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Related Experiment Videos

Quantifying similarity between motifs.

Shobhit Gupta1, John A Stamatoyannopoulos, Timothy L Bailey

  • 1Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Box 355065, Seattle, WA 98195, USA. shobhitg@u.washington.edu

Genome Biology
|February 28, 2007
PubMed
Summary
This summary is machine-generated.

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Comparing newly discovered motifs to existing databases is crucial. Tomtom is a new algorithm that accurately measures motif similarity, helping researchers find related motifs in databases.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • De novo motif discovery aims to identify novel DNA or protein sequence patterns.
  • Comparing newly found motifs to existing databases is essential for biological interpretation.
  • Existing methods may lack robust statistical measures for motif comparison.

Purpose of the Study:

  • To develop a statistical measure for quantifying motif-motif similarity.
  • To introduce Tomtom, an algorithm for searching motif databases with a query motif.
  • To evaluate the accuracy and effectiveness of Tomtom in identifying similar motifs.

Main Methods:

  • Definition of a novel statistical measure for motif-motif similarity.
  • Development of the Tomtom algorithm for database searching.

Related Experiment Videos

  • Experimental simulations to assess algorithm performance and E-value accuracy.

    • Main Results:

    • Tomtom provides accurate E-values for motif similarity searches.
    • The algorithm effectively identifies previously discovered motifs that are similar to a query motif.
    • Simulations confirm the reliability of the statistical measure.

    Conclusions:

    • Tomtom offers a statistically sound and effective method for comparing de novo motifs against databases.
    • This tool aids in the biological interpretation of newly discovered sequence motifs.
    • The accuracy of Tomtom's E-values supports its utility in motif discovery workflows.