Title |
Motif-based analysis of large nucleotide data sets using MEME-ChIP
|
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Published in |
Nature Protocols, May 2014
|
DOI | 10.1038/nprot.2014.083 |
Pubmed ID | |
Authors |
Wenxiu Ma, William S Noble, Timothy L Bailey |
Abstract |
MEME-ChIP is a web-based tool for analyzing motifs in large DNA or RNA data sets. It can analyze peak regions identified by ChIP-seq, cross-linking sites identified by CLIP-seq and related assays, as well as sets of genomic regions selected using other criteria. MEME-ChIP performs de novo motif discovery, motif enrichment analysis, motif location analysis and motif clustering, providing a comprehensive picture of the DNA or RNA motifs that are enriched in the input sequences. MEME-ChIP performs two complementary types of de novo motif discovery: weight matrix-based discovery for high accuracy; and word-based discovery for high sensitivity. Motif enrichment analysis using DNA or RNA motifs from human, mouse, worm, fly and other model organisms provides even greater sensitivity. MEME-ChIP's interactive HTML output groups and aligns significant motifs to ease interpretation. This protocol takes less than 3 h, and it provides motif discovery approaches that are distinct and complementary to other online methods. |
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Geographical breakdown
Country | Count | As % |
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Canada | 1 | 20% |
France | 1 | 20% |
Israel | 1 | 20% |
China | 1 | 20% |
United States | 1 | 20% |
Demographic breakdown
Type | Count | As % |
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Scientists | 3 | 60% |
Members of the public | 2 | 40% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 2% |
Canada | 2 | <1% |
Spain | 2 | <1% |
Sweden | 1 | <1% |
Brazil | 1 | <1% |
Italy | 1 | <1% |
Mexico | 1 | <1% |
Denmark | 1 | <1% |
Korea, Republic of | 1 | <1% |
Other | 5 | 1% |
Unknown | 361 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 121 | 32% |
Researcher | 70 | 18% |
Student > Master | 48 | 13% |
Student > Bachelor | 31 | 8% |
Student > Doctoral Student | 17 | 4% |
Other | 54 | 14% |
Unknown | 41 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 144 | 38% |
Biochemistry, Genetics and Molecular Biology | 122 | 32% |
Computer Science | 15 | 4% |
Medicine and Dentistry | 10 | 3% |
Immunology and Microbiology | 9 | 2% |
Other | 31 | 8% |
Unknown | 51 | 13% |