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119 results for “mutation rate”
Bedrock radioactivity influences the rate and spectrum of mutation - Orthologous genes
<p>Alignments of the 2490 orthologous genes used in the article "Natural Bedrock radioactivity influences the rate and spectrum of mutation" to estimate the mutational spectrum and synonymous substitution rate.</p> <p>To compute accurate synonymous substitution rate, we removed genes with short sequences (<half of the alignment) and genes strongly supporting another phylogeny using ProfileNJ <a href="https://paperpile.com/c/Klqlpb/W5sS">(Noutahi et al. 2016)</a> with a bootstrap threshold of 90%, resulting in a subset of 769 genes listed in the file "List_769_1-to-1_orthologs_EvolutionRate.txt".</p> <p>Transcriptome paired-end reads used to define these orthologous genes have been deposited to the European Nucleotide Archive and are available under the study ID PRJEB14193.</p> <p>Sequences were aligned with Prank<a href="https://paperpile.com/c/Klqlpb/pilh"> (Löytynoja & Goldman 2008)</a> using a codon model and sites ambiguously aligned were removed with Gblocks <a href="https://paperpile.com/c/Klqlpb/c5kb">(Castresana 2000)</a>.</p>
Dataset for Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)
<p>This file contains the R and C++ code used for simulating experiments, simulated fluctuation data, and the results of estimations used in the paper "Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)".</p>
gnomAD polymorphism and de novo mutation data for analysis of mutation rates in highly mutable gene classes
<p>We analyze the human mutation rate in three gene classes (IGK, RNU, and tRNA) which deviate from the expectations of a mutation rate model. We examine the distribution of allele frequencies for SNVs within these genes and we analyze the counts of de novo mutations stratified by whether the SNV was observed or not. </p> <p>{CHR}_IGK_SFS_v2_denovo.gz: allele frequencies, mutation rate estimates, and whether the de novo mutation was observed for IGK, RNU, and tRNA genes. Based on gnomAD v3. </p> <p>{CHR}_indiv_mu.csv: quality information for variants in these gene classes from the 1kg subset of gnomAD.</p> <p>"CHR", "POS", "REF", "ALT", "FILTER", "AC", "AN", "MQRankSum", "pab_max", "VQSLOD", "AB", "PN", "MR", "AR", "MG", "MC", "QUAL"</p> <p>all_variants_chr21_mu_h.csv.gz: all variants from chromosome 21 to use for comparing allele frequencies to those in our gene classes.</p> <p>21_indiv_mu_all.csv.gz: quality information from all variants on chromosome 21 from the 1kg subset of gnomAD to use for comparison with gene classes.</p> <p>"CHR", "POS", "REF", "ALT", "FILTER", "AC", "AN", "MQRankSum", "pab_max", "VQSLOD", "AB", "PN", "MR", "AR", "MG", "MC", "QUAL"</p> <p> </p>
Data for: Regularized sequence-context mutational trees capture variation in mutation rates across the human genome
<p>Additional data on output models from Bayer as reported in:</p> <p>Regularized sequence-context mutational trees capture variation in mutation rates across the human genome</p> <p>Adams CJ, Conery M, Auerbach BJ, Jensen ST, Mathieson I, Voight BF. BioRxiv https://doi.org/10.1101/2022.10.14.512160</p> <p>Accepted, PLoS Genetics. </p> <p>Code Available at: https://github.com/bvoightlab/Baymer</p>
Genome streamlining: effect of mutation rate and population size on genome size reduction: simulated data
<p>Lineages data of populations simulated with Aevol (<a href="https://gitlab.inria.fr/aevol/aevol">https://gitlab.inria.fr/aevol/aevol</a>), and the Wild-Types sequences used for that.</p> <p>Conditions: change of mutation rate, population size, or both.<br>Mutational bias: none, insertion bias or deletion bias</p>
Epigenetic modifications modify the rate of spontaneous mutations in a pathogenic fungus
<p>Mutations are the source of genetic variation and the substrate for evolution. Genome-wide mutation rates appear to be affected by selection and are probably adaptive. Mutation rates are also known to vary along genomes, possibly in response to epigenetic modifications, but causality is only assumed. In this study we determine the direct impact of epigenetic modifications and temperature stress on mitotic mutation rates in a fungal pathogen using a mutation accumulation approach. Deletion mutants lacking epigenetic modifications confirm that histone mark H3K27me3 increases whereas H3K9me3 decreases the mutation rate. Furthermore, cytosine methylation in transposable elements (TE) increases the mutation rate 15‑fold resulting in significantly less TE mobilization. Also accessory chromosomes have significantly higher mutation rates. Finally, we find that temperature stress substantially elevates the mutation rate. Taken together, we find that epigenetic modifications and environmental conditions modify the rate and the location of spontaneous mutations in the genome and alter its evolutionary trajectory.</p>
Study to Investigate the Objective Response Rate of Dabrafenib in Combination With Trametinib in Subjects With BRAF V600 Mutation-Positive Melanoma
ClinicalTrials.gov study NCT02083354. IPD Sharing: UNDECIDED. Countries: 5. Publications: 2.
