The Long-Range Haplotype (LRH), integrated Haplotype Score (iHS) and Cross Population EHH (XP-EHH) tests detect alleles that have risen to high frequency rapidly enough that long-range association with nearby polymorphisms—the long-range haplotype—has not been eroded by recombination; haplotype length is measured by the EHH8 (link),9 (link). The first two tests detect partial selective sweeps, whereas XP-EHH detects selected alleles that have risen to near fixation in one but not all populations. To evaluate the tests, we simulated genomic data for each HapMap population in a range of demographic scenarios—under neutral evolution and twenty scenarios of positive selection—developing the program Sweep (www.broad.mit.edu/mpg/sweep ) for analysis. For our top candidates by the three tests, we tested for haplotype-specific recombination rates and copy-number polymorphisms, possible confounders.
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Evolution, Neutral
Evolution, Neutral
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Most cited protocols related to «Evolution, Neutral»
Alleles
Copy Number Polymorphism
Evolution, Neutral
Genetic Polymorphism
Haplotypes
HapMap
Population Group
Recombination, Genetic
The APPRIS annotation pipeline deploys a range of computational methods to provide value to the annotations of the human genome. The server flags variants that code for proteins with altered structure, function or localization, and exons that are evolving in a non-neutral fashion. APPRIS also selects one of the CDS for each gene as the principal functional isoform. The pipeline is made up of separate modules that combine protein structure and function information and evolutionary evidence. Each module has been implemented as a separate web service.
firestar (Lopez et al. 2007 (link), 2011 (link)) is a method that predicts functionally important residues in protein sequences.
Matador3D is locally installed and checks for structural homologs for each transcript in the PDB (Berman et al. 2000 (link)).
SPADE uses a locally installed version of the program Pfamscan (Finn et al. 2010 (link)) to identify the conservation of protein functional domains.
INERTIA detects exons with non-neutral evolutionary rates. Transcripts are aligned against related species using three different alignment methods, Kalign (Lassmann and Sonnhammer 2005 (link)), multiz (Blanchette et al. 2004 (link)), and PRANK (Loytynoja and Goldman 2005 (link)), and evolutionary rates of exons for each of the three alignments are contrasted using SLR (Massingham and Goldman 2005 (link)).
CRASH makes conservative predictions of signal peptides and mitochondrial signal sequences by using locally installed versions of the SignalP and TargetP programs (Emanuelsson et al. 2007 (link)) .
THUMP makes conservative predictions of trans-membrane helices by analyzing the output of three locally installed trans-membrane prediction methods, MemSat (Jones 2007 (link)), PRODIV (Viklund and Elofsson 2004 (link)), and PHOBIUS (Kall et al. 2004 (link)).
CExonic is a locally developed method that uses exonerate (Slater and Birney 2005 (link)) to align mouse and human transcripts and then looks for patterns of conservation in exonic structure.
CORSAIR is a locally installed method that checks for orthologs for each variant in a locally installed vertebrate protein sequence database.
Amino Acid Sequence
Biological Evolution
Evolution, Neutral
Exons
Genes
Genome, Human
Helix (Snails)
Homo sapiens
Mitochondria
Mus
Mutant Proteins
Protein Domain
Protein Isoforms
Proteins
Signal Peptides
Tissue, Membrane
Vertebrates
Error rates and the power of the tests reported in the previous sections were derived using sequence data simulated under the following protocol. We have used trees, base frequencies, branch lengths (assuming neutral evolution), and nucleotide substitution biases fitted to the two ZA RT sample from our study, with 74 sequences each to simulate 100 (200 codons in each). A neighbor joining tree (using the Tamura–Nei distance metric [67 (link)]) was reconstructed from each data replicate and used for further inference, allowing us to investigate whether the power and error rates of the tests were unduly influenced by errors in phylogenetic reconstruction. Previous studies [22 (link)] and simulations results presented here (Figure S7 ) suggest that fixed effect likelihood methods are able to infer site-specific substitution rates accurately, on average, with moderate smoothing effects for larger rates (due to a fairly small sample size). With that in mind, we set out to generate sequences under a distribution of substitution rates that is similar to those which have influenced our real samples. Having fitted the IFEL model (and thus three rates: αs,
, and
) to all four samples, we pooled each type of estimated rates into the following seven bins: [0,0.25), [0.25,0.5), [0.5,1), [1,1.5), [1.5,2.0), [2.0,4.0), and [4.0,∞), and represented each bin with its midpoint (except the final bin, which was represented by 8). For each codon, we drew αs,
, and
from the appropriate estimated rate distribution (also shown inFigure S8 ). Sampling from distributions with identical supports ensured that a sufficient proportion of sites was generated under the null distribution (e.g.,
for IFEL and
). For the evaluation of the differential selection test, we picked successive pairs of simulations (1−2, 2−3, 3−4, … , 99−100) for a total of 99 runs of the analysis.
