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1,574 results for “genome sequence”
Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project
SV analysis of the long-read sequencing data of 1,019 samples from the 1000 Genomes Project. The data is hosted at the International Genome Sample Resource (IGSR) in the <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/">1KG_ONT_VIENNA</a> directory. Please see the <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/README_1KG_ONT_VIENNA.md">README</a> and <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/README_1KG_ONT_VIENNA_datareuse_statement.md">data reuse statement</a> for further information about this dataset.
Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes
<p>We use computational models based on Direct Coupling Analysis - DCA - trained on PFAM domains of distant distant homologues to accurately predict the polymorphisms segregating in a panel of 61,157 <em>Escherichia coli </em>genomes.</p> <p>We show that the genetic context (<em>i.e. </em>the rest of the protein sequence) strongly constrains the tolerable amino acids in 30% to 50% of amino-acid sites. Our study also suggests the gradual build-up of genetic context over long evolutionary timescales by the accumulation of small epistatic contributions.</p> <p>Please refer to the README file for additional information on the structure of this dataset.</p> <p>Code to analyse this dataset is available at https://github.com/GiancarloCroce/DCA_polymorphism_Ecoli.</p> <p> </p>
Data supporting "Transformer Model Generated Bacteriophage Genomes are Compositionally Distinct from Natural Sequences"
<p>Sequence and composition data supporting doi: <a href="https://doi.org/10.1101/2024.03.19.585716" target="_blank" rel="noopener">10.1101/2024.03.19.585716</a>. Uncompressed file size is ~5.8GB.</p> <p>Data in zip files is organized by sequence provenance (generRNA, natural, or transformer (megaDNA)). Common file types between folders include:</p> <ul> <li>Multi-record fasta file: Sequence data for all sequences of a given provenance. For generRNA sequences, these are found within the `seq` column of file "MFE_distribution_Fig4a.csv"</li> <li>Composition files: Individual sequence level compositional metrics for sliding 120 bp windows. Only structural metrics were used in this study.</li> <li>Genomad: Results from the genomad pipeline (https://portal.nersc.gov/genomad/)</li> <li>Stats: Aggregate statistics for all sequences of a given provenance.</li> </ul> <p>The natural folder also has a metadata file detailing the taxonomy for all natural sequences.<br><br>Figure datasets are the cleaned (sometimes aggregated) datasets that underly specific figures in the manuscript. The figure designations are based on the order in: https://www.biorxiv.org/content/10.1101/2024.03.19.585716v1.</p>
Variant dataset and code for "Population-level whole genome sequencing of Ascochyta rabiei identifies genomic loci associated with isolate aggressiveness"
<p>This dataset contains genetic variants (SNPs) of <em>Ascochyta rabiei</em> isolates and the R code used in their analysis to generate the results and figures described in the manuscript "<strong>Population-level whole genome sequencing of <em>Ascochyta rabiei</em> identifies genomic loci associated with isolate aggressiveness</strong>".</p> <div> <div> </div> </div>
A Simulated Heterozygous Diploid Genome for Third-gen Sequencing, Assembly, and Curation
<p>A simulated heterozygous diploid genome based on <em>Saccharomyces</em> <em>cerevisiae</em>, and <em>S. paradoxus</em> homologous chromosomes.</p> <p>Simulated PacBio subreads were generated from both parent haplomes and mixed together. A phased assembly was produced using FALCON assembler and FALCON Unzip (doi:10.1038/nmeth.4035). This dataset and assembly were then used to validate the Purge Haplotigs pipeline (https://bitbucket.org/mroachawri/purge_haplotigs). See workflow.sh for commands, comments and file descriptions.</p>
Generation of transcriptional novelty by transposable element insertions in Arabidopsis, Genome Sequencing and eccDNA Data
