scholarly journals Standard codon substitution models overestimate purifying selection for non-stationary data

2016 ◽  
Author(s):  
Benjamin D Kaehler ◽  
Von Bing Yap ◽  
Gavin A Huttley
Keyword(s):  
Natural Selection ◽  
De Novo ◽  
Purifying Selection ◽  
Neutral Evolution ◽  
Protein Coding ◽  
Synonymous Substitutions ◽  
New Model ◽  
Sequence Composition ◽  
Codon Substitution ◽  
Substitution Models

Estimation of natural selection on protein-coding sequences is a key comparative genomics approach for de novo prediction of lineage specific adaptations. Selective pressure is measured on a per-gene basis by comparing the rate of non-synonymous substitutions to the rate of neutral evolution, typically assumed to be the rate of synonymous substitutions. All published codon substitution models have been time-reversible and thus assume that sequence composition does not change over time. We previously demonstrated that if time-reversible DNA substitution models are applied blindly in the presence of changing sequence composition, the number of substitutions is systematically biased towards overestimation. We extend these findings to the case of codon substitution models and further demonstrate that the ratio of non-synonymous to synonymous rates of substitution tends to be underestimated over three data sets of insects, mammals, and vertebrates. Our basis for comparison is a non-stationary codon substitution model that allows sequence composition to change. Model selection and model fit results demonstrate that our new model tends to fit the data better. Direct measurement of non-stationarity shows that bias in estimates of natural selection and genetic distance increases with the degree of violation of the stationarity assumption. Additionally, inferences drawn under time-reversible models are systematically affected by compositional divergence. As genomic sequences accumulate at an accelerating rate, the importance of accurate de novo estimation of natural selection increases. Our results establish that our new model provides a more robust perspective on this fundamental quantity.

scholarly journals Standard codon substitution models overestimate purifying selection for non-stationary data

2016 ◽  
Author(s):  
Benjamin D Kaehler ◽  
Von Bing Yap ◽  
Gavin A Huttley
Keyword(s):  
Natural Selection ◽  
De Novo ◽  
Purifying Selection ◽  
Neutral Evolution ◽  
Protein Coding ◽  
Synonymous Substitutions ◽  
New Model ◽  
Sequence Composition ◽  
Codon Substitution ◽  
Substitution Models

Estimation of natural selection on protein-coding sequences is a key comparative genomics approach for de novo prediction of lineage specific adaptations. Selective pressure is measured on a per-gene basis by comparing the rate of non-synonymous substitutions to the rate of neutral evolution, typically assumed to be the rate of synonymous substitutions. All published codon substitution models have been time-reversible and thus assume that sequence composition does not change over time. We previously demonstrated that if time-reversible DNA substitution models are applied blindly in the presence of changing sequence composition, the number of substitutions is systematically biased towards overestimation. We extend these findings to the case of codon substitution models and further demonstrate that the ratio of non-synonymous to synonymous rates of substitution tends to be underestimated over three data sets of insects, mammals, and vertebrates. Our basis for comparison is a non-stationary codon substitution model that allows sequence composition to change. Model selection and model fit results demonstrate that our new model tends to fit the data better. Direct measurement of non-stationarity shows that bias in estimates of natural selection and genetic distance increases with the degree of violation of the stationarity assumption. Additionally, inferences drawn under time-reversible models are systematically affected by compositional divergence. As genomic sequences accumulate at an accelerating rate, the importance of accurate de novo estimation of natural selection increases. Our results establish that our new model provides a more robust perspective on this fundamental quantity.

scholarly journals How do we transition from non-coding to coding?

2017 ◽  
Author(s):  
Jorge Ruiz-Orera ◽  
José Luis Villanueva-Cañas ◽  
William Blevins ◽  
M.Mar Albà
Keyword(s):  
De Novo ◽  
Gene Evolution ◽  
Purifying Selection ◽  
Neutral Evolution ◽  
Functional Protein ◽  
Protein Coding ◽  
Coding Sequences ◽  
Sequence Composition ◽  
Protein Coding Genes ◽  
Small Proteins