, and
) to all four samples, we pooled each type of estimated rates into the following seven bins: [0,0.25), [0.25,0.5), [0.5,1), [1,1.5), [1.5,2.0), [2.0,4.0), and [4.0,∞), and represented each bin with its midpoint (except the final bin, which was represented by 8). For each codon, we drew αs,
, and
from the appropriate estimated rate distribution (also shown in
for IFEL and
). For the evaluation of the differential selection test, we picked successive pairs of simulations (1−2, 2−3, 3−4, … , 99−100) for a total of 99 runs of the analysis.
Codon
DNA Replication
Evolution, Neutral
Nucleotides
Trees
The Λmax statistic of Sweepfinder [75] (link) uses allele frequencies to evaluate the relative likelihood of a selective sweep versus neutral evolution. To add information regard diversity reductions, we implemented the approach of Pavlidis et al.[76] (link) to include a fraction of the invariant sites. One invariant site was added to the input for every 10 invariant sites that had <50% missing data. Likelihoods were evaluated for 1000 positions from each window. The folded allele frequency spectrum from short intron sites (see below) was used for background allele frequencies, assumed by the method to represent neutral evolution.
Local outliers for Λmax and FST were examined in overlapping windows of 100 RG non-singleton SNPs (roughly 5 kb on average). For FST, overlapping windows were offset by increments of 20 RG non-singleton SNPs, in order to identify outlier loci that could result from adaptive population differentiation. Outlier windows were defined by the upper 2.5% (FST) or 5% (Λmax) quantile for each chromosome arm. The lower threshold for FST avoids an excessive number of outliers due to the greater number of (overlapping) windows, compared to the non-overlapping windows for Λmax. Outliers with up to two non-overlapping non-outlier windows between them were considered as part of the same “outlier region”, since they might reflect a single evolutionary signal. For FST, the center of an outlier region was defined as the midpoint of its most extreme window. The nearest gene to an outlier region was calculated based on the closest exon (protein-coding or untranslated) to the above location, based on D. melanogaster genome release 5.43 coordinates obtained from Flybase.
Two FST outlier analyses were conducted. One, with the aim of identifying loci that may have contributed to the adaptive difference between African and cosmopolitan populations, focused on FST between the FR and RG population samples. The other scan was intended to search for potential adaptive differences among African populations. The nine population samples with a mean post-filtering sample size above 3.75 were included (CO, ED, GA, GU, NG, RG, UG, ZI, ZS). The mean FST from all pairwise population comparisons was evaluated for each window, and outlier regions for this overall FST were obtained. Each population was also analyzed separately, in terms of the mean FST from eight pairwise population comparisons. Here, outliers were analyzed separately for each African population, but the lists of population-specific outliers were also combined for more statistically powerful enrichment tests.
The enrichment of gene ontology (GO) categories among sets of outliers was evaluated. For each GO category, the number of unique genes that were the closest to an outlier region center (see above) was noted. A P value was then calculated, representing the probability of observing as many (or more) outlier genes from that category under the null hypothesis of a random distribution of outlier region centers across all windows. Calculating null probabilities based on windows, rather than treating each gene identically, accounts for the fact that genes vary greatly in length, and hence in the number of windows that they are associated with. P values were obtained from a permutation approach in which all outlier region center windows were randomly reassigned 10,000 times (results not shown).
Local outliers for Λmax and FST were examined in overlapping windows of 100 RG non-singleton SNPs (roughly 5 kb on average). For FST, overlapping windows were offset by increments of 20 RG non-singleton SNPs, in order to identify outlier loci that could result from adaptive population differentiation. Outlier windows were defined by the upper 2.5% (FST) or 5% (Λmax) quantile for each chromosome arm. The lower threshold for FST avoids an excessive number of outliers due to the greater number of (overlapping) windows, compared to the non-overlapping windows for Λmax. Outliers with up to two non-overlapping non-outlier windows between them were considered as part of the same “outlier region”, since they might reflect a single evolutionary signal. For FST, the center of an outlier region was defined as the midpoint of its most extreme window. The nearest gene to an outlier region was calculated based on the closest exon (protein-coding or untranslated) to the above location, based on D. melanogaster genome release 5.43 coordinates obtained from Flybase.
