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Related Experiment Videos

Optimal alignment between groups of sequences and its application to multiple sequence alignment

O Gotoh1

  • 1Department of Biochemistry, Saitama Cancer Center, Japan.

Computer Applications in the Biosciences : CABIOS
|June 1, 1993
PubMed
Summary
This summary is machine-generated.

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Four new algorithms (A-D) improve biological sequence alignment, especially for internal gaps. Algorithm D optimizes the sum of pairs score, offering a more accurate method for group-to-group sequence alignment in bioinformatics.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate alignment of biological sequences is crucial for understanding protein function and evolution.
  • Existing methods may struggle with internal gaps in multiple sequence alignments.
  • Developing efficient and accurate algorithms for group-to-group sequence alignment is an ongoing challenge.

Purpose of the Study:

  • To develop and evaluate four novel algorithms (A-D) for aligning two groups of biological sequences.
  • To specifically address the accurate cost evaluation of deletions/insertions in the presence of internal gaps.
  • To optimize the sum of pairs (SP) score for improved alignment accuracy.

Main Methods:

  • Algorithm A: Conventional dynamic programming.

Related Experiment Videos

  • Algorithms B-D: Novel methods for accurate internal gap cost evaluation.
  • Algorithm D: Rigorous optimization of the sum of pairs (SP) score with average performance O(MNL2).
  • Algorithms B and C: Approximations and simpler variants for profile-based operations.

    • Main Results:

    • Algorithms B-D demonstrate improved accuracy in handling internal gaps compared to Algorithm A.
    • Algorithm D achieves rigorous SP score optimization, showing strong performance.
    • Application to multiple sequence alignment using progressive and randomized strategies highlights algorithm performance variations.
    • Comparative analysis reveals the advantages and disadvantages of each algorithm for different protein families.

    Conclusions:

    • The developed algorithms, particularly D, offer enhanced accuracy for group-to-group biological sequence alignment.
    • The choice of algorithm depends on the specific requirements for accuracy and computational efficiency.
    • These algorithms provide valuable tools for multiple sequence alignment tasks in computational biology.