Recent years have witnessed the discovery of protein–coding genes which appear to have evolved de novo from previously non-coding sequences. This has changed the long-standing view that coding sequences can only evolve from other coding sequences. However, there are still many open questions regarding how new protein-coding sequences can arise from non-genic DNA. Two prerequisites for the birth of a new functional protein-coding gene are that the corresponding DNA fragment is transcribed and that it is also translated. Transcription is known to be pervasive in the genome, producing a large number of transcripts that do not correspond to conserved protein-coding genes, and which are usually annotated as long non-coding RNAs (lncRNA). Recently, sequencing of ribosome protected fragments (Ribo-Seq) has provided evidence that many of these transcripts actually translate small proteins. We have used mouse non-synonymous and synonymous variation data to estimate the strength of purifying selection acting on the translated open reading frames (ORFs). Whereas a subset of the lncRNAs are likely to actually be true protein-coding genes (and thus previously misclassified), the bulk of lncRNAs code for proteins which show variation patterns consistent with neutral evolution. We also show that the ORFs that have a more favorable, coding-like, sequence composition are more likely to be translated than other ORFs in lncRNAs. This study provides strong evidence that there is a large and ever-changing reservoir of lowly abundant proteins; some of these peptides may become useful and act as seeds for de novo gene evolution.

scholarly journals De novo gene evolution: How do we transition from non-coding to coding?

2017 ◽  
Author(s):  
Jorge Ruiz-Orera ◽  
José Luis Villanueva-Cañas ◽  
William Blevins ◽  
M.Mar Albà
Keyword(s):  
De Novo ◽  
Gene Evolution ◽  
Neutral Evolution ◽  
Functional Protein ◽  
Protein Coding ◽  
Coding Sequences ◽  
Sequence Composition ◽  
Protein Coding Genes ◽  
Small Proteins ◽  
De Novo Gene

Recent years have witnessed the discovery of protein–coding genes which appear to have evolved de novo from previously non-coding sequences. This has changed the long-standing view that coding sequences can only evolve from other coding sequences. However, there are still many open questions regarding how new protein-coding sequences can arise from non-genic DNA. Two prerequisites for the birth of a new functional protein-coding gene are that the corresponding DNA fragment is transcribed and that it is also translated. Transcription is known to be pervasive in the genome, producing a large number of transcripts that do not correspond to conserved protein-coding genes, and which are usually annotated as long non-coding RNAs (lncRNA). Recently, sequencing of ribosome protected fragments (Ribo-Seq) has provided evidence that many of these transcripts actually translate small proteins. We have used mouse non-synonymous and synonymous variation data to estimate the strength of purifying selection acting on the translated open reading frames (ORFs). Whereas a subset of the lncRNAs are likely to actually be true protein-coding genes (and thus previously misclassified), the bulk of lncRNAs code for proteins which show variation patterns consistent with neutral evolution. We also show that the ORFs that have a more favorable, coding-like, sequence composition are more likely to be translated than other ORFs in lncRNAs. This study provides strong evidence that there is a large and ever-changing reservoir of lowly abundant proteins; some of these peptides may become useful and act as seeds for de novo gene evolution.

scholarly journals De novo gene evolution: How do we transition from non-coding to coding?

2017 ◽  
Author(s):  
Jorge Ruiz-Orera ◽  
José Luis Villanueva-Cañas ◽  
William Blevins ◽  
M.Mar Albà
Keyword(s):  
De Novo ◽  
Gene Evolution ◽  
Neutral Evolution ◽  
Functional Protein ◽  
Protein Coding ◽  
Coding Sequences ◽  
Sequence Composition ◽  
Protein Coding Genes ◽  
Small Proteins ◽  
De Novo Gene

Recent years have witnessed the discovery of protein–coding genes which appear to have evolved de novo from previously non-coding sequences. This has changed the long-standing view that coding sequences can only evolve from other coding sequences. However, there are still many open questions regarding how new protein-coding sequences can arise from non-genic DNA. Two prerequisites for the birth of a new functional protein-coding gene are that the corresponding DNA fragment is transcribed and that it is also translated. Transcription is known to be pervasive in the genome, producing a large number of transcripts that do not correspond to conserved protein-coding genes, and which are usually annotated as long non-coding RNAs (lncRNA). Recently, sequencing of ribosome protected fragments (Ribo-Seq) has provided evidence that many of these transcripts actually translate small proteins. We have used mouse non-synonymous and synonymous variation data to estimate the strength of purifying selection acting on the translated open reading frames (ORFs). Whereas a subset of the lncRNAs are likely to actually be true protein-coding genes (and thus previously misclassified), the bulk of lncRNAs code for proteins which show variation patterns consistent with neutral evolution. We also show that the ORFs that have a more favorable, coding-like, sequence composition are more likely to be translated than other ORFs in lncRNAs. This study provides strong evidence that there is a large and ever-changing reservoir of lowly abundant proteins; some of these peptides may become useful and act as seeds for de novo gene evolution.