Two FST outlier analyses were conducted. One, with the aim of identifying loci that may have contributed to the adaptive difference between African and cosmopolitan populations, focused on FST between the FR and RG population samples. The other scan was intended to search for potential adaptive differences among African populations. The nine population samples with a mean post-filtering sample size above 3.75 were included (CO, ED, GA, GU, NG, RG, UG, ZI, ZS). The mean FST from all pairwise population comparisons was evaluated for each window, and outlier regions for this overall FST were obtained. Each population was also analyzed separately, in terms of the mean FST from eight pairwise population comparisons. Here, outliers were analyzed separately for each African population, but the lists of population-specific outliers were also combined for more statistically powerful enrichment tests.
The enrichment of gene ontology (GO) categories among sets of outliers was evaluated. For each GO category, the number of unique genes that were the closest to an outlier region center (see above) was noted. A P value was then calculated, representing the probability of observing as many (or more) outlier genes from that category under the null hypothesis of a random distribution of outlier region centers across all windows. Calculating null probabilities based on windows, rather than treating each gene identically, accounts for the fact that genes vary greatly in length, and hence in the number of windows that they are associated with. P values were obtained from a permutation approach in which all outlier region center windows were randomly reassigned 10,000 times (
Acclimatization
Biological Evolution
Chromosomes
Evolution, Neutral
Exons
Genes
Genome
Introns
Negroid Races
Proteins
Radionuclide Imaging
Single Nucleotide Polymorphism
Every codon s can be endowed with a single synonymous rate αs and two nonsynonymous rates:
(for terminal branches, or leaves) and
(for internal branches). If the latter two rates differ significantly, we deduce that evolution along internal branches (historical, e.g., influenced primarily by selection for transmission in HIV) and along terminal branches (recent, e.g., influenced by within-patient evolution in HIV) are subject to differing selective constraints. Formally,
A straightforward modification of the null hypothesis can be used to test for non-neutral evolution only along internal branches of the tree:
We refer to the latter test as IFEL (internal fixed effects likelihood). Significance is assessed by the likelihood ratio test with one degree of freedom. Our simulations (see simulation strategy details below) have shown that the use of the
asymptotic distribution leads to a conservative test, and actual false positive rates (in our simulation scenario) are lower than the nominal significance level of the test (Figure S7 ). For a given sample size, the power of the test depends on the divergence level and the disparity between levels of selection between internal and terminal branches. For example, at p = 0.05, the overall power of the test to detect non-neutral evolution is only 25%. This rather low number can be partially explained by a large proportion of codon sites with low degree of polymorphism. Such sites are nearly impossible to classify within the current phylogenetic framework. However, if we narrow our focus to strongly selected sites (i.e., sites where
) with an above average level of divergence (αs > 1), the power increases to 41%. For very strongly selected sites (K ≥ 16), the power is boosted to 68%. Overall, the PPV of the test is 98.8%.
(for terminal branches, or leaves) and
(for internal branches). If the latter two rates differ significantly, we deduce that evolution along internal branches (historical, e.g., influenced primarily by selection for transmission in HIV) and along terminal branches (recent, e.g., influenced by within-patient evolution in HIV) are subject to differing selective constraints. Formally,
A straightforward modification of the null hypothesis can be used to test for non-neutral evolution only along internal branches of the tree:
We refer to the latter test as IFEL (internal fixed effects likelihood). Significance is assessed by the likelihood ratio test with one degree of freedom. Our simulations (see simulation strategy details below) have shown that the use of the
asymptotic distribution leads to a conservative test, and actual false positive rates (in our simulation scenario) are lower than the nominal significance level of the test (
) with an above average level of divergence (αs > 1), the power increases to 41%. For very strongly selected sites (K ≥ 16), the power is boosted to 68%. Overall, the PPV of the test is 98.8%.
