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Evidence for sequence-independent evolutionary traces in genomics data.
1Ceres Inc., 3007 Malibu Canyon Road, Malibu, CA 90265, USA.
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.
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.
- 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.