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Rock, paper, scissors: harnessing complementarity in ortholog detection methods improves comparative genomic inference.

Maher MC, Hernandez RD - G3 (Bethesda) (2015)

Bottom Line: OD methods comprise a wide variety of approaches, each with their own benefits and costs under a variety of evolutionary and practical scenarios.In head-to-head comparisons, we find that these algorithms significantly outperform one another for 38-45% of the genes analyzed.Further, this improvement in alignment quality yields more confidently aligned sites and higher levels of overall conservation, while simultaneously detecting of up to 180% more positively selected sites.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco, University of California, San Francisco, San Francisco, California.

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Related in: MedlinePlus

A schematic of the sequence selection algorithm. Steps: (1) Construct graph; (2) Choose the sequence from a random OD method for each species; (3) Iterate through species. For each species, pick the orthologs with highest similarity to the current best choices for all other species; (4) Return current best choices if no changes are made after iterating through all species; (5) To find global optimum, repeat steps 1-4 with random sampling paths.
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fig1: A schematic of the sequence selection algorithm. Steps: (1) Construct graph; (2) Choose the sequence from a random OD method for each species; (3) Iterate through species. For each species, pick the orthologs with highest similarity to the current best choices for all other species; (4) Return current best choices if no changes are made after iterating through all species; (5) To find global optimum, repeat steps 1-4 with random sampling paths.

Mentions: MOSAIC provides a highly flexible, graph-based framework for integrating diverse OD methods. For a given reference sequence, proposal orthologs are conceptualized as nodes in a graph, connected with edges weighted according to the pairwise similarity between sequences (Figure 1). The task of OD integration is then to choose proposal orthologs from each species such that a chosen measure of intra-cluster similarity is optimized.


Rock, paper, scissors: harnessing complementarity in ortholog detection methods improves comparative genomic inference.

Maher MC, Hernandez RD - G3 (Bethesda) (2015)

A schematic of the sequence selection algorithm. Steps: (1) Construct graph; (2) Choose the sequence from a random OD method for each species; (3) Iterate through species. For each species, pick the orthologs with highest similarity to the current best choices for all other species; (4) Return current best choices if no changes are made after iterating through all species; (5) To find global optimum, repeat steps 1-4 with random sampling paths.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4390578&req=5

fig1: A schematic of the sequence selection algorithm. Steps: (1) Construct graph; (2) Choose the sequence from a random OD method for each species; (3) Iterate through species. For each species, pick the orthologs with highest similarity to the current best choices for all other species; (4) Return current best choices if no changes are made after iterating through all species; (5) To find global optimum, repeat steps 1-4 with random sampling paths.
Mentions: MOSAIC provides a highly flexible, graph-based framework for integrating diverse OD methods. For a given reference sequence, proposal orthologs are conceptualized as nodes in a graph, connected with edges weighted according to the pairwise similarity between sequences (Figure 1). The task of OD integration is then to choose proposal orthologs from each species such that a chosen measure of intra-cluster similarity is optimized.

Bottom Line: OD methods comprise a wide variety of approaches, each with their own benefits and costs under a variety of evolutionary and practical scenarios.In head-to-head comparisons, we find that these algorithms significantly outperform one another for 38-45% of the genes analyzed.Further, this improvement in alignment quality yields more confidently aligned sites and higher levels of overall conservation, while simultaneously detecting of up to 180% more positively selected sites.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco, University of California, San Francisco, San Francisco, California.

Show MeSH
Related in: MedlinePlus