Biological Evolution
Codon
Evolution, Neutral
Genetic Polymorphism
Patients
Transmission, Communicable Disease
Trees
Most recents protocols related to «Evolution, Neutral»
To understand the molecular evolution at the amino acid level and the intensity of natural selection acting on metabolism in a specific clade, we used a tree based on codon alignment produced by the maximum-likelihood method using the software EasyCodeML109 (link). We retrieved Coding Sequencing (CDS) sequences from TPS-b genes from A. thaliana, E. grandis, P. cattleyanum, V. vinifera and P. trichocarpa species in Phytozome v11 (http://phytozome.jgi.doe.gov/ ; last accessed November 2020), to use in positive selection analysis. The dataset included 76 sequences and 389 amino acids from five species. We performed statistical analysis using the CodeML program in PAML version 4.9 software using the site, branch, and branch-site models110 (link), implemented in EasyCodeML109 (link).
Parameter estimates (ω) and likelihood scores111 (link) were calculated for the three pairs of models. These were M0 (one-ratio, assuming a constant ω ratio for all coding sites) vs. M3 (discrete, allowed for three discrete classes of ω within the gene), M1a (nearly neutral, allowed for two classes of ω sites: negative sites with ω0 < 1 estimated from our data and neutral sites with ω1 = 1) vs. M2a (positive selection, added a third class with ω2 possibly > 1 estimated from our data), and M7 (beta, a null model in which ω was assumed to be beta-distributed among sites) vs. M8 (beta and ω, an alternative selection model that allowed an extra category of positively selected sites)112 (link).
A series of branch models and branch site models were tested: the one-ratio model for all lineages and the two-ratio model, where the original enzyme functional evolution occurred. The branch-site model assumes that the branches in the phylogeny are divided into the foreground (the one of interest for which positive selection is expected) and background (those not expected to exhibit positive selection).
Likelihood ratio tests (LRT) were conducted to determine which model measured the statistical significance of the data. The twice the log likelihood difference between each pair of models (2ΔL) follows a chi-square distribution with the number of degrees of freedom equal to the difference in the number of free parameters, resulting in a p-value for this113 (link). A significantly higher likelihood of the alternative model compared to the null model suggests positive selection. Positive sites with high posterior probabilities (> 0.95) were obtained using empirical Bayes analysis. If ω > 1, then there is a positive selection on some branches or sites, but the positive selection sites may occur in very short episodes or on only a few sites during the evolution of duplicated genes; ω < 1 suggests a purifying selection (selective constraints), and ω = 1 indicates neutral evolution. Finally, naive empirical Bayes (NEB) approaches were used to calculate the posterior probabilities that a site comes from the site class with ω > 1112 (link). The selected sites and images of protein topology were predicted using Protter114 (link).
Parameter estimates (ω) and likelihood scores111 (link) were calculated for the three pairs of models. These were M0 (one-ratio, assuming a constant ω ratio for all coding sites) vs. M3 (discrete, allowed for three discrete classes of ω within the gene), M1a (nearly neutral, allowed for two classes of ω sites: negative sites with ω0 < 1 estimated from our data and neutral sites with ω1 = 1) vs. M2a (positive selection, added a third class with ω2 possibly > 1 estimated from our data), and M7 (beta, a null model in which ω was assumed to be beta-distributed among sites) vs. M8 (beta and ω, an alternative selection model that allowed an extra category of positively selected sites)112 (link).
A series of branch models and branch site models were tested: the one-ratio model for all lineages and the two-ratio model, where the original enzyme functional evolution occurred. The branch-site model assumes that the branches in the phylogeny are divided into the foreground (the one of interest for which positive selection is expected) and background (those not expected to exhibit positive selection).
Likelihood ratio tests (LRT) were conducted to determine which model measured the statistical significance of the data. The twice the log likelihood difference between each pair of models (2ΔL) follows a chi-square distribution with the number of degrees of freedom equal to the difference in the number of free parameters, resulting in a p-value for this113 (link). A significantly higher likelihood of the alternative model compared to the null model suggests positive selection. Positive sites with high posterior probabilities (> 0.95) were obtained using empirical Bayes analysis. If ω > 1, then there is a positive selection on some branches or sites, but the positive selection sites may occur in very short episodes or on only a few sites during the evolution of duplicated genes; ω < 1 suggests a purifying selection (selective constraints), and ω = 1 indicates neutral evolution. Finally, naive empirical Bayes (NEB) approaches were used to calculate the posterior probabilities that a site comes from the site class with ω > 1112 (link). The selected sites and images of protein topology were predicted using Protter114 (link).
