A Generalized Mechanistic Codon Model

Zaheri, Maryam; Dib, Linda; Salamin, Nicolas

doi:10.1093/molbev/msu196

Cited by 26 publications

(37 citation statements)

References 78 publications

(143 reference statements)

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“…Overall, these relations between forces acting on ω 0 deserve further investigation with more elaborate evolutionary models (e.g., Murrell et al 2012;Zaheri et al 2014). Despite the limitations of the estimation of positive selection, this is the component of evolutionary rate which has the strongest partial correlation with the level of gene expression, both with the median expression over all tissues, and with expression in brain tissues.…”

Section: Global Study Of Evolutionary Ratementioning

confidence: 98%

Tissue-specific evolution of protein coding genes in human and mouse

Kryuchkova

Robinson‐Rechavi

2014

Preprint

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Protein-coding genes evolve at different rates, and the influence of different parameters, from gene size to expression level, has been extensively studied. While in yeast gene expression level is the major causal factor of gene evolutionary rate, the situation is more complex in animals. Here we investigate these relations further, especially taking in account gene expression in different organs as well as indirect correlations between parameters. We used RNA-seq data from two large datasets, covering 22 mouse tissues and 27 human tissues. Over all tissues, evolutionary rate only correlates weakly with levels and breadth of expression. The strongest explanatory factors of purifying selection are GC content, expression in many developmental stages, and expression in brain tissues. While the main component of evolutionary rate is purifying selection, we also find tissue-specific patterns for sites under neutral evolution and for positive selection. We observe fast evolution of genes expressed in testis, but also in other tissues, notably liver, which are explained by weak purifying selection rather than by positive selection.

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Section: Global Study Of Evolutionary Ratementioning

confidence: 98%

Tissue-specific evolution of protein coding genes in human and mouse

Kryuchkova

Robinson‐Rechavi

2014

Preprint

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“…Moreover, because of its versatility, Bio++ allows for more parameterization and the easy declaration of many modelings that would permit to detect positive selection under user-defined scenarios (e.g. using non-homogeneous mixture models, or other kinds of models such as allowing amino-acid specificity or simultaneous substitutions (Weber et al, 2019, Zaheri et al, 2014).…”

Section: Dginn Integrates Numerous Methods For the Detection Of Positmentioning

confidence: 99%

DGINN, an automated and highly-flexible pipeline for the Detection of Genetic INNovations on protein-coding genes

Picard

Ganivet

Allatif

et al. 2020

Preprint

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Adaptive evolution has shaped major biological processes. Finding the protein-coding genes and the sites that have been subjected to adaptation during evolutionary time is a major endeavor. However, very few methods fully automate the identification of positively selected genes, and widespread sources of genetic innovations as gene duplication and recombination are absent from most pipelines. Here, we developed DGINN, a highly-flexible and public pipeline to Detect Genetic INNovations and adaptive evolution in protein-coding genes. DGINN automates, from a gene's sequence, all steps of the evolutionary analyses necessary to detect the aforementioned innovations, including the search for homologues in databases, assignation of orthology groups, identification of duplication and recombination events, as well as detection of positive selection using five different methods to increase precision and ranking of genes when a large panel is analyzed. DGINN was validated on nineteen genes with previously-characterized evolutionary histories in primates, including some engaged in host-pathogen arms-races. The results obtained with DGINN confirm and also expand results from the literature, establishing DGINN as an efficient tool to automatically detect genetic innovations and adaptive evolution in diverse datasets, from the user's gene of interest to a large gene list in any species range. Running Title: DGINN, an automated pipeline to Detect Genetic INNovations

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“…The KCM model of (Zaheri et al, 2014) only has a single rate for multiple hits (double or triple), and has position-specific nucleotide substitution rates (θ in our notation), so it would be most comparable to the 3H model with δ = ψ.…”

Section: Substitution Modelsmentioning

confidence: 99%

“…via their effects on protein folding (Okada et al, 2017). Whelan and Goldman (2004) ("... these events [MH] are far more prevalent than previously thought"), Zaheri et al (2014), and Dunn et al (2019) (the latter two studies show a dramatically better model fit to empirical alignments when allowing MH). Other studies that "output" -2020/5/14 -0:29 -page 3 -#3…”

Section: Introductionmentioning

confidence: 99%

Extra base hits: widespread empirical support for instantaneous multiple-nucleotide changes

Lucaci

Wisotsky

Shank

et al. 2020

Preprint

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Despite many attempts to introduce evolutionary models that permit substitutions that instantly alter more than one nucleotide in a codon, the prevailing wisdom remains that such changes are rare and generally negligible (or are reflective of non-biological artifacts, such as alignment errors), and codon models continue to posit that only single nucleotide change have non-zero rates. We develop and test a simple hierarchy of codon-substitution models with non-zero evolutionary rates for only one-nucleotide (1H), one-and two-nucleotide (2H), or any (3H) codon substitutions. Using 35,000 empirical alignments, we find widespread statistical support for multiple hits: 58% of alignments prefer models with 2H allowed, and 22% -with 3H allowed. Analyses of simulated data suggest that these results are not likely to be due to simple artifacts such as model misclassification or alignment errors. Further modeling revealed that synonymous codon island jumping among codons encoding serine, especially along short branches, contributes significantly to this 3H signal. While serine codons were prominently involved in multiplehit substitutions, there were other common exchanges contributing to better model fit. It appears that a small subset of sites in most alignments have unusual evolutionary dynamics not well explained by existing model formalisms, and that commonly estimated quantities, such as dN/dS ratios may be biased by model misspecification. Our findings highlight the need for continued evaluation of assumptions underlying workhorse evolutionary models and subsequent evolutionary inference techniques. We provide a software implementation for evolutionary biologists to assess the potential impact of extra base hits in their data in the HyPhy package.

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A Generalized Mechanistic Codon Model

Cited by 26 publications

References 78 publications

Tissue-specific evolution of protein coding genes in human and mouse

Tissue-specific evolution of protein coding genes in human and mouse

DGINN, an automated and highly-flexible pipeline for the Detection of Genetic INNovations on protein-coding genes

Extra base hits: widespread empirical support for instantaneous multiple-nucleotide changes

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