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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-06

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. More... »

PAGES

1428-1450

References to SciGraph publications

  • 2007-02. Quantifying similarity between motifs in GENOME BIOLOGY
  • 2011-07. Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data in NATURE BIOTECHNOLOGY
  • 2008-11. Model-based Analysis of ChIP-Seq (MACS) in GENOME BIOLOGY
  • 2013-11. A high-resolution map of the three-dimensional chromatin interactome in human cells in NATURE
  • 2013-07. A compendium of RNA-binding motifs for decoding gene regulation in NATURE
  • 2000-04. Identification of in vivo DNA targets of chromatin proteins using tethered Dam methyltransferase in NATURE BIOTECHNOLOGY
  • 2010-07. iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution in NATURE STRUCTURAL & MOLECULAR BIOLOGY
  • 2008-11. HITS-CLIP yields genome-wide insights into brain alternative RNA processing in NATURE
  • 2009-07. Argonaute HITS-CLIP decodes microRNA–mRNA interaction maps in NATURE
  • 2008-07. Mapping and quantifying mammalian transcriptomes by RNA-Seq in NATURE METHODS
  • 2011-08. PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data in GENOME BIOLOGY
  • 2013-02. Pervasive and dynamic protein binding sites of the mRNA transcriptome in Saccharomyces cerevisiae in GENOME BIOLOGY
  • 2011-05-15. A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins in NATURE METHODS
  • 2012-08. A complete workflow for the analysis of full-size ChIP-seq (and similar) data sets using peak-motifs in NATURE PROTOCOLS
  • 2012-09. An integrated encyclopedia of DNA elements in the human genome in NATURE
  • 2009-10. ChIP–seq: advantages and challenges of a maturing technology in NATURE REVIEWS GENETICS
  • 2010-08. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences in GENOME BIOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nprot.2014.083

    DOI

    http://dx.doi.org/10.1038/nprot.2014.083

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1051570591

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24853928


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