Response Rates to Anti–PD-1 Immunotherapy in Microsatellite-Stable Solid Tumors With 10 or More Mutations per Megabase
<p>This dataset has been used to analyze the association between tumor mutational burden and response to treatment in immunotherapy-treated patients with microsatellite-stable solid tumors. The dataset contains clinical and genomic data for 1,678 patients with 16 cancer types.</p> <p> </p>
Data used to produce figures in "Monotonicity of Fitness Landscapes and Mutation Rate Control"
<p>Data used in Figures 2, 3, 4, 6, 7, 8, 9 and 10 of the manuscript "Monotonicity of Fitness Landscapes and Mutation Rate Control"</p>
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Rate-enhancing PETase mutations determined through DFT/MM molecular dynamics simulations†
<p>Raw data for classical MD simulations ran with Gromacs 2018.3 for the two mutants Asp83Asn and Asp89Asn.</p><p>Raw data for quantum mechanics/molecular mechanics simulations ran with CP2K 6.1 for the two mutants Asp83Asn and Asp89Asn.</p><p>Distance and free energy analysis from the QM/MM MD simulations for the wild-type and the two mutants Asp83Asn and Asp89Asn.</p>
Data required for "Low mutation rate of spontaneous mutants enables detection of causative genes by comparing whole genome sequences"
<p>In the early 1900s,mutation breeding to select varieties with desirable traits using spontaneous mutation was actively conducted around the world, including Japan. In rice, the number of fixed mutations per generation was estimated to be 1.38-2.25. Although this low mutation rate was a major problem for breeding in those days, in the modern era with the development of NGS technology, it was conversely considered to be an advantage for efficient gene identification. In this paper, we proposed an in silico approach using next-generation sequencing (NGS) to compare the whole genome sequence of a spontaneous mutant with that of a closely related strain with a nearly identical genome, to find polymorphisms that differ between them, and to identify the causal gene by predicting the functional variation of the gene caused by the polymorphism. Using this approach, we found four causal genes for the dwarf mutation, the round shape grain mutation and the awnless mutation. Three of these genes were the same as those previously reported, but one was a novel gene involved in awn formation. The novel gene was isolated from Bozu-Aikoku, a mutant of Aikoku with the awnless trait, in which nine polymorphisms were predicted to alter gene function by their whole-genome comparison. Based on the information on gene function and tissue-specific expression patterns of these candidate genes, Os03g0115700/LOC_Os03g02460, annotated as a shortchain dehydrogenase/reductase SDR family protein, is most likely to be involved in the awnless mutation. Indeed, complementation tests by transformation showed that it is involved in awn formation. Thus, this method is an effective way to accelerate genome breeding of various crop species by enabling the identification of useful genes that can be used for crop breeding with minimal effort for NGS analysis.</p>
Data from: Population size mediates the contribution of high-rate and large-benefit mutations to parallel evolution
<p>The study "Population size mediates the contribution of high-rate and large-benefit mutations to parallel evolution" by Schenk et al. explores the phenotypic and genotypic changes in <em>Escherichia coli </em>after 500 generations of laboratory adaptation to increasing concentrations of an antibiotic (CTX). The source data files and scripts pertaining to the figures in the main manuscript and the extended data are available on the publishers webiste. Here we provide the source data files and scripts pertaining to the supplementary materials, organized according the figures in the supplementary material. Data are provided for Figures S2-S4, S6-S8, and S10-S12.</p>
The effect of presence and absence of DNA repair genes on the rate and pattern of mutation in bacteria
