Motif-based analysis of large nucleotide data sets using MEME-ChIP

Ma, Wenxiu, Noble, William S. and Bailey, Timothy L. (2014) Motif-based analysis of large nucleotide data sets using MEME-ChIP. Nature Protocols, 9 6: 1428-1450. doi:10.1038/nprot.2014.083

Author Ma, Wenxiu
Noble, William S.
Bailey, Timothy L.
Title Motif-based analysis of large nucleotide data sets using MEME-ChIP
Journal name Nature Protocols   Check publisher's open access policy
ISSN 1754-2189
Publication date 2014-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.1038/nprot.2014.083
Open Access Status Not Open Access
Volume 9
Issue 6
Start page 1428
End page 1450
Total pages 23
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 38 times in Thomson Reuters Web of Science Article | Citations
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