Detecting coevolution without phylogenetic trees? Tree-ignorant metrics of coevolution perform as well as tree-aware metrics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-12-03

AUTHORS

J Gregory Caporaso, Sandra Smit, Brett C Easton, Lawrence Hunter, Gavin A Huttley, Rob Knight

ABSTRACT

BACKGROUND: Identifying coevolving positions in protein sequences has myriad applications, ranging from understanding and predicting the structure of single molecules to generating proteome-wide predictions of interactions. Algorithms for detecting coevolving positions can be classified into two categories: tree-aware, which incorporate knowledge of phylogeny, and tree-ignorant, which do not. Tree-ignorant methods are frequently orders of magnitude faster, but are widely held to be insufficiently accurate because of a confounding of shared ancestry with coevolution. We conjectured that by using a null distribution that appropriately controls for the shared-ancestry signal, tree-ignorant methods would exhibit equivalent statistical power to tree-aware methods. Using a novel t-test transformation of coevolution metrics, we systematically compared four tree-aware and five tree-ignorant coevolution algorithms, applying them to myoglobin and myosin. We further considered the influence of sequence recoding using reduced-state amino acid alphabets, a common tactic employed in coevolutionary analyses to improve both statistical and computational performance. RESULTS: Consistent with our conjecture, the transformed tree-ignorant metrics (particularly Mutual Information) often outperformed the tree-aware metrics. Our examination of the effect of recoding suggested that charge-based alphabets were generally superior for identifying the stabilizing interactions in alpha helices. Performance was not always improved by recoding however, indicating that the choice of alphabet is critical. CONCLUSION: The results suggest that t-test transformation of tree-ignorant metrics can be sufficient to control for patterns arising from shared ancestry. More... »

PAGES

327-327

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2148-8-327

DOI

http://dx.doi.org/10.1186/1471-2148-8-327

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049674901

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19055758


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