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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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Comparison of sequence identity levels between methods. Heat map of the percent of orthologs for which MultiParanoid (MP), OMA (OMA), BLAT (BL), and MultiZ (MZ) outperform one another. Performance is based on percent identity of each method’s orthologs to the human sequence. One method is considered to outperform another method if it improves percent identity by at least five percentage points. Text in diagonal cells shows the number of orthologs identified by each method, colored by the percent of orthologs for which a given method outperforms all the others.
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fig2: Comparison of sequence identity levels between methods. Heat map of the percent of orthologs for which MultiParanoid (MP), OMA (OMA), BLAT (BL), and MultiZ (MZ) outperform one another. Performance is based on percent identity of each method’s orthologs to the human sequence. One method is considered to outperform another method if it improves percent identity by at least five percentage points. Text in diagonal cells shows the number of orthologs identified by each method, colored by the percent of orthologs for which a given method outperforms all the others.

Mentions: We begin with a comprehensive comparison of four popular, methodologically distinct OD methods. In Figure 2, we show the head-to-head performances of these different methods for a range of primates and closely related mammals. Performance is assessed using alignments between all human consensus coding sequences (Pruitt et al. 2009) and their corresponding orthologs from each method. For each possible ortholog (defined by human target sequence and species of origin), we examine whether sequence identity to human is at least five percentage points greater for one method vs. another. We otherwise consider the two methods to be tied. By this metric, one method significantly outperforms another 38−45% of the time. Importantly, no method uniformly outperforms all others, underlining the complementarity of the chosen algorithms. For each method, distributions of percent identity and relative performance by species are presented in Figure S1 and Figure S2,


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

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

Comparison of sequence identity levels between methods. Heat map of the percent of orthologs for which MultiParanoid (MP), OMA (OMA), BLAT (BL), and MultiZ (MZ) outperform one another. Performance is based on percent identity of each method’s orthologs to the human sequence. One method is considered to outperform another method if it improves percent identity by at least five percentage points. Text in diagonal cells shows the number of orthologs identified by each method, colored by the percent of orthologs for which a given method outperforms all the others.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Comparison of sequence identity levels between methods. Heat map of the percent of orthologs for which MultiParanoid (MP), OMA (OMA), BLAT (BL), and MultiZ (MZ) outperform one another. Performance is based on percent identity of each method’s orthologs to the human sequence. One method is considered to outperform another method if it improves percent identity by at least five percentage points. Text in diagonal cells shows the number of orthologs identified by each method, colored by the percent of orthologs for which a given method outperforms all the others.
Mentions: We begin with a comprehensive comparison of four popular, methodologically distinct OD methods. In Figure 2, we show the head-to-head performances of these different methods for a range of primates and closely related mammals. Performance is assessed using alignments between all human consensus coding sequences (Pruitt et al. 2009) and their corresponding orthologs from each method. For each possible ortholog (defined by human target sequence and species of origin), we examine whether sequence identity to human is at least five percentage points greater for one method vs. another. We otherwise consider the two methods to be tied. By this metric, one method significantly outperforms another 38−45% of the time. Importantly, no method uniformly outperforms all others, underlining the complementarity of the chosen algorithms. For each method, distributions of percent identity and relative performance by species are presented in Figure S1 and Figure S2,

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