Amino Acids
Biological Evolution
Codon
Enzymes
Evolution, Molecular
Evolution, Neutral
Exons
Genes
Metabolism
Natural Selection
Proteins
Trees
Protocol full text hidden due to copyright restrictions
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Evolution, Neutral
Genes
Population Group
The files used for benchmarking and the selection scan on chromsome 2 are provided by UK Biobank in BGEN format. These are converted to the VCF format with BGENIX, a tool included in the BGEN library (Band and Marchini 2018 ). We remove samples flagged as closely related by UK Biobank, as well as individuals who requested to be excluded from the dataset, leaving 405,623 out of 487,409 individuals. Input VCF files are filtered to keep only biallelic variants with minor allele frequency above .
For the analysis of the UK Biobank, we use a population-specific genetic map for British in England and Scotland (GBR) published in Spence and Song (2019) .
RAiSD was installed according to the documentation provided athttps://github.com/alachins/raisd and run with default parameters on the same architecture and with the same input files as HaploBlocks. Larger files containing more individuals require an increase of the maximum memory allowance in the RAiSD source code.
In order to assess the expected distribution of false positives in our analysis of the UK Biobank data, we simulated a dataset under neutral evolution comparable to the UK Biobank. The current autosomal effective population size of the UK Biobank has most recently been estimated to be 107 (Cai et al. 2022 , figs. 3–5), and the 405,623 individuals individuals analyzed here therefore correspond to ∼4%. As the computational resources for a simulation of that size are excessive, we instead sampled 4,000 individuals from a simulation with a population size of 105. We used the exact same approach as in the simulations for validation, with a chromosome length of 242,193,530 bp. We set the recombination rate to 7.7 × 109 to closely match the ∼187 cM of chromosome 2 (Spence and Song 2019 ). We filtered for minor allele frequency above , and sampled the resulting 11,146,258 SNPs down to 48,033 as for the UK Biobank genotype array data analyzed here in a way to match the allele frequency spectrum of the original. In addition, we generated a second version randomly sampling the SNPs down to 800,664, corresponding to the number of SNPs with minor allele frequency above 0.01 found in chromosome 2 of the UK10k data (UK10K Consortium et al. 2015 (link)), in order to emulate full sequencing data. We computed a lookup table for effective population size 105 and set the parameter of HaploBlocks accordingly for the analysis of the simulation.
Furthermore, we simulated a second dataset under a neutral nonequilibrium model, inspired by Gravel et al. (2011) (link) and intended to match the demography of the UK Biobank population more closely. Instead of a constant population size we started 5,921 generations ago with 28,948 artificial chromosomes and introduced a bottleneck 2,056 generations ago reducing the population size to only 3,722 artificial chromosomes. We introduced an exponential growth rate at 0.4247, resulting in a final population size of 105 in the present generation. Again, 4,000 individuals were sampled for the analysis and additional steps were performed as described above, including downsampling the number of SNPs matching the allele frequency spectrum of the UK Biobank data. We also analyzed the full dataset consisting of 721,189 SNPs without downsampling. Under this demographic model the geometric mean of the effective population size is 9741, which we used for the lookup table and as HaploBlocks parameter for the analysis of this simulation.
For the analysis of the UK Biobank, we use a population-specific genetic map for British in England and Scotland (GBR) published in Spence and Song (2019) .
RAiSD was installed according to the documentation provided at
In order to assess the expected distribution of false positives in our analysis of the UK Biobank data, we simulated a dataset under neutral evolution comparable to the UK Biobank. The current autosomal effective population size of the UK Biobank has most recently been estimated to be 107 (Cai et al. 2022 , figs. 3–5), and the 405,623 individuals individuals analyzed here therefore correspond to ∼4%. As the computational resources for a simulation of that size are excessive, we instead sampled 4,000 individuals from a simulation with a population size of 105. We used the exact same approach as in the simulations for validation, with a chromosome length of 242,193,530 bp. We set the recombination rate to 7.7 × 109 to closely match the ∼187 cM of chromosome 2 (Spence and Song 2019 ). We filtered for minor allele frequency above , and sampled the resulting 11,146,258 SNPs down to 48,033 as for the UK Biobank genotype array data analyzed here in a way to match the allele frequency spectrum of the original. In addition, we generated a second version randomly sampling the SNPs down to 800,664, corresponding to the number of SNPs with minor allele frequency above 0.01 found in chromosome 2 of the UK10k data (UK10K Consortium et al. 2015 (link)), in order to emulate full sequencing data. We computed a lookup table for effective population size 105 and set the parameter of HaploBlocks accordingly for the analysis of the simulation.