<p><strong>Raw Illumina sequencing data from the Manuscript entitled "Generation of transcriptional novelty by transposable element insertions in Arabidopsis"</strong></p> <p><strong>A. Illumina genome sequencing reads of Arabidopsis control and hcLines that contain novel transposable element insertions.</strong></p> <p>To identify the genomic position of the new <em>ONSEN</em> insertions, the extracted DNA of the 11 selected lines (nine lines with new insertions and two control lines) was sent to BGI, Hong-Kong for Illumina paired-end 150 bp sequencing, aiming for a minimum of 20X sequencing coverage. Quality control of the raw reads was done using FastQC (Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc">http://www.bioinformatics.babraham.ac.uk/projects/fastqc</a>) and trimming/clipping was done using Trimmomatic with parameters ILLUMINACLIP: TruSeq3:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 and MINLEN:36. Quality of the reads was deemed excellent and no further actions were taken.</p> <p>Samples identifications: genome_hcLineX with "_1" indicating the forward and "_2" the reverse reads.</p> <p><strong>B. Illumina eccDNA sequencing of Arabidopsis control and hcLines following stress treatments</strong></p> <p>Extrachromosomal circular DNA was prepared and sequenced as follows: twenty plants from each petri dish were pooled separately and DNA was extracted using the CTAB method (<a href="https://dx.doi.org/10.17504/protocols.io.quidwue">dx.doi.org/10.17504/protocols.io.quidwue</a>). Following the mobilome-seq method described in (Lanciano et al., 2017), for all samples, we digested linear DNA from 2 µg of total DNA for 17 hours at 37<sup>o</sup>C using 10 U of PlasmidSafe (<em>LubioScience cat# E3101K</em>), followed by enzyme denaturation (30 mins at 70<sup>o</sup>C). Digested DNA was precipitated with isopropanol supplemented with 1 µg of GlycoBlue coprecipitant (<em>Fisher Scientific cat# 10391565</em>). Circular DNA was then amplified through rolling circle amplification (RCA) with the Illustra TempliPhi kit (<em>GE Healthcare cat# 25-6400-10</em>), following the manufacturer recommendation and leaving the reaction for 16h at 30<sup>o</sup>C. DNA was once again precipitated with isopropanol and sent for Illumina paired end 150 bp sequencing at BGI, Hong Kong. </p> <p>Samples identification: </p> <p>eccDNA_A.thaliana_ctrl: control reads</p> <p>eccDNA_A.thaliana_HS: heat stressed plants reads</p> <p>eccDNA_A.thaliana_AZ_HS: reads of alpha-amanitin, zebularine and heat-stressed plants</p> <p>"R1" indicates forward and "R2" reverse reads.</p> <p> </p>
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects. Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1, dated using <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with nodes contain the mean and variance of tsdate's posterior distribution on node time. To access these values, we can use:</p> <pre><code>import json node = ts.node(10000) metadata_dict = json.loads(node.metadata) print("The mean of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["mn"])) print("The variance of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["vr"]))</code></pre> <p>Age estimates for each variant site can be derived from the mean of the age estimates of the upper and lower bounding nodes of the oldest mutation associated with a site. tsdate includes <a href="https://tsdate.readthedocs.io/en/latest/python-api.html?highlight=sites_time_from_ts#tsdate.sites_time_from_ts">a function to find the age estimates of all sites in the tree sequence</a>:</p> <pre><code>import tsdate site_times = tsdate.sites_time_from_ts(ts, node_selection='arithmetic')</code></pre> <p>This returns a numpy array which has a length equal to the number of sites.</p> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p>
Whole-genome capture and sequencing of Mycobacterium tuberculosis directly from clinical samples - Design of RNA oligonucleotide baits for Agilent Technologies' SureSelect target enrichment
<p>This dataset comprises the sequence of <strong>44 278 RNA oligonucleotide "baits" (120 bp each) </strong>designed to perform <strong>whole-genome capture and sequencing of <em>Mycobacterium tuberculosis</em> directly from clinical samples</strong> (DNA) using Agilent Technologies’ SureSelect target enrichment system following the Illumina paired-end multiplexed sequencing library protocol. </p> <p>RNA oligonucleotide “baits” were designed to span the ∼4.5 Mb of the <em>M. tuberculosis</em> genome. In brief, the reference genome sequence of the MTBC H37Rv strain (Genbank #AL123456) was <em>in silico</em> fragmented into 120 bp sequences twice, to ensure an overlap of 60 bp between sequences. Due to their rich GC content, which could interfere with DNA capture, all MTBC genes of the PE, PPE and PE-PGRS family were also independently fragmented into 120 bp sequences, in order to increase capture sensitivity. All resulting sequences were BLASTn searched against the Human Genomic + Transcript database to excluded homologous sequences to the human genome. Overall, a total of 42,278 RNA probes were generated and this custom bait library was then uploaded to the SureDesign software (https://earray.chem.agilent.com/suredesign) and synthesized by Agilent Technologies. During synthesis, the 2198 sequences complementary to the PE, PPE and PE-PGRS family were unbalanced 8:1 to potentiate capture.