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Assessing sequence comparison methods with the average precision criterion.

Zhuoran Chen1

  • 1Department of Computer Science and Technology, Beijing University, Beijing 100871, People's Republic of China. cray@pku.edu.cn

Bioinformatics (Oxford, England)
|December 12, 2003
PubMed
Summary

This study introduces the average precision (AP) measure for evaluating sequence database search methods. Results show that BLAST

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Assessing sequence database search methods is crucial for advancing bioinformatics.
  • Sensitivity, selectivity, and speed are key performance criteria, often in conflict.
  • The average precision (AP) metric integrates sensitivity and selectivity for comprehensive evaluation.

Purpose of the Study:

  • To evaluate and compare the performance of popular sequence comparison algorithms.
  • To introduce and demonstrate the utility of the average precision (AP) measure.
  • To provide insights for developing improved search algorithms and guiding method selection.

Main Methods:

  • Utilized the SCOP database for experimental evaluation.
  • Assessed Smith-Waterman (SSEARCH), FASTA, BLAST, and PSI-BLAST algorithms.

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  • Employed the average precision (AP) metric and time-AP plots for analysis.

    • Main Results:

    • BLAST's low-complexity segment filtration negatively impacts search quality.
    • Average precision (AP) scores correlate logarithmically with search time.
    • Homologs in large protein families are generally more difficult to detect than those in smaller families.

    Conclusions:

    • The average precision (AP) measure offers a valuable tool for assessing sequence search performance.
    • Findings can guide the development of new search algorithms.
    • Researchers can use this evaluation to select optimal search methods for their specific needs.