Furthermore, we simulated a second dataset under a neutral nonequilibrium model, inspired by Gravel et al. (2011) (link) and intended to match the demography of the UK Biobank population more closely. Instead of a constant population size we started 5,921 generations ago with 28,948 artificial chromosomes and introduced a bottleneck 2,056 generations ago reducing the population size to only 3,722 artificial chromosomes. We introduced an exponential growth rate at 0.4247, resulting in a final population size of 105 in the present generation. Again, 4,000 individuals were sampled for the analysis and additional steps were performed as described above, including downsampling the number of SNPs matching the allele frequency spectrum of the UK Biobank data. We also analyzed the full dataset consisting of 721,189 SNPs without downsampling. Under this demographic model the geometric mean of the effective population size is 9741, which we used for the lookup table and as HaploBlocks parameter for the analysis of this simulation.
Chromosomes
Chromosomes, Artificial
Chromosomes, Human, Pair 2
DNA Library
Evolution, Neutral
Figs
Genotype
Memory
Radionuclide Imaging
Recombination, Genetic
Single Nucleotide Polymorphism
The selective pressure for each gene segments in clades A, B, and C was determined on the Datamonkey online version of HyPhy package.6 The more appropriate number of sequences (n ≥ 200) we selected were depend on a study of Ji-Ming et al. (2022) . All the sequences in every branch were analyzed by ratio estimation of non-synonymous (dN) to synonymous (dS) substitutions (ω = dN/dS) on a codon-by-codon basis, and ω < 1 indicates negative or purifying selective pressure; ω = 1 implies neutral evolution; and ω > 1 shows positive selection. BioEdit and MEGA 6.0 (Tamura et al., 2013 (link)) were used for the analysis of the sequence format conversion and key amino acid changes, and MegAlign was used to analyze the sequence homologies.7 We used Weblogo to analyze the conservation of amino acids.8 The prediction of potential N-glycosylation sites was performed using the NetNGlyc server 1.0.9
Amino Acids
Codon
Evolution, Neutral
Genes
Pressure
Protein Glycosylation
Sequence Analysis
Aligning ICKs, or any cysteine rich peptide, is difficult due to nonhomologous cysteines mistakenly being aligned. Thus, the finalized consensus disulfide connectivities were used to inform the alignment of ICKs using a similar approach to Pineda et al. 2020 [6 (link)]. Rather than align everything at once and then manually adjusting misaligned cysteines followed by realignment of regions between the two adjusted cysteines, only amino acids between cysteines participating in disulfide bonds common to all ICKs in the dataset were used for the alignment. Additionally, the regions between homologous cysteines were aligned separately while using the barcode “WWYHWYYHMM” to replace flanking cysteines to prevent inner cysteines from misaligning with flanking cysteines similar to the approach by Shafee et al. 2016 [68 (link)]. The alignment was then provided as input to IQTREE v1.5.5 [58 (link)] to test the amino acid composition using a Chi-squared test. Any sequences that failed the test were removed from the alignment, and the sub-regions were realigned following the previously described procedure. The resulting alignment was reverse translated to form a coding sequence alignment using PRANK [69 ].
The same outgroup as used by Pineda et al. 2020 [6 (link)] (disulfide-directed -hairpin from the whip scorpion Mastigoproctus giganteus) was added to the protein alignment using MAFFT v7.455 [56 (link)]. The phylogenetic relationships of the ICKs in this alignment were reconstructed using IQTREE and the default settings.
Adaptive molecular evolution is typically inferred in coding sequences by comparing ratios of the rates of nonsynonymous substitution and synonymous substitution ( or ), where exceeding indicates positive selection, exceeding indicates negative selection, and approaching unity indicates neutral evolution. The HYPHY [70 ] implementation of Branch-Site Unrestricted Statistical Test (BUSTED) for Episodic Diversification was used to assess whether a gene has experienced positive selection at at least one site on at least one branch. To determine if ICKs have experienced positive selection, the codon multiple sequence alignment and phylogeny were provided as input to BUSTED using default parameters.
In ICKs, specific amino acid sites may play an important role in the structure-function (e.g., binding specificity) and adaptive evolution. To identify specific amino acid sites that have undergone pervasive positive selection, the HYPHY implementation of a Fast, Unconstrained Bayesian AppRoximation (FUBAR) was used with the codon multiple sequence alignment and phylogeny provided as input and default parameters.
