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Related Experiment Videos

COFFEE: an objective function for multiple sequence alignments

C Notredame1, L Holm, D G Higgins

  • 1EMBL Outstation-The European Bioinformatics Institute, Hinxton Hall, Hinxton, Cambridge CB10 1SD, UK.

Bioinformatics (Oxford, England)
|July 31, 1998
PubMed
Summary
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We developed COFFEE (Consistency based Objective Function For alignmEnt Evaluation), a new strategy using genetic algorithms to optimize multiple sequence alignments. COFFEE improves accuracy, especially for low-identity sequences, and acts as a reliability index.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) accuracy is crucial for biological sequence analysis.
  • Existing methods may struggle with low sequence identity.
  • A novel optimization strategy is needed to enhance MSA quality.

Purpose of the Study:

  • To introduce COFFEE (Consistency based Objective Function For alignmEnt Evaluation), a new method for optimizing MSAs.
  • To evaluate the performance of COFFEE against existing alignment methods.
  • To establish COFFEE as a reliability index for MSAs.

Main Methods:

  • Designed a genetic algorithm-based strategy named COFFEE.
  • Utilized the SAGA genetic algorithm package for optimization.

Related Experiment Videos

  • Tested COFFEE on 11 structural alignment test cases from 3D_ali.
  • Compared COFFEE performance against five alternative alignment methods.

    • Main Results:

    • COFFEE significantly improves MSA accuracy, particularly for sequences with low identity.
    • The COFFEE score effectively serves as a reliability index for MSAs.
    • COFFEE, when combined with SAGA and FSSP pairwise alignments, generates high-quality MSAs.
    • COFFEE demonstrates flexibility, enabling extension of pairwise alignment methods to multiple alignments.

    Conclusions:

    • COFFEE offers a flexible and effective approach to enhance MSA accuracy.
    • The COFFEE score provides valuable insights into alignment reliability.
    • This strategy advances the field of computational sequence analysis.