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Related Experiment Videos

eBLOCKs: enumerating conserved protein blocks to achieve maximal sensitivity and specificity.

Qiaojuan Jane Su1, Lin Lu, Serge Saxonov

  • 1Abgenix, Inc., 6701 Kaiser Drive, MS 11, Fremont, CA 94555, USA.

Nucleic Acids Research
|December 21, 2004
PubMed
Summary

The eBLOCKs database classifies protein sequences into families and superfamilies, generating conserved domain signatures. This computational tool enhances high-throughput genome annotation by predicting protein function with high specificity and sensitivity.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein classification into families and superfamilies is crucial for identifying conserved domains.
  • Conserved domains yield motifs and scoring matrices for computational tools that predict protein function in novel sequences.
  • Existing methods require robust tools for high-throughput genome annotation.

Purpose of the Study:

  • To introduce the eBLOCKs database, a novel resource for enumerating protein blocks with varied conservation levels.
  • To provide computational tools for identifying functionally important conserved domains.
  • To enable high-throughput genome annotation with maximal specificity and sensitivity.

Main Methods:

  • Utilized PSI-BLAST and a modified K-means clustering algorithm to group protein sequences by similarity.

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  • Generated multiple sequence alignments, trimmed into ungapped blocks.
  • Derived motifs and position-specific scoring matrices from these blocks for sequence search and annotation.

    • Main Results:

    • The eBLOCKs database successfully enumerates protein blocks across different conservation levels.
    • Generated motifs and scoring matrices are effective for sequence search and annotation.
    • The database facilitates the generation of specific signatures from highly conserved blocks and sensitive signatures from more divergent blocks.

    Conclusions:

    • The eBLOCKs database offers a valuable tool for high-throughput genome annotation.
    • It provides computational resources for predicting protein function based on conserved domain analysis.
    • The database achieves maximal specificity and sensitivity in sequence annotation.