There may only be specific episodes where certain amino acids receive strong bouts of positive selection. To determine if amino acid sites have undergone positive selection, the HYPHY implementation of a Mixed Effects Model of Evolution (MEME) [71 (link)] was used to determine if certain amino acid sites have undergone episodic positive selection. The codon multiple sequence alignment and phylogeny were provided as input to MEME with default parameters and the phylogeny set as the background.
To evaluate specific instances on a phylogeny where positive selection has occurred, branch-site models are typically implemented. Much like how MEME is unable to statistically specify the exact branches within a site undergoing episodic positive selection, branch-site models are only able to identify specific branches where a certain portion of sites have undergone positive selection. To accomplish this, the HYPHY implementation of adaptive Branch-Site Random Effects Likelihood (aBSREL) [72 (link)] was used with default parameters, and the codon alignments and phylogeny were provided as input.
Aside from evaluating signatures of positive selection through calculations of codon substitution rates, we also investigated the co-occurrence between amino acid positions in ICKs, which may provide useful inferences into the evolution of their structure/function. This can be achieved using the HYPHY implementation of the Bayesian Graphical Model (BGM) [73 (link)], which maps amino acid substitutions to a phylogeny and reconstructs ancestral states for a given model of codon substitution rates that is then followed up by a series of 2 × 2 contingency table analyses.
The same outgroup as used by Pineda et al. 2020 [6 (link)] (disulfide-directed -hairpin from the whip scorpion Mastigoproctus giganteus) was added to the protein alignment using MAFFT v7.455 [56 (link)]. The phylogenetic relationships of the ICKs in this alignment were reconstructed using IQTREE and the default settings.
Adaptive molecular evolution is typically inferred in coding sequences by comparing ratios of the rates of nonsynonymous substitution and synonymous substitution ( or ), where exceeding indicates positive selection, exceeding indicates negative selection, and approaching unity indicates neutral evolution. The HYPHY [70 ] implementation of Branch-Site Unrestricted Statistical Test (BUSTED) for Episodic Diversification was used to assess whether a gene has experienced positive selection at at least one site on at least one branch. To determine if ICKs have experienced positive selection, the codon multiple sequence alignment and phylogeny were provided as input to BUSTED using default parameters.
In ICKs, specific amino acid sites may play an important role in the structure-function (e.g., binding specificity) and adaptive evolution. To identify specific amino acid sites that have undergone pervasive positive selection, the HYPHY implementation of a Fast, Unconstrained Bayesian AppRoximation (FUBAR) was used with the codon multiple sequence alignment and phylogeny provided as input and default parameters.
There may only be specific episodes where certain amino acids receive strong bouts of positive selection. To determine if amino acid sites have undergone positive selection, the HYPHY implementation of a Mixed Effects Model of Evolution (MEME) [71 (link)] was used to determine if certain amino acid sites have undergone episodic positive selection. The codon multiple sequence alignment and phylogeny were provided as input to MEME with default parameters and the phylogeny set as the background.
To evaluate specific instances on a phylogeny where positive selection has occurred, branch-site models are typically implemented. Much like how MEME is unable to statistically specify the exact branches within a site undergoing episodic positive selection, branch-site models are only able to identify specific branches where a certain portion of sites have undergone positive selection. To accomplish this, the HYPHY implementation of adaptive Branch-Site Random Effects Likelihood (aBSREL) [72 (link)] was used with default parameters, and the codon alignments and phylogeny were provided as input.
Aside from evaluating signatures of positive selection through calculations of codon substitution rates, we also investigated the co-occurrence between amino acid positions in ICKs, which may provide useful inferences into the evolution of their structure/function. This can be achieved using the HYPHY implementation of the Bayesian Graphical Model (BGM) [73 (link)], which maps amino acid substitutions to a phylogeny and reconstructs ancestral states for a given model of codon substitution rates that is then followed up by a series of 2 × 2 contingency table analyses.
Acclimatization
Amino Acids
Amino Acid Substitution
Biological Evolution
Codon
Cysteine
Disulfides
Evolution, Molecular
Evolution, Neutral
Exons
Genes
ICK protein, human
Microtubule-Associated Proteins
Peptides
Proteins
Scorpions
Sequence Alignment
Top products related to «Evolution, Neutral»
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OriginPro 2017 is a data analysis and graphing software developed by OriginLab. It provides tools for data manipulation, visualization, and analysis. The core function of OriginPro 2017 is to enable users to import, organize, and analyze data, as well as create publication-quality graphs and charts.
