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1,666 results for “human genome”
Source code for StrVCTVRE: a supervised learning method to predict the pathogenicity of human genome structural variants
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Training data for 'Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads' tutorial (Galaxy Training Material)
<p>The data here is a copy of the corresponding SRR records in the NCBI SRA. The duplication serves a dual purpose:</p> <ol> <li>as a backup should there be problems connecting to NCBI servers, e.g., during Galaxy user trainings.</li> <li>to illustrate how to obtain raw sequencing data from alternative sources, and to organize the data into the same collection structure in a Galaxy history that is generated by specialized Galaxy SRA download tools.</li> </ol>
Index and biological spectrum of accessible DNA elements in the human genome
<p>Data associated with the manuscript titled<br> "Index and biological spectrum of accessible DNA elements in the human genome"<br> <a href="https://doi.org/10.1101/822510">https://doi.org/10.1101/822510</a></p> <p>Code repositories for these data are available here:</p> <ul> <li>https://github.com/Altius/Index</li> <li>https://github.com/Altius/Vocabulary</li> </ul> <p><br> Tab-separated file with DNase I Hypersensitive Site (DHS) coordinates,<br> including DHS summits and core regions and assignments to regulatory components.<br> A separate legend file describes the contents of each column in more detail.</p> <ul> <li>DHS_Index_and_Vocabulary_hg38_WM20190703.txt.gz</li> <li>DHS_Index_and_Vocabulary_hg19_WM20190703.txt.gz (mapped using liftOver, not ideal)</li> <li>DHS_Index_and_Vocabulary_legend.txt</li> </ul> <p> </p> <p>Metadata files describing biosample characteristics and annotations,<br> provided in HTML, PDF, TSV and Excel formats:</p> <ul> <li>DHS_Index_and_Vocabulary_metadata.html</li> <li>DHS_Index_and_Vocabulary_metadata.pdf</li> <li>DHS_Index_and_Vocabulary_metadata.tsv</li> <li>DHS_Index_and_Vocabulary_metadata.xlsx</li> </ul> <p> </p> <p>Presence/absence matrix of DHSs (rows) versus biosamples (columns),<br> provided in RData, MatrixMarket and raw formats:</p> <ul> <li>dat_bin_FDR01_hg38.RData</li> <li>dat_bin_FDR01_hg38.mtx.gz</li> <li>dat_bin_FDR01_hg38.txt.gz</li> <li>dat_bin_FDR01_hg19.RData (mapped using liftOver, not ideal)</li> <li>dat_bin_FDR01_hg19.txt.gz (mapped using liftOver, not ideal)</li> </ul> <p> </p> <p>Normalized DNase-seq signal matrix of DHSs (rows) versus biosamples (columns),<br> provided in RData and raw formats:</p> <ul> <li>dat_FDR01_hg38.RData</li> <li>dat_FDR01_hg38.txt.gz</li> </ul> <p>The order of DHSs (rows) is the same as in the DHS Index file(s) above,<br> and the order of biosamples (columns) is the same as in the metadata files.</p> <p> </p> <p>Non-negative Matrix Factorization (NMF) results, decomposing the presence/absence matrix (hg38) into 16 components:</p> <ul> <li>2018-06-08NC16_NNDSVD_Mixture.npy.gz</li> <li>2018-06-08NC16_NNDSVD_Basis.npy.gz</li> </ul> <p> </p> <p>Putative transcription factor-specific regulatory elements,<br> identified using DHS Vocabulary components, TF motif databases and biosample-specific footprinting data:</p> <ul> <li>TF_associated_DHSs_hg38.tar.gz</li> </ul>
Footprint of the host restriction factors APOBEC3 on the genome of human viruses
<p><span><span>APOBEC3 enzymes are innate immune effectors that introduce mutations into viral genomes. These enzymes are cytidine deaminases which transform cytosine into uracil. They preferentially mutate cytidine preceded by thymidine making the 5'TC motif their favored target. Viruses have evolved different strategies to evade APOBEC3 restriction. Certain viruses actively encode viral proteins antagonizing the APOBEC3s, others passively face the APOBEC3 selection pressure thanks to a depleted genome for APOBEC3-targeted motifs. Hence, the APOBEC3s left on the genome of certain viruses an evolutionary footprint.