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MEME-LaB: motif analysis in clusters.

Paul Brown1, Laura Baxter, Richard Hickman

  • 1Warwick Systems Biology Centre, University of Warwick, Coventry CV4 7AL, UK. p.e.brown@warwick.ac.uk

Bioinformatics (Oxford, England)
|May 18, 2013
PubMed
Summary
This summary is machine-generated.

Analyzing co-expressed gene clusters is challenging. MEME-LaB is a new web tool that simplifies the discovery and interpretation of transcription factor-binding sites within large gene sets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genome-wide expression analysis generates numerous co-expressed gene clusters.
  • Existing tools for de novo transcription factor-binding site discovery are not optimized for large-scale cluster analysis.

Purpose of the Study:

  • To introduce MEME-LaB, a web tool designed to streamline the analysis of transcription factor-binding sites in multiple gene clusters.
  • To facilitate the interpretation of results from large-scale gene expression datasets.

Main Methods:

  • MEME-LaB integrates the MEME (Multiple Em for Motif Elicitation) algorithm for ab initio motif discovery.
  • The tool provides an interface for users to input gene clusters, retrieve promoter sequences, and run motif finding analyses.
  • It offers functionalities for browsing and condensing motif-finding results.

Main Results:

  • MEME-LaB enables efficient processing of multiple gene clusters for motif discovery.
  • The tool simplifies the examination and consolidation of motif-finding outcomes.
  • Facilitates enhanced interpretation of large-scale gene expression data.

Conclusions:

  • MEME-LaB addresses the need for a user-friendly tool to analyze transcription factor-binding sites across numerous gene clusters.
  • The web tool enhances the interpretation of large-scale genomic datasets by simplifying motif discovery and result management.