scholarly journals Contribution of retrotransposition to developmental disorders

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Eugene J. Gardner ◽  
Elena Prigmore ◽  
Giuseppe Gallone ◽  
Petr Danecek ◽  
Kaitlin E. Samocha ◽  
...  
Keyword(s):  
Developmental Disorders ◽  
De Novo ◽  
Purifying Selection ◽  
Selective Constraint ◽  
Protein Coding ◽  
Genome Wide ◽  
De Novo Gene ◽  
The Impact ◽  
Transcribed Sequences

Abstract Mobile genetic Elements (MEs) are segments of DNA which can copy themselves and other transcribed sequences through the process of retrotransposition (RT). In humans several disorders have been attributed to RT, but the role of RT in severe developmental disorders (DD) has not yet been explored. Here we identify RT-derived events in 9738 exome sequenced trios with DD-affected probands. We ascertain 9 de novo MEs, 4 of which are likely causative of the patient’s symptoms (0.04%), as well as 2 de novo gene retroduplications. Beyond identifying likely diagnostic RT events, we estimate genome-wide germline ME mutation rate and selective constraint and demonstrate that coding RT events have signatures of purifying selection equivalent to those of truncating mutations. Overall, our analysis represents a comprehensive interrogation of the impact of retrotransposition on protein coding genes and a framework for future evolutionary and disease studies.

scholarly journals Crossing fitness valleys via double substitutions within codons

BMC Biology ◽  
2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Frida Belinky ◽  
Itamar Sela ◽  
Igor B. Rogozin ◽  
Eugene V. Koonin
Keyword(s):  
Amino Acid ◽  
Positive Selection ◽  
Fitness Landscape ◽  
Purifying Selection ◽  
Second Step ◽  
Nucleotide Substitutions ◽  
Protein Coding ◽  
Synonymous Substitutions ◽  
Ancestral States ◽  
Double Substitution

Abstract Background Single nucleotide substitutions in protein-coding genes can be divided into synonymous (S), with little fitness effect, and non-synonymous (N) ones that alter amino acids and thus generally have a greater effect. Most of the N substitutions are affected by purifying selection that eliminates them from evolving populations. However, additional mutations of nearby bases potentially could alleviate the deleterious effect of single substitutions, making them subject to positive selection. To elucidate the effects of selection on double substitutions in all codons, it is critical to differentiate selection from mutational biases. Results We addressed the evolutionary regimes of within-codon double substitutions in 37 groups of closely related prokaryotic genomes from diverse phyla by comparing the fractions of double substitutions within codons to those of the equivalent double S substitutions in adjacent codons. Under the assumption that substitutions occur one at a time, all within-codon double substitutions can be represented as “ancestral-intermediate-final” sequences (where “intermediate” refers to the first single substitution and “final” refers to the second substitution) and can be partitioned into four classes: (1) SS, S intermediate–S final; (2) SN, S intermediate–N final; (3) NS, N intermediate–S final; and (4) NN, N intermediate–N final. We found that the selective pressure on the second substitution markedly differs among these classes of double substitutions. Analogous to single S (synonymous) substitutions, SS double substitutions evolve neutrally, whereas analogous to single N (non-synonymous) substitutions, SN double substitutions are subject to purifying selection. In contrast, NS show positive selection on the second step because the original amino acid is recovered. The NN double substitutions are heterogeneous and can be subject to either purifying or positive selection, or evolve neutrally, depending on the amino acid similarity between the final or intermediate and the ancestral states. Conclusions The results of the present, comprehensive analysis of the evolutionary landscape of within-codon double substitutions reaffirm the largely conservative regime of protein evolution. However, the second step of a double substitution can be subject to positive selection when the first step is deleterious. Such positive selection can result in frequent crossing of valleys on the fitness landscape.

scholarly journals De novo emergence of adaptive membrane proteins from thymine-rich genomic sequences

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Nikolaos Vakirlis ◽  
Omer Acar ◽  
Brian Hsu ◽  
Nelson Castilho Coelho ◽  
S. Branden Van Oss ◽  
...  
Keyword(s):  
Natural Selection ◽  
De Novo ◽  
Natural Populations ◽  
Evolutionary Model ◽  
Transmembrane Domains ◽  
Protein Coding ◽  
Fitness Effects ◽  
Evolutionary Innovation ◽  
Intergenic Regions ◽  
Novel Protein