Sourced in United States
The BigDye Terminator Kit version 3.1 is a DNA sequencing reagent kit used for the fluorescent dye-terminator cycle sequencing of DNA samples. It contains the necessary components for performing DNA sequencing reactions, including DNA polymerase, dNTPs, and fluorescently labeled dideoxynucleotides.
Sourced in Germany, United States, United Kingdom, Italy, Japan, Netherlands, China, France, Switzerland
The MinElute PCR Purification Kit is a laboratory equipment product designed for the efficient purification of PCR amplicons. It utilizes a silica-membrane-based technology to capture and purify DNA fragments from PCR reactions, allowing for the removal of primers, nucleotides, enzymes, and other impurities.
Sourced in United States, Canada
The PJET1.2/blunt cloning vector is a plasmid-based cloning vector designed for the insertion and propagation of DNA fragments. It features a blunt cloning site, allowing for the direct ligation of DNA fragments without the need for restriction enzyme digestion or the generation of compatible sticky ends. The vector backbone provides antibiotic resistance and a origin of replication, facilitating the selection and amplification of the cloned DNA in bacterial hosts.
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Phusion High-Fidelity DNA Polymerase is a thermostable DNA polymerase engineered for high-fidelity DNA amplification. It possesses 3'→5' exonuclease proofreading activity, resulting in an error rate up to 50-fold lower than Taq DNA polymerase.
Sourced in France
PhyML v3.0 is a software package for phylogenetic analysis. It is designed to estimate maximum likelihood phylogenies from DNA or protein sequence data.
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SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.
Sourced in United States
GraphPad Prism 5.0 for Windows is a data analysis and graphing software. It provides tools for a variety of data analysis tasks, including curve fitting, statistical analysis, and the creation of scientific graphs and charts.
Sourced in United States, United Kingdom
SeqScape software is a bioinformatics tool designed for analysis and review of DNA sequence data. It provides a platform for sequence alignment, variant calling, and visualization of genomic data.
More about "Evolution, Neutral"
Evolutionary biology, natural selection, genetic drift, population genetics, phylogenetics, bioinformatics, molecular evolution, OriginPro, BigDye Terminator, MinElute PCR Purification, PJET1.2/blunt, Phusion DNA Polymerase, PhyML, SAS, GraphPad Prism, SeqScape.
Explore the fascinating field of Evolution, Neutral, where the fundamental mechanisms that drive biological change are investigated.
From the role of genetic drift in shaping genetic diversity, to the interplay between natural selection and neutral evolutionary processes, this domain offers insights into the origins and adaptations of living organisms.
Leverage cutting-edge tools and techniques, such as OriginPro for data analysis, BigDye Terminator for DNA sequencing, and PhyML for phylogenetic reconstruction, to unravel the complexities of neutral evolution.
Enhance the accuracy and reproducibility of your research with the MinElute PCR Purification Kit and the PJET1.2/blunt cloning vector, while utilizing powerful software like SAS, GraphPad Prism, and SeqScape to streamline your workflow and draw meaningful conclusions.
Whether you're studying the evolutionary dynamics of populations, exploring the molecular evolution of genes, or investigating the phylogenetic relationships between species, the field of Evolution, Neutral, provides a wealth of opportunities to expand our understanding of the natural world.
Embark on your research journey with confidence, armed with the insights and tools that will propel your work forward.
Explore the fascinating field of Evolution, Neutral, where the fundamental mechanisms that drive biological change are investigated.
From the role of genetic drift in shaping genetic diversity, to the interplay between natural selection and neutral evolutionary processes, this domain offers insights into the origins and adaptations of living organisms.
Leverage cutting-edge tools and techniques, such as OriginPro for data analysis, BigDye Terminator for DNA sequencing, and PhyML for phylogenetic reconstruction, to unravel the complexities of neutral evolution.
Enhance the accuracy and reproducibility of your research with the MinElute PCR Purification Kit and the PJET1.2/blunt cloning vector, while utilizing powerful software like SAS, GraphPad Prism, and SeqScape to streamline your workflow and draw meaningful conclusions.
Whether you're studying the evolutionary dynamics of populations, exploring the molecular evolution of genes, or investigating the phylogenetic relationships between species, the field of Evolution, Neutral, provides a wealth of opportunities to expand our understanding of the natural world.
Embark on your research journey with confidence, armed with the insights and tools that will propel your work forward.