</span></span></p> <p><span><span>The aim of our study is the identification of these viruses having a genome shaped by the APOBEC3s. We analyzed the genome of 33,400 human viruses for the depletion of APOBEC3-favored motifs. We demonstrate that the APOBEC3 selection pressure impacts at least 22% of all currently annotated human viral species. The <i>papillomaviridae</i> and <i>polyomaviridae</i> are the most intensively footprinted families; evidencing a selection pressure acting genome-wide and on both strands. Members of the <i>parvoviridae</i> family are differentially targeted in term of both magnitude and localization of the footprint. Interestingly, a massive APOBEC3 footprint is present on both strands of the B19 erythroparvovirus; making this viral genome one of the most cleaned sequences for APOBEC3-favored motifs. We also identified the endemic <i>coronaviridae</i> as significantly footprinted. Interestingly, no such footprint has been detected on the zoonotic MERS-CoV, SARS-CoV-1 and SARS-CoV-2 coronaviruses. In addition to viruses that are footprinted genome-wide, certain viruses are footprinted only on very short sections of their genome. That is the case for the <i>gamma-herpesviridae</i> and <i>adenoviridae</i> where the footprint is localized on the lytic origins of replication. A mild footprint can also be detected on the negative strand of the reverse transcribing HIV-1, HIV-2, HTLV-1 and HBV viruses.</span></span></p> <p><span><span>Together, our data illustrate the extent of the APOBEC3 selection pressure on the human viruses and identify new putatively APOBEC3-targeted viruses.</span></span></p>
Eigen scores for human genome assembly GRCh37 Part 1 (Chr1 - Chr3)
<p>Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.</p>
Eigen scores for human genome assembly GRCh37 Part 4 (Chr17 - Chr22)
<p>Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.</p>
Assessing changes in genomic divergence following a century of human mediated secondary contact among wild and captive-bred ducks
<p>Along with manipulating habitat, the direct release of domesticated individuals into the wild is a practice used world-wide to augment wildlife populations. We test between possible outcomes of human-mediated secondary contact using genomic techniques at both historical and contemporary time scales for two iconic duck species. First, we sequence several thousand ddRAD-seq loci for contemporary mallards (<i>Anas platyrhynchos</i>) throughout North America, and two domestic mallard-types (i.e., known game-farm mallards and feral Khaki Campbell's). We show that North American mallards may well be becoming a hybrid swarm due to interbreeding with domesticated game-farm mallards released for hunting. Next, to attain a historical perspective, we applied a bait-capture array targeting thousands of loci in century-old (1842-1915) and contemporary (2009-2010) mallard and American black duck (<i>A. rubripes</i>) specimens. We conclude that American black ducks and mallards have always been closely related, with a divergence time of ~600,000 years before present, and likely evolved through prolonged isolation followed by limited bouts of gene flow (i.e., secondary contact). They continue to maintain genetic separation, a finding that overturns decades of prior research and speculation suggesting the genetic extinction of the American black duck due to contemporary interbreeding with mallards. Thus, despite having high rates of hybridization, actual gene flow is limited between mallards and American black ducks. Conversely, our historical and contemporary data confirm that the intensive stocking of game-farm mallards during the last ~100 years has fundamentally changed the genetic integrity of North America's wild mallard population, especially in the east. It thus becomes of great interest to ask whether the iconic North American mallard is declining in the wild due to introgression of maladaptive traits from domesticated forms. Moreover, we hypothesize that differential gene flow from domestic game-farm mallards into the wild mallard population may explain the overall temporal increase in differentiation between wild black ducks and mallards, as well as the uncoupling of genetic diversity and effective population size estimates across time in our results. Finally, our findings highlight how genomic methods can recover complex population histories by capturing DNA preserved in traditional museum specimens.</p>
de novo genome assembly of the LNCaP human prostate cancer cell line
