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SSMAL: similarity searching with alignment graphs

P Nicodème1

  • 1INRIA-Rocquencourt and LIX Ecole Polytechnique, France; Present address: DKFZ, Abt. Theoretische Bioinformatik, INF 280, D-69120 Heidelberg, Germany. p.nicodeme@DKFZ-Heidelberg.de

Bioinformatics (Oxford, England)
|August 8, 1998
PubMed
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Biologists can now use SSMAL (Shuffling Similarities with Multiple Alignments) for faster protein sequence and alignment searching. This tool significantly speeds up local alignment searches against protein databases and multiple alignments.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Developing efficient tools for protein sequence analysis is crucial for biological research.
  • Current methods for searching protein databases can be time-consuming.
  • There is a need for tools that can handle both sequence-to-alignment and alignment-to-sequence searches.

Purpose of the Study:

  • To develop a fast and sensitive scanning tool for biologists.
  • To enable searching local alignments of protein sequences against multiple alignment databases (e.g., ProDom).
  • To provide a tool for locally aligning protein multiple alignment queries against protein databases (e.g., SWISSPROT).

Main Methods:

  • Developed the SSMAL (Shuffling Similarities with Multiple Alignments) program.

Related Experiment Videos

  • Utilized features and code from the Blast algorithm.
  • Implemented handling for deletions specific to multiple alignments.

    • Main Results:

    • SSMAL allows both scanning of multiple alignments and searching with a multiple alignment.
    • SSMAL searches can be 20-30 times faster than profile scans against databases like ProDom.
    • In worst-case scenarios, SSMAL searches are approximately 9 times faster than profile searches.

    Conclusions:

    • SSMAL offers a significant speed improvement for protein sequence and alignment searching.
    • The tool provides flexibility by supporting both query types.
    • SSMAL is a valuable addition to the bioinformatics toolkit for protein analysis.