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80 results for “Mutational signature”
Genome-Wide Mutational Signatures of Aristolochic Acid and Its Application as a Screening Tool
<p>The .zip files contain the fastq files for the cell line data published in DOI: 10.1126/scitranslmed.3006086 plus results of analyzing these data. HK2_AA and HK2b-8d2 were exposed to AA (aristolochic acid I), and HK2_ctrl was an unexposed control. The .tsv files are VCF-like files with the mutations found in the exposed cells. spectrum_counts.txt are what are now called "SBS96" spectra -- counts of single base substitutions in the contexts of preceding and following bases. The PDF file has plots of these. (The HUC1 cells that were exposed did not show AA-related mutations.)</p> <p> </p>
Synthetic mutational spectra with mixed and correlated mutational signatures SBS1 and SBS5
<p>Experience suggests that it harder to extract mutational signatures that always co-occur and that generate correlated numbers of mutations. Here we provide 12 data sets, each consisting of 500 synthetic mutational spectra with varying degrees of mixture and correlation between two mutational signatures. The signatures studied were SBS1, a "clock-like" signature due to deamination of 5-methyl cytosine that consists primarily of mutations from CG > TG, and SBS5, a relatively flat "clock-like" signature. By "clock-like" we mean that the numbers of mutations attributable to these signatures increase with patients' ages. The 12 data sets varied in two dimensions: (i) average ratio of the number of SBS1 to the number of SBS5 mutations in spectra in the data set and (ii) correlation between the number of SBS1 and the number of SBS5 mutations in spectra in the data set. The mutational signatures SBS1 and SBS5 are described at https://doi.org/10.7303/syn12025148 and https://doi.org/10.1101/322859. </p>
Spectrum of mutational signatures in T-cell lymphoma reveals a key role for UV radiation in mycosis fungoides and Sezary syndrome
<p>T-cell non-Hodgkin's lymphomas (NHL) develop following transformation of tissue resident T-cells. We performed a meta-analysis of mutational catalogues derived from whole exome sequencing data from 403 patients with eight subtypes of T-cell NHL to identify mutational signatures and recurrent gene mutations associated with specific causal peaks within these signatures. Signature 1, indicative of age-related deamination, was prevalent across all T-cell NHL subtypes, reflecting the derivation of these malignancies from memory T-cell subsets. Adult T-cell leukemia-lymphoma (ATLL) was specifically associated with signature 17, which was found to strongly correlate with the IRF4 K59R mutation that is exclusive to ATLL. Signature 7, implicating UV exposure as a potential initiating factor was uniquely identified in cutaneous T-cell lymphoma, contributing 52% of the mutational burden in mycosis fungoides and 23% in Sezary syndrome. Importantly this UV signature was observed in CD4+ T-cells isolated from blood suggesting extensive re-circulation of these T-cells through both skin and blood and strongly implicating a role for UV in the pathogenesis of cutaneous T-cell lymphoma.</p>
The Cellular and Extra-Cellular Proteomic Signature of Human Dopaminergic Neurons Carrying the LRRK2 G2019S Mutation
<p>The provided data sets were generated during the work described in "The Cellular and Extra-Cellular Proteomic Signature of Human Dopaminergic Neurons Carrying the LRRK2 G2019S Mutation" published in Frontiers in Neuroscience, 2024.</p> <p>They contain the results from our differential expression analysis of our raw DIA Protemic Data as well as a list of input data for GO analyses.</p>
TCGA RNA-Seq normalized rsem data, TCGA clinical data and mutational signature profiles
<p>Normalized rsem RNA-Seq data for each of the 33 TCGA tumor types named as "<em>tumortype</em>rnaSeq.tar", a file containing TCGA clinical data ("cliDat_tcga_18.tar") and a text file with single base substitution mutational signatures for each sample from COSMIC (https://cancer.sanger.ac.uk/cosmic, “signatureProfileSample.txt.zip”).</p>
Proerythroblast cells of diamond-blackfan anemia patients with RPS19 and CECR1 mutations have similar transcriptomic signature
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Spectrum of mutational signatures in T-cell lymphoma reveals a key role for UV radiation in mycosis fungoides and Sezary syndrome
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BASCULE: Bayesian inference and clustering of mutational signatures leveraging biological priors