<p>Whole-genome sequencing reads from the LNCaP human prostate cancer cell line were used to generate a <em>de novo </em>assembly with SGA v0.10.15. Please see https://github.com/sciseim/PCaWGS for associated scripts. Library preparation was performed using a TruSeq Nano DNA kit (Illumina) with a target insert size of 350bp. Paired-end libraries (150bp) were sequenced using a HiSeqX sequencer (Illumina).</p>
de novo genome assembly of the PC3 human prostate cancer cell line
<p>Whole-genome sequencing reads from the PC3 human prostate cancer cell line were used to generate a <em>de novo </em>assembly with SGA v0.10.15. Please see https://github.com/sciseim/PCaWGS for associated scripts. Library preparation was performed using a TruSeq Nano DNA kit (Illumina) with a target insert size of 350bp. Paired-end libraries (150bp) were sequenced using a HiSeqX sequencer (Illumina).</p> <p> </p> <p> </p> <p> </p>
Mappability of the mouse and human genomes and methylomes with Umap and Bismap
<p>This dataset consists of single-read mappability (Bed files) and multi-read mappability (Wiggle files) of human and mouse genomes and methylomes (bisulfite-converted genome). We provide mappability information for the two most recent assemblies of each organism, and for four different read lengths (24 bp, 36 bp, 50 bp, and 100 bp).</p> <p> </p>
Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation
<p>Summary statistics for "Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation" </p><p>Files are organized in the following directory structure:</p><p><strong>G2G/</strong> - Summary statistics of G2G associations (SNPs, HLA, and gene-level analysis). </p><p><strong>HLA/ </strong>- Peptide binding prediction results</p><p><strong>preS1_haplotypes/ - </strong>Resolved intra-host haplotypes of the preS1 binding region. </p><p><strong>DnDs/</strong> - Calculation of intra-host positive selection, within the preS1 binding region. </p><p> </p>
VMGC - Human Vaginal Microbiome Genome Collection
<p>The VMGC is a large-scale reference genome resource of the human vaginal microbiome, including over 33,000 genomes derived from 786 prokaryotes, 11 fungi, and 4,263 viruses associated with the human vagina. In terms of representation, the VMGC demonstrates high efficiency in capturing microbial sequences, with a median mapping rate of 91.7% across 4,472 vaginal metagenomic samples obtained from 14 countries.</p>
A genome catalogue of mercury-methylating bacteria and archaea from sediments of a boreal river facing human disturbances
<p>Methyl mercury is a toxic compound produced by anaerobic microbes that biomagnifies in aquatic food webs, impacting animal and human health. Genome-based explorations of Hg methylators remain limited, particularly in the context of river ecosystems. To fill this knowledge gap, we created a genome catalogue of putative Hg-methylating microorganisms (based on the presence of <em>hgcAB</em>) from the sediments of a river impacted by two run-of-river hydroelectric dams, logging, and a wildfire. By using genome-resolved metagenomics, we uncovered a unique and diverse assemblage of Hg methylators dominated by members of the metabolically versatile Bacteroidota and particularly enriched in butyrate fermentative microbes. By comparing diversity and abundance of Hg methylators between sites that were subjected to different disturbances, we found that ongoing disturbances, such as input of organic matter related to logging activities. were particularly favorable to the establishment of a Hg-methylating niche. Lastly, for a deeper understanding of the environmental factors shaping Hg methylator diversity, we juxtaposed the Hg-methylating genome catalogue with the wider microbial community. The results suggest that Hg methylators respond to environmental conditions similarly to overall microbial community, and therefore it is crucial to interpret the diversity and abundance of Hg methylators within their specific ecological context.</p>
Genomic epidemiology of Escherichia coli: antimicrobial resistance through a One Health lens in sympatric humans, livestock and peri-domestic wildlife in Nairobi, Kenya
