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Related Experiment Videos

SPLASH: structural pattern localization analysis by sequential histograms.

A Califano1

  • 1IBM TJ Watson Research Center, PO Box 704, Yorktown Heights, NY 10598, USA. acal@us.ibm.com

Bioinformatics (Oxford, England)
|June 27, 2000
PubMed
Summary
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Splash is a new algorithm for discovering sparse patterns in DNA and protein sequences. It is efficient, parallelizable, and can identify conserved regions for building accurate biological models.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Discovering sparse amino acid patterns in protein sequences is crucial for identifying evolutionarily conserved functional or structural regions.
  • Existing algorithms for sparse pattern discovery are often inefficient or limited in the types of patterns they can detect.
  • Conserved DNA patterns also hold significant biological information, necessitating effective discovery methods.

Purpose of the Study:

  • Introduce Splash, a novel deterministic algorithm for discovering sparse patterns in biological sequences.
  • Enable the identification of both identical and similar sparse patterns in protein and DNA sequences.
  • Overcome the limitations of existing algorithms in terms of efficiency and pattern type.

Main Methods:

Related Experiment Videos

  • Developed a deterministic pattern discovery algorithm named Splash.
  • Designed Splash to be highly efficient and inherently parallelizable.
  • Ensured Splash can discover sparse patterns of any length without performance degradation.

    • Main Results:

    • Splash successfully identifies sparse amino acid and nucleic acid patterns in protein and DNA sequences.
    • The algorithm demonstrates high efficiency, processing large databases like SWISS-PROT within hours on a standard workstation.
    • Identified biologically relevant motifs in histone I and G-Protein Coupled Receptor families, showcasing its utility.

    Conclusions:

    • Splash provides an efficient and versatile tool for discovering conserved sequence patterns.
    • Its speed and parallelizability allow for exhaustive analysis of protein families and large databases.
    • The discovered patterns can be leveraged to construct accurate Position-Specific Scoring Matrix (PSSM) or Hidden Markov Model (HMM) profiles for enhanced sequence analysis.