Title |
Detecting coevolution without phylogenetic trees? Tree-ignorant metrics of coevolution perform as well as tree-aware metrics
|
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Published in |
BMC Ecology and Evolution, December 2008
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DOI | 10.1186/1471-2148-8-327 |
Pubmed ID | |
Authors |
J Gregory Caporaso, Sandra Smit, Brett C Easton, Lawrence Hunter, Gavin A Huttley, Rob Knight |
Abstract |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 12 | 9% |
United Kingdom | 3 | 2% |
Netherlands | 2 | 1% |
Chile | 2 | 1% |
Germany | 2 | 1% |
Canada | 2 | 1% |
Australia | 1 | <1% |
Brazil | 1 | <1% |
Switzerland | 1 | <1% |
Other | 6 | 4% |
Unknown | 106 | 77% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 39 | 28% |
Student > Ph. D. Student | 28 | 20% |
Professor | 12 | 9% |
Student > Bachelor | 12 | 9% |
Student > Doctoral Student | 10 | 7% |
Other | 30 | 22% |
Unknown | 7 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 76 | 55% |
Biochemistry, Genetics and Molecular Biology | 16 | 12% |
Computer Science | 12 | 9% |
Social Sciences | 5 | 4% |
Environmental Science | 4 | 3% |
Other | 14 | 10% |
Unknown | 11 | 8% |