AbstractRecent evidence demonstrates that novel protein-coding genes can arise de novo from non-genic loci. This evolutionary innovation is thought to be facilitated by the pervasive translation of non-genic transcripts, which exposes a reservoir of variable polypeptides to natural selection. Here, we systematically characterize how these de novo emerging coding sequences impact fitness in budding yeast. Disruption of emerging sequences is generally inconsequential for fitness in the laboratory and in natural populations. Overexpression of emerging sequences, however, is enriched in adaptive fitness effects compared to overexpression of established genes. We find that adaptive emerging sequences tend to encode putative transmembrane domains, and that thymine-rich intergenic regions harbor a widespread potential to produce transmembrane domains. These findings, together with in-depth examination of the de novo emerging YBR196C-A locus, suggest a novel evolutionary model whereby adaptive transmembrane polypeptides emerge de novo from thymine-rich non-genic regions and subsequently accumulate changes molded by natural selection.

A codon model of nucleotide substitution with selection on synonymous codon usage

2014 ◽  
Author(s):  
Laura Kubatko ◽  
Premal Shah ◽  
Radu Herbei ◽  
Michael Gilchrist
Keyword(s):  
Protein Production ◽  
Synonymous Codon ◽  
Error Rates ◽  
Selection Model ◽  
Substitution Model ◽  
Protein Coding ◽  
New Model ◽  
Protein Coding Genes ◽  
Codon Substitution ◽  
Yeast Genes

The quality of phylogenetic inference made from protein-coding genes depends, in part, on the realism with which the codon substitution process is modeled. Here we propose a new mechanistic model that combines the standard M0 substitution model of Yang (1997) with a simplified model from Gilchrist (2007) that includes selection on synonymous substitutions as a function of codon-specific nonsense error rates. We tested the newly proposed model by applying it to 104 protein-coding genes in brewer's yeast, and compared the fit of the new model to the standard M0 model and to the mutation-selection model of Yang and Nielsen (2008) using the AIC. Our new model provided significantly better fit in approximately 85% of the cases considered for the basic M0 model and in approximately 25% of the cases for the M0 model with estimated codon frequencies, but only in a few cases when the mutation-selection model was considered. However, our model includes a parameter that can be interpreted as a measure of the rate of protein production, and the estimates of this parameter were highly correlated with an independent measure of protein production for the yeast genes considered here. Finally, we found that in some cases the new model led to the preference of a different phylogeny for a subset of the genes considered, indicating that substitution model choice may have an impact on the estimated phylogeny.

scholarly journals Extreme purifying selection against point mutations in the human genome

2021 ◽  
Author(s):  
Noah Dukler ◽  
Mehreen R Mughal ◽  
Ritika Ramani ◽  
Yi-Fei Huang ◽  
Adam Siepel
Keyword(s):  
Human Genome ◽  
De Novo ◽  
Point Mutations ◽  
Purifying Selection ◽  
Selection Coefficient ◽  
Sequencing Data ◽  
Protein Coding ◽  
Coding Regions ◽  
Protein Coding Genes ◽  
Selective Effects

Genome sequencing of tens of thousands of human individuals has recently enabled the measurement of large selective effects for mutations to protein-coding genes. Here we describe a new method, called ExtRaINSIGHT, for measuring similar selective effects at individual sites in noncoding as well as in coding regions of the human genome. ExtRaINSIGHT estimates the prevalance of strong purifying selection, or "ultraselection" (λs), as the fractional depletion of rare single-nucleotide variants (minor allele frequency <0.1%) in a target set of genomic sites relative to matched sites that are putatively neutrally evolving, in a manner that controls for local variation and neighbor-dependence in mutation rate. We show using simulations that, above an appropriate threshold, λs is closely related to the average site-specific selection coefficient against heterozygous point mutations, as predicted at mutation-selection balance. Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find particularly strong evidence of ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. Moreover, our estimated selection coefficient against heterozygous amino-acid replacements across the genome (at 1.4%) is substantially larger than previous estimates based on smaller sample sizes. By contrast, we find weak evidence of ultraselection in other noncoding RNAs and transcription factor binding sites, and only modest evidence in ultraconserved elements and human accelerated regions. We estimate that ~0.3-0.5% of the human genome is ultraselected, with one third to one half of ultraselected sites falling in coding regions. These estimates suggest ~0.3-0.4 lethal or nearly lethal de novo mutations per potential human zygote, together with ~2 de novo mutations that are more weakly deleterious. Overall, our study sheds new light on the genome-wide distribution of fitness effects for new point mutations by combining deep new sequencing data sets and classical theory from population genetics.

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