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Reconciliation-based detection of co-evolving gene families.

Chan YB, Ranwez V, Scornavacca C - BMC Bioinformatics (2013)

Bottom Line: Genes located in the same chromosome region share common evolutionary events more often than other genes (e.g. a segmental duplication of this region).Their evolution may also be related if they are involved in the same protein complex or biological process.Identifying co-evolving genes can thus shed light on ancestral genome structures and functional gene interactions.

View Article: PubMed Central - HTML - PubMed

Affiliation: ISEM, Université Montpellier 2, Montpellier, 34095, France. celine.scornavacca@univ-montp2.fr.

ABSTRACT

Background: Genes located in the same chromosome region share common evolutionary events more often than other genes (e.g. a segmental duplication of this region). Their evolution may also be related if they are involved in the same protein complex or biological process. Identifying co-evolving genes can thus shed light on ancestral genome structures and functional gene interactions.

Results: We devise a simple, fast and accurate probability method based on species tree-gene tree reconciliations to detect when two gene families have co-evolved. Our method observes the number and location of predicted macro-evolutionary events, and estimates the probability of having the observed number of common events by chance.

Conclusions: Simulation studies confirm that our method effectively identifies co-evolving families. This opens numerous perspectives on genome-scale analysis where this method could be used to pinpoint co-evolving gene families and thus help to unravel ancestral genome arrangements or undocumented gene interactions.

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Test power. Power of the test for various values of c.
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Figure 3: Test power. Power of the test for various values of c.

Mentions: In Figure 3, we plot the power of the test (the true positive rate) for various values of the co-evolution parameter. As expected the power rises with c; it is greater than 0.8 (a standard threshold value for power measurement) for approximately c>0.52.


Reconciliation-based detection of co-evolving gene families.

Chan YB, Ranwez V, Scornavacca C - BMC Bioinformatics (2013)

Test power. Power of the test for various values of c.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4225522&req=5

Figure 3: Test power. Power of the test for various values of c.
Mentions: In Figure 3, we plot the power of the test (the true positive rate) for various values of the co-evolution parameter. As expected the power rises with c; it is greater than 0.8 (a standard threshold value for power measurement) for approximately c>0.52.

Bottom Line: Genes located in the same chromosome region share common evolutionary events more often than other genes (e.g. a segmental duplication of this region).Their evolution may also be related if they are involved in the same protein complex or biological process.Identifying co-evolving genes can thus shed light on ancestral genome structures and functional gene interactions.

View Article: PubMed Central - HTML - PubMed

Affiliation: ISEM, Université Montpellier 2, Montpellier, 34095, France. celine.scornavacca@univ-montp2.fr.

ABSTRACT

Background: Genes located in the same chromosome region share common evolutionary events more often than other genes (e.g. a segmental duplication of this region). Their evolution may also be related if they are involved in the same protein complex or biological process. Identifying co-evolving genes can thus shed light on ancestral genome structures and functional gene interactions.

Results: We devise a simple, fast and accurate probability method based on species tree-gene tree reconciliations to detect when two gene families have co-evolved. Our method observes the number and location of predicted macro-evolutionary events, and estimates the probability of having the observed number of common events by chance.

Conclusions: Simulation studies confirm that our method effectively identifies co-evolving families. This opens numerous perspectives on genome-scale analysis where this method could be used to pinpoint co-evolving gene families and thus help to unravel ancestral genome arrangements or undocumented gene interactions.

Show MeSH