Identifying Coevolving Partners from Paralogous Gene Families
Author Information
Author(s): Chen-Hsiang Yeang
Primary Institution: Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ, U.S.A
Hypothesis
Can an algorithm be developed to identify coevolving partners from two families of sequences with distinct phylogenetic trees?
Conclusion
The proposed algorithm successfully identifies coevolving partners with an accuracy of 60% to 88% on both simulated data and real protein domain sequences.
Supporting Evidence
- The algorithm identified coevolving partners with 73%–88% accuracy on simulated data.
- On aligned protein domain sequences, the algorithm identified domain pairs belonging to the same proteins with similar accuracy.
- The algorithm significantly outperforms random assignments on both simulated and real data.
Takeaway
The study created a new method to find pairs of genes that evolve together, which can help us understand how different genes interact.
Methodology
The algorithm uses dynamic programming to map gene trees to a reference species tree and identifies coevolving partners based on sequence composition.
Limitations
The algorithm's time and space complexities are high for large trees, and it relies on strong assumptions about sequence evolution.
Statistical Information
Statistical Significance
p<0.05
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