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11 results for “Codon models”
Modeling and measuring how codon usage modulates the relationship between burden and yield during protein overexpression in bacteria
<p>Additional data from experiments associated with paper revisions.</p> <p>Also added codon optimizer script.</p>
A codon model for associating phenotypic traits with altered selective patterns of sequence evolution
<p>Detecting the signature of selection in coding sequences and associating it with shifts in phenotypic states can unveil genes underlying complex traits. Of the various signatures of selection exhibited at the molecular level, changes in the pattern of selection at protein coding genes have been of main interest. To this end, phylogenetic branch-site codon models are routinely applied to detect changes in selective patterns along specific branches of the phylogeny. Many of these methods rely on a pre-specified partition of the phylogeny to branch categories, thus treating the course of trait evolution as fully resolved and assuming that phenotypic transitions have occurred only at speciation events. Here we present TraitRELAX, a new phylogenetic model that alleviates these strong assumptions by explicitly accounting for the uncertainty in the evolution of both trait and coding sequences. This joint statistical framework enables the detection of changes in selection intensity upon repeated trait transitions. We evaluated the performance of TraitRELAX using simulations and then applied it to two case studies. Using TraitRELAX, we found an intensification of selection in the primate SEMG2 gene in polygynandrous species compared to species of other mating forms, as well as changes in the intensity of purifying selection operating on sixteen bacterial genes upon transitioning from a free-living to an endosymbiotic lifestyle.</p>
A codon model for associating phenotypic traits with altered selective patterns of sequence evolution
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Data from: A phenotype-genotype codon model for detecting adaptive evolution
A central objective in biology is to link adaptive evolution in a gene to structural and/or functional phenotypic novelties. Yet most analytic methods make inferences mainly from either phenotypic data or genetic data alone. A small number of models have been developed to infer correlations between the rate of molecular evolution and changes in a discrete or continuous life history trait. But such correlations are not necessarily evidence of adaptation. Here we present a novel approach called the phenotype-genotype branch-site model (PG-BSM) designed to detect evidence of adaptive codon evolution associated with discrete-state phenotype evolution. An episode of adaptation is inferred under standard codon substitution models when there is evidence of positive selection in the form of an elevation in the nonsynonymous-to-synonymous rate ratio ω to a value ω > 1. As it is becoming increasingly clear that ω > 1 can occur without adaptation, the PG-BSM was formulated to infer an instance of adaptive evolution without appealing to evidence of positive selection. The null model makes use of a covarion-like component to account for general heterotachy (i.e., random changes in the evolutionary rate at a site over time). The alternative model employs samples of the phenotypic evolutionary history to test for phenomenological patterns of heterotachy consistent with specific mechanisms of molecular adaptation. These include (i) a persistent increase/decrease in ω at a site following a change in phenotype (the pattern) consistent with an increase/decrease in the functional importance of the site (the mechanism); and (ii) a transient increase in ω at a site along a branch over which the phenotype changed (the pattern) consistent with a change in the site's optimal amino acid (the mechanism). Rejection of the null is followed by post hoc analyses to identify sites with strongest evidence for adaptation in association with changes in the phenotype as well as the most likely evolutionary history of the phenotype. Simulation studies based on a novel method for generating mechanistically realistic signatures of molecular adaptation show that the PG-BSM has good statistical properties. Analyses of three real alignments show that site patterns identified post hoc are consistent with the specific mechanisms of adaptation included in the alternate model. Further simulation studies show that the covarion-like component of the PG-BSM plays a crucial role in mitigating recently discovered statistical pathologies associated with confounding by accounting for heterotachy-by-any-means.
Data for Standard Codon Substitution Models Overestimate Purifying Selection for Non-Stationary Data
<p>Codon-aligned, filtered alignments for Kaehler et al. (2016) (https://peerj.com/preprints/2218/). Please refer to preprint for preparation details.</p> <p>Data obtained from Ensembl (http://www.ensembl.org/) and antbase (http://antbase.org).</p> <p> </p> <p> </p>
Data from: A phenotype-genotype codon model for detecting adaptive evolution
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Data from: ModelOMatic: fast and automated comparison between RY, nucleotide, amino acid, and codon substitution models
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Modeling ribosome dwell times and relationship with tRNA loading and codon usage in mammals (Ribosome Profiling)
GEO Series GSE126383. Mus musculus. 11 samples. Type: Other.
Modeling ribosome dwell times and relationship with tRNA loading and codon usage in mammals (tRNA Profiling)
GEO Series GSE126382. Mus musculus. 24 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Data from: Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates
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miRNAs and codon usage regulate striatal gene and protein expression in two mouse models of Parkinson’s disease
GEO Series GSE8030. Mus musculus. 9 samples. Type: Expression profiling by array.
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.