How to calculate the non-synonymous to synonymous rate ratio of protein-coding genes under the Fisher–Wright mutation–selection framework

Reis, Mario dos

doi:10.1098/rsbl.2014.1031

Cited by 42 publications

(37 citation statements)

References 14 publications

(30 reference statements)

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“…PAML was able to identify directional selection when there were a large number of sites undergoing a change in selective along short branches [the positive results required short branches in order to reduce the number of non-synonymous changes occuring under neutral or slight purifying condition (dos Reis 2015)]. Although PAML was able to detect the presence of positive selection when there were long branches with few sites (LRT), PAML was unable to determine which sites were under directional selection (BEB).…”

Section: Resultsmentioning

confidence: 99%

“…In the situations mentioned above, where the organism is adapting to a new environment or to new opportunities, the positive selection would be characterized as directional selection, as new rare alleles will be favored that better adapt the organism to its new situation. After this process is completed, the organism may become well adapted to its new environment, and purifying selection will resume (dos Reis 2015). …”

Section: Introductionmentioning

confidence: 99%

“…Such approaches are possible when many locations are under sufficiently strong selection on a short branch, which may not reflect common situations (Hughes 2007). One important limitation is that the sites that are under strong directional selection during these periods are also likely to be under strong purifying selection at other times (Messier and Stewart 1997), making it less likely that there would be sufficiently many non-synonymous substitutions during the episodic directional selection to generate d N / d S > 1 (dos Reis 2015). …”

Section: Introductionmentioning

confidence: 99%

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Finding Direction in the Search for Selection

2016

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Tests for positive selection have mostly been developed to look for diversifying selection where change away from the current amino acid is often favorable. However, in many cases we are interested in directional selection where there is a shift toward specific amino acids, resulting in increased fitness in the species. Recently, a few methods have been developed to detect and characterize directional selection on a molecular level. Using the results of evolutionary simulations as well as HIV drug resistance data as models of directional selection, we compare two such methods with each other, as well as against a standard method for detecting diversifying selection. We find that the method to detect diversifying selection also detects directional selection under certain conditions. One method developed for detecting directional selection is powerful and accurate for a wide range of conditions, while the other can generate an excessive number of false positives.Electronic supplementary materialThe online version of this article (doi:10.1007/s00239-016-9765-5) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Finding Direction in the Search for Selection

2016

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“…Therefore, many regard MutSel models as more mechanistically representative of real codingsequence evolution than dN=dS-based models, which are primarily phenomenological in nature (Thorne et al 2007(Thorne et al , 2012Holder et al 2008;Rodrigue et al 2010;Tamuri et al 2012;Liberles et al 2013). For this reason, MutSel-based simulation approaches have been used to study the behavior of phylogenetic and evolutionary rate inferences (Holder et al 2008;McCandlish et al 2013;dos Reis 2015;Spielman and Wilke 2015b).Recently, we introduced a mathematical framework that allows us to accurately calculate a dN=dS ratio directly from the parameters of a MutSel model (Spielman and Wilke 2015b). This framework gives rise to a robust benchmarking strategy through which we can simulate sequences using a MutSel model, infer dN=dS on the simulated sequences using established approaches, and then compare the inferred to the expected dN=dS given by the parameters of the MutSel model.…”

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A Comparison of One-Rate and Two-Rate Inference Frameworks for Site-Specific dN/ dS Estimation

2016

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Two broad paradigms exist for inferring dN=dS; the ratio of nonsynonymous to synonymous substitution rates, from coding sequences: (i) a one-rate approach, where dN=dS is represented with a single parameter, or (ii) a two-rate approach, where d N and d S are estimated separately. The performances of these two approaches have been well studied in the specific context of proper model specification, i.e., when the inference model matches the simulation model. By contrast, the relative performances of one-rate vs. two-rate parameterizations when applied to data generated according to a different mechanism remain unclear. Here, we compare the relative merits of one-rate and two-rate approaches in the specific context of model misspecification by simulating alignments with mutation-selection models rather than with dN=dS-based models. We find that one-rate frameworks generally infer more accurate dN=dS point estimates, even when d S varies among sites. In other words, modeling d S variation may substantially reduce accuracy of dN=dS point estimates. These results appear to depend on the selective constraint operating at a given site. For sites under strong purifying selection (dN=dS ≲ 0:3), one-rate and two-rate models show comparable performances. However, one-rate models significantly outperform two-rate models for sites under moderate-to-weak purifying selection. We attribute this distinction to the fact that, for these more quickly evolving sites, a given substitution is more likely to be nonsynonymous than synonymous. The data will therefore be relatively enriched for nonsynonymous changes, and modeling d S contributes excessive noise to dN=dS estimates. We additionally find that high levels of divergence among sequences, rather than the number of sequences in the alignment, are more critical for obtaining precise point estimates.KEYWORDS dN/dS; mutation-selection models; evolutionary rate; sequence simulation; molecular evolution A variety of computational approaches have been developed to infer selection pressure from protein-coding sequences in a phylogenetically aware context. Among the most commonly used methods are those that compute the evolutionary rate ratio dN=dS; which represents the ratio of nonsynonymous to synonymous substitution rates. Beginning in the mid-1990s, this value has been calculated using maximum-likelihood (ML) approaches (Goldman and Yang 1994;Muse and Gaut 1994), and since then, a wide variety of inference frameworks have been developed to infer dN=dS at individual sites in protein-coding sequences (Nielsen and Yang 1998;Yang et al. 2000;Yang and Nielsen 2002;Yang and Swanson 2002;Lemey et al. 2012;Murrell et al. 2012bMurrell et al. , 2013.Typically, the performance of evolutionary models is examined using simulation-based approaches wherein sequences are simulated according to the model being examined, and inferences are subsequently performed on the simulated data. This approach ensures that simulated and inferred parameters correspond. Although useful, this strategy fundam...

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Molecular evolution and phylogenomic analysis of complete chloroplast genomes of Cotinus (Anacardiaceae)

Liu

Yang

Dong

et al. 2023

Ecology and Evolution

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Mill. is an oligo-specific genus in the family Anacardiaceae with great ornamental value. It exhibits a disjunct distribution in the Northern Hemisphere particularly in southern Europe, eastern Asia, and North America (Matić et al., 2016;Pell et al., 2011). The common name for Cotinus, "smoketree or smokebush", refers to its unique large panicles, covered with tiny, persistent feathery plumes that give the inflorescences a wispy smoke-like appearance. In addition, owing to their fast growth and drought tolerance, Cotinus species

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How to calculate the non-synonymous to synonymous rate ratio of protein-coding genes under the Fisher–Wright mutation–selection framework

Cited by 42 publications

References 14 publications

Finding Direction in the Search for Selection

Finding Direction in the Search for Selection

A Comparison of One-Rate and Two-Rate Inference Frameworks for Site-Specific dN/ dS Estimation

Molecular evolution and phylogenomic analysis of complete chloroplast genomes of Cotinus (Anacardiaceae)

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