<p><strong><span>Background</span></strong></p> <p><span>Livestock systems have been proposed as a reservoir for antimicrobial-resistant (AMR) bacteria and AMR genetic determinants that may infect or colonise humans, yet quantitative evidence regarding their epidemiological role remains lacking. Here we used a combination of genomics, epidemiology and ecology to investigate patterns of AMR gene carriage in <em>Escherichia</em> <em>coli</em>, regarded as a sentinel organism.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>We conducted a structured epidemiological survey of 99 households across Nairobi, Kenya, and whole genome sequenced <em>E</em>. <em>coli</em> isolates from 311 human, 606 livestock, and 399 wildlife faecal samples. We used statistical models to investigate the prevalence of AMR carriage and characterise AMR gene diversity and structure of AMR genes in different host populations across the city. We also investigated house-hold level risk factors for exchange of AMR genes between sympatric humans and livestock.</span></p> <p><strong><span>Findings</span></strong></p> <p><span>We detected 56 unique acquired genes along with 13 point mutations present in variable proportions in human and animal isolates, known to confer resistance to nine antibiotic classes. We find that AMR gene community composition is not associated with host species, but AMR genes were frequently co-located, potentially enabling the acquisition and dispersal of multi-drug resistance in a single step. We find that whilst keeping livestock had no influence on human AMR gene carriage, the potential for AMR transmission across human-livestock interfaces is greatest when manure is poorly disposed of and in larger households.</span></p> <p><strong><span>Conclusions</span></strong></p> <p><span>Findings of widespread carriage of AMR bacteria in human and animal populations, including in long-distance wildlife species, in community settings, highlight the value of evidence-based surveillance to address antimicrobial resistance on a global scale. Our genomic analysis provided in-depth understanding of AMR determinants at the interfaces of One-Health sectors that will inform AMR prevention and control.</span></p>
Data and code to reproduce analyses in Heinken et al, "A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites"
<p>This datasets archives the GitHub version found at https://github.com/ThieleLab/CodeBase to reproduce simulations for the article Heinken et al, "A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites", Cell Systems, in press.</p>
MACIE scores for human genome assembly GRCh37 Part 1 (Chr1 - Chr3)
<p>MACIE (Multi-dimensional Annotation Class Integrative Estimation) is an unsupervised multivariate mixed model framework to assess multi-dimensional functional impacts for both coding and non-coding variants in the human genome. MACIE integrates a variety of functional annotations, including protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics, and estimates the joint posterior probabilities of each genetic variant being functional.</p> <p>For each non-synonymous coding variant, the MACIE score is a vector of length 4, representing the estimated joint posterior probabilities of “not damaging protein functional and evolutionarily conserved” (MACIE01); “damaging protein functional and not evolutionarily conserved” (MACIE10); “not damaging protein functional and not evolutionarily conserved” (MACIE00); “both damaging protein functional and evolutionarily conserved” (MACIE11). MACIE_protein is the estimated posterior probability of “damaging protein functional”, which is the sum of MACIE10 and MACIE11; MACIE_conserved is the estimated posterior probability of “evolutionarily conserved”, which is the sum of MACIE01 and MACIE11; MACIE_anyclass is the estimated posterior probability of “damaging protein functional” or “evolutionarily conserved”, which is the sum of MACIE01, MACIE10, and MACIE11.</p> <p>For each non-coding and synonymous coding variant, the MACIE score is a vector of length 4, representing the estimated joint posterior probabilities of “not evolutionarily conserved and regulatory functional” (MACIE01); “evolutionarily conserved and not regulatory functional” (MACIE10); “not evolutionarily conserved and not regulatory functional” (MACIE00); “both evolutionarily conserved and regulatory functional (MACIE11). MACIE_conserved is the estimated posterior probability of “evolutionarily conserved”, which is the sum of MACIE10 and MACIE11; MACIE_regulatory is the estimated posterior probability of “regulatory functional”, which is the sum of MACIE01 and MACIE11; MACIE_anyclass is the estimated posterior probability of “evolutionarily conserved” or “regulatory functional”, which is the sum of MACIE01, MACIE10, and MACIE11.</p>
