Rock, paper, scissors: harnessing complementarity in ortholog detection methods improves comparative genomic inference.
Bottom Line: This task involves accurately identifying genes across species that descend from a common ancestral sequence.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.
Affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco, University of California, San Francisco, San Francisco, California.Show MeSH
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Mentions: Having presented a schematic of the algorithm itself in Figure 1, we provide in Figure 3 a view of example inputs and output MSAs. These are illustrations of real alignments for carbonic anhydrase 12, an enzyme critical to a number of biological functions, including the formation of bone, saliva, and gastric acid (Pruitt et al. 2014). MSA columns that are aligned to below 95% confidence are displayed in red and masked from the analysis. Orthologs that were not returned for a given species are denoted with a horizontal black bar. Those that were filtered using pre-integration sequence identity cutoffs (see the section Materials and Methods) are indicated with gray bars. Sequence identity is measured based on pairwise realignment to the human sequence. Note that, just as when employing a single ortholog detection method, this filtering step is critical to guaranteeing alignment quality.
Affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco, University of California, San Francisco, San Francisco, California.