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Related Experiment Video

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Discovering sequence motifs with arbitrary insertions and deletions.

Martin C Frith1, Neil F W Saunders, Bostjan Kobe

  • 1Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. martin@cbrc.jp

Plos Computational Biology
|April 26, 2008
PubMed
Summary

A new computational method, GLAM2 (Gapped Local Alignment of Motifs), accurately discovers biological sequence motifs, including those with insertions or deletions. This advancement aids in understanding DNA, RNA, and protein functions by identifying critical sequence patterns.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biological functions are encoded in molecular sequences (DNA, RNA, proteins).
  • Discovering sequence motifs is crucial for understanding biological mechanisms.
  • Existing algorithms often fail to account for insertions and deletions (indels) within motifs.

Purpose of the Study:

  • To present GLAM2 (Gapped Local Alignment of Motifs), a novel method for discovering sequence motifs that allows for indels.
  • To introduce GLAM2SCAN for searching sequence databases using these indel-aware motifs.
  • To demonstrate the superior performance of GLAM2 compared to existing motif discovery tools.

Main Methods:

  • GLAM2 is a generalization of the gapless Gibbs sampling algorithm.
  • It identifies motifs with insertions and deletions in a general manner.
  • GLAM2SCAN is a companion tool for database searching with discovered motifs.

Main Results:

  • GLAM2 accurately re-discovers variable-width protein motifs from PROSITE, outperforming PRATT and SAM-T2K.
  • It refines ELM database motifs, significantly reducing overpredictions.
  • GLAM2 shows promise on the BAliBASE benchmark and for identifying protein kinase substrate motifs and gapped DNA motifs.

Conclusions:

  • GLAM2 offers a robust method for discovering sequence motifs with indels.
  • It improves the accuracy of motif identification in DNA, RNA, and protein sequences.
  • The software is publicly available and expected to advance the study of biological sequence patterns and functions.