MACIE scores for human genome assembly GRCh37 Part 4 (Chr14 - Chr22)
<p>MACIE (Multi-dimensional Annotation Class Integrative Estimation) is an unsupervised multivariate mixed model framework to assess multi-dimensional functional impacts for both coding and non-coding variants in the human genome. MACIE integrates a variety of functional annotations, including protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics, and estimates the joint posterior probabilities of each genetic variant being functional.</p> <p>For each non-coding and synonymous coding variant, the MACIE score is a vector of length 4, representing the estimated joint posterior probabilities of “not evolutionarily conserved and regulatory functional” (MACIE01); “evolutionarily conserved and not regulatory functional” (MACIE10); “not evolutionarily conserved and not regulatory functional” (MACIE00); “both evolutionarily conserved and regulatory functional (MACIE11). MACIE_conserved is the estimated posterior probability of “evolutionarily conserved”, which is the sum of MACIE10 and MACIE11; MACIE_regulatory is the estimated posterior probability of “regulatory functional”, which is the sum of MACIE01 and MACIE11; MACIE_anyclass is the estimated posterior probability of “evolutionarily conserved” or “regulatory functional”, which is the sum of MACIE01, MACIE10, and MACIE11.</p>
MACIE scores for human genome assembly GRCh37 Part 3 (Chr8 - Chr13)
<p>MACIE (Multi-dimensional Annotation Class Integrative Estimation) is an unsupervised multivariate mixed model framework to assess multi-dimensional functional impacts for both coding and non-coding variants in the human genome. MACIE integrates a variety of functional annotations, including protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics, and estimates the joint posterior probabilities of each genetic variant being functional.</p> <p>For each non-coding and synonymous coding variant, the MACIE score is a vector of length 4, representing the estimated joint posterior probabilities of “not evolutionarily conserved and regulatory functional” (MACIE01); “evolutionarily conserved and not regulatory functional” (MACIE10); “not evolutionarily conserved and not regulatory functional” (MACIE00); “both evolutionarily conserved and regulatory functional (MACIE11). MACIE_conserved is the estimated posterior probability of “evolutionarily conserved”, which is the sum of MACIE10 and MACIE11; MACIE_regulatory is the estimated posterior probability of “regulatory functional”, which is the sum of MACIE01 and MACIE11; MACIE_anyclass is the estimated posterior probability of “evolutionarily conserved” or “regulatory functional”, which is the sum of MACIE01, MACIE10, and MACIE11.</p>
HiFi Metagenomic Sequencing Enables Assembly of Accurate and Complete Genomes from Human Gut Microbiota.
<p>We reported 102 complete metagenome assembled genomes (cMAGs) from five human fecal HiFi sequencing samples.</p> <p>102_cMAGs_fna.tar.gz: Fasta sequence files of 102 cMAGs.</p> <p>gc_skew_figures.tar.gz: GC-skew pattern figures of 102 cMAGs. (SVG format)</p> <p>coverage_plots.tar.gz: Genome coverage plot of 102 cMAGs.</p>
Genomic, transcriptomic and proteomic comparison of MRSA CC398 isolates collected from human and wild animal samples (Genome assembly and annotation dataset)
<p>This dataset includes the assembled contigs (.fasta and .gbk files), the nucleotide sequences of the prediction transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA) (.ffn files) and the respective amino acid sequences of the translated CDS sequences (.faa files) for the following methicillin-resistant <em>Staphylococcus aureus</em> (MRSA) strains: MRSA CC398 isolates recovered from humans, namely C5621 and C9017, and from a wild boar, namely OR418.</p> <p>All raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB35102).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.