<p>In the preprint available at https://doi.org/10.1101/2024.09.16.613266 we present BASCULE, a new method to perform Bayesian signatures deconvolution and to cluster patients from the inferred exposures. The method can deconvolve any kind of mutational signature types (SBS, DBS, ID, etc.) including as input a reference catalogue of known signatures (i.e., COSMIC), and cluster the samples joinltly from the exposures of all signature types. BASCULE is available as an R package (https://github.com/caravagnalab/bascule.git). Here we release the data and code to reproduce the analysis on synthetic and real datasets presented in the preprint, in the "synthetic_data_validation.zip" and "real_data_validation.zip", respectively.</p>
Substitution mutational signatures in whole-genome-sequenced cancers in the UK population, Mutational Signatures Data
<p>This uploads contains the mutational signature data from the article <strong>Substitution mutational signatures in whole-genome-sequenced cancers in the UK population</strong>,<strong> </strong><em>Science</em>, doi:10.1126/science.abl9283, 2022.</p>
DNA nicks induce mutational signatures associated with BRCA1 deficiency
<p>the microscope datasets for metaphase and micronuclei analysis for cells transfected with Cas9/nCas9-gB2 </p>
Mutational Signatures identification in clinical-setting assays using neural embedding based representations
<p>This dataset includes supporting information for the manuscript titled "Mutational Signatures identification in clinical-setting assays using neural embedding based representations". </p> <p>Code and custom scripts are available in https://github.com/Adarya/MESiCA. </p>
A Dirichlet-multinomial mixed model for determining differential abundance of mutational signatures
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Genomic signatures of inbreeding and mutation load in a threatened rattlesnake
<p>Theory predicts that threatened species living in small populations will experience high levels of inbreeding that will increase their genetic load, but recent work suggests that the impact of load may be minimized by purging resulting from long-term population bottlenecks. Empirical studies that examine this idea using genome-wide estimates of inbreeding and genetic load in threatened species are limited. Here we use individual genome resequencing data to compare levels of inbreeding, levels of genetic load (estimated as mutation load), and population history in threatened Eastern massasauga rattlesnakes (Sistrurus catenatus), which exist in small isolated populations, and closely-related yet outbred Western massasauga rattlesnakes (S. tergeminus). In terms of inbreeding, S. catenatus genomes had a greater number of ROHs of varying sizes, indicating sustained inbreeding through repeated bottlenecks when compared to S. tergeminus. At the species level, outbred S. tergeminus had higher genome-wide levels of mutation load in the form of greater numbers of derived deleterious mutations compared to S. catenatus, presumably due to long-term purging of deleterious mutations in S. catenatus. In contrast, mutations that escaped species-level drift effects within S. catenatus populations were in general more frequent and more often found in homozygous genotypes than in S. tergeminus, suggesting a reduced efficiency of purifying selection in smaller S. catenatus populations for most mutations. Our results support an emerging idea that the historical demography of a threatened species has a significant impact on the type of genetic load present, which impacts implementation of conservation actions such as genetic rescue.</p>
Genomic signatures of inbreeding and mutation load in a threatened rattlesnake
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RNA signatures of DMD urinary stem cells identify antisense effect and causative mutations in undiagnosed patients
GEO Series GSE162108. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
High-resolution liver cancer genomic profiling links etiology, epigenetic and mutation signatures [ChIP-Seq]
GEO Series GSE103728. Homo sapiens. 24 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Concordance of hydrogen peroxide-induced 8-oxoguanine patterns with two cancer mutation signatures of upper GI tract tumors
GEO Series GSE184820. Homo sapiens. 8 samples. Type: Other.
Long-read single-cell RNA transcriptomics reveals a signature of PIK3CA mutation in a human capillary malformation
GEO Series GSE253153. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Spatial Environment Affects HNF4A Mutation-Specific Proteome Signatures and Cellular Morphology in hiPSC-Derived β-Like Cells
GEO Series GSE188827. Homo sapiens. 28 samples. Type: Expression profiling by high throughput sequencing.
Somatic IRF4 mutations drive disease-defining expression signatures and thymic tropism in primary mediastinal large B-cell lymphoma [RNA-seq]
GEO Series GSE294943. Homo sapiens. 50 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.