<p>This repository contains the code, data, and outputs used in the manuscript "The effect of presence and absence of DNA repair genes on the rate and pattern of mutation in bacteria".</p> <ul> <li><strong>Script.py:</strong> This is a Python script for estimating the number of polymorphisms and mutation rates of bacterial clusters of orthologous genes found in ATGC_data.zip. It produces individual strain outputs to<em> Polymorphism_Results/</em> and<em> Rate_Results/ </em>directories.</li> <li><strong>ATGC_data.zip: </strong>This is a data set of bacterial clusters of orthologous genes downloaded from the ATGC database by Kristensen et al. (2017). The fasta files are utilised by <em>Script.py</em> and should be extracted to the same working directory, under <em>ATGC_data/.</em></li> <li><strong>mutation_rate_data.csv: </strong>This file contains the outputs of <em>Script.py </em>merged into a single file, alongside additional information regarding the presence and absence of particular repair genes, and the calculation of overall mutation rate and Watterson's corrected mutation rate.</li> <li><strong>Phylogenetic_Comparisons.xlsx:</strong> This file contains the averages of overall mutation rate, transition/transversion ratio, and GC-AT bias, for 50 phylogenetic comparisons under presence and absence of repair enzymes. </li> </ul>
Data and code of "Germline mutation rate predicts cancer mortality across 37 vertebrate species" article
<p>Data and code used in this article.</p> <p>The "Data.csv" file contains information about each species class, common name, average yearly mutation rate, trophic level, number of animals per species that died from cancer, records of mortality per species, the minimum confidence interval (CI) for the cancer mortality values, the maximum CI for the cancer mortality values, the standard error (SE) of the records of mortality per species, cancer mortality, average parental age (measured in months), maximum lifespan (measured in months), average parental age (measured in months), and whether a species is reported as domesticated/semidomesticated or not. The way the authors obtained these data, and the original sources of these data, are described in the methodology section of the article.</p> <p>The "Regression_analyses.R" file is the code we used in the regression analyses conducted in this article. The .phy file is the phylogenetic tree used in the regression analyses.</p> <p>The .txt, .nwk, .Rproj, and "germFit.R" files are the data and code we used for comparing the different models of phenotype evolution (comparison of 3 different models: Ornstein–Uhlenbeck, Brownian, and Early Burst).</p>
Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria
<p>Code and data for Frenoy & Bonhoeffer 2018 (Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria, in PLoS Biology)</p> <p>See README for details and steps to reproduce the full analysis from the raw data</p>
The genetic basis of mutation rate variation in yeast [data and script]
<p>This dataset contains the data and the scripts for the study of exploring the genetic basis of mutation rate in yeast (https://www.biorxiv.org/content/early/2018/06/04/338723). </p> <ul> <li>The genotype folder contains the genotype for 1040 segreants, as well as the genotype for the 843 segregants used for QTL mapping.</li> <li>The mutation_spectrum contains the codes for MiSeq data processing, mutation variants calling, mutation spectrum detection and plotting. </li> <li>The phenotype folder contains the mutations per culture (parameter m) for 843 segreants, as well as the number of cells per culture (parameter N) for the corresponding segregants.</li> <li>The qtl_mapping folder contains the codes for QTL mapping and the codes to plot the correlation between mutation rate and mutagen resistance.</li> </ul>
Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD
<p>Set of data and scripts for the analysis of RAMD dissociation trajectories generated for BN-NS and BT/BCT-BPTI mutants, reported in the manuscript:</p> <p><br>"Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD"</p> <p>by Giulia D'Arrigo, Daria B. Kokh, Ariane Nunes-Alves and Rebecca C. Wade</p> <p> </p> <ol> <li>RAMD movies of WT Bn-Bs and D35Abn mutant dissociation pathway (WT-diss.mp4 and D35Abn-diss.mp4)</li> <li>Protein-Protein.zip contains:</li> </ol> <ul> <li>README - set of instructions to go through files and for the use of the scripts</li> <li>Jupyter notebooks: tauRAMD_PP_Residence_time.ipynb and tauRAMD_PP_Unbinding_pathways.ipynb</li> <li>Scripts used for analysis and postprocessing RAMD simulations - for the correct usage of the scripts check the corresponding file header</li> <li>System input files in BN-BS/ and BT_BCT-BPTI/</li> <li>Experimental data used</li> <li>2 RAMD movies (.mp4) showing dissociation of WT Bn-Bs and D35Abn mutant</li> </ul>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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