 48 | 21% |
Student > Master | 22 | 10% |
Student > Bachelor | 18 | 8% |
Student > Doctoral Student | 12 | 5% |
Other | 35 | 15% |
Unknown | 28 | 12% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 68 | 30% |
Computer Science | 14 | 6% |
Medicine and Dentistry | 13 | 6% |
Immunology and Microbiology | 5 | 2% |
Other | 19 | 8% |
Unknown | 36 | 16% |