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Related Experiment Videos

Evidence for sequence-independent evolutionary traces in genomics data.

W Volkmuth1, N Alexandrov

  • 1Ceres Inc., 3007 Malibu Canyon Road, Malibu, CA 90265, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|April 4, 2002
PubMed
Summary

Discovering new evolutionary traces, like genomic neighborhood and gene expression correlation, helps predict protein biochemical function. These sequence-independent methods improve upon traditional homology detection, even in the twilight zone of sequence similarity.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Protein sequence conservation is key for functional classification in genome sequencing.
  • Traditional homology detection methods struggle with low sequence similarity (twilight zone).
  • Existing methods like sequence profiles, multi-linked alignment, and threading have limitations.

Purpose of the Study:

  • To identify novel, sequence-independent evolutionary traces for inferring protein fold.
  • To enhance the prediction of protein biochemical function.
  • To explore the utility of genomic neighborhood and gene expression data in protein fold prediction.

Main Methods:

  • Investigated conservation of protein fold in genomic neighborhood.
  • Analyzed gene expression data for correlations with shared protein folds.

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  • Evaluated these traces for effectiveness independent of sequence homology.

    • Main Results:

    • Found that genes in close genomic proximity exhibit a higher tendency to share protein folds, even without significant sequence homology.
    • Observed that genes with correlated expression patterns are more likely to share protein folds than randomly selected gene pairs.
    • Demonstrated that these evolutionary traces are detectable even when sequence similarity is low.

    Conclusions:

    • Genomic neighborhood and gene expression correlation represent novel, sequence-independent evolutionary traces.
    • These findings offer new avenues for predicting protein fold and biochemical function.
    • The study expands the application of gene expression data beyond pathway analysis to inferring protein structure and function.