</p> <p>More details can be found in the following publication:</p> <p>- Macedo, R., Isidro, J., Ferreira, R., Pinto, M., Borges, V., Duarte, S., Vieira, L., & Gomes, J. P. (2023). Molecular Capture of <em>Mycobacterium tuberculosis</em> Genomes Directly from Clinical Samples: A Potential Backup Approach for Epidemiological and Drug Susceptibility Inferences. <em>International journal of molecular sciences</em>, <em>24</em>(3), 2912. https://doi.org/10.3390/ijms24032912</p>
Mitochondrial genome sequencing and analysis of the invasive Microstegium vimineum: a resource for systematics, invasion history, and management
<p>Table S1: Accession data for Microstegium samples included in this study.</p> <p>File S1: Alignment of Mitochondrial CDS for Poales mitochondrial sequences.</p> <p>File S2: SNP data for Microstegium vimineum mitochondrial variants.</p> <p>Figure S1: Transposable element content in the Microstegium vimineum mitogenome.</p> <p>Figure S2: Summary of Kraken2 output.</p> <p> </p>
Supplementary dataset to publication: "Genomic insight into Campylobacter jejuni isolated from commercial turkey flocks in Germany using whole-genome sequencing analysis"
<p><em>Campylobacter jejuni </em>is a zoonotic bacterium of public health significance. The present investigation was designed to assess the epidemiology and genetic heterogeneity of <em>Campylobacter jejuni</em> recovered from commercial turkey farms in Germany using whole-genome sequencing. The Illumina MiSeq<sup>®</sup> technology was used to sequence 66 <em>Campylobacter jejuni </em>isolates obtained between 2010 and 2011 from commercial meat turkey flocks located in ten German federal states. Phenotypic antimicrobial resistance was determined. Phylogeny, resistome, plasmidome and virulome profiles were analyzed using whole-genome sequencing data. Genetic resistancemarkers were identified with bioinformatics tools (AMRFinder, ResFinder, NCBI and ABRicate) and compared with the phenotypic antimicrobial resistance.</p>
Lepidoptera genomics based on 88 chromosomal reference sequences informs population genetic parameters for conservation
<p>This repository contains (1) germline mutations called by the DeepVariant (v1.1.0) pipeline in VCF format; (2) rejected substitution scores calculated by the Genomic Evolutionary Rate Profiling (GERP++) software on each species and chromosome; and (3) the phylogenetic tree used as guide tree in the Cactus alignment.</p>
Data For: Identifying rare variants inconsistent with identity-by-descent in population-scale whole-genome sequencing data
<p>Simulation output and Genome-wide scan for nIBD variants in UK10K data as reported in:</p> <p>Identifying rare variants inconsistent with identity-by-descent in population-scale whole-genome sequencing data</p> <p>Johnson KE, Adams CJ, Voight BF. Methods Ecol Evol 2022 Nov;13(11): 2429–2442.</p> <p>Code available at: https://github.com/kelsj/EVICORD</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>
Variation of and associations with the depth and evenness of sequencing coverage in a sample of archived plastid genomes
<p>Depth and evenness of sequencing coverage are considered potential indicators of genome assembly quality. In plastid genomics, where new data generation has outpaced the development of suitable assembly quality indicators, these coverage metrics could offer insights into the quality of plastomes of different sizes, structures, or taxonomic origins. However, the typical variation of sequencing depth and evenness among archived plastid genomes, their variability between plastome partitions, and any association with methodological factors have yet to be evaluated. This study explores the variation of sequencing depth and evenness across a sample of publicly accessible plastid genomes and their potential associations with plastome structure, assembly accuracy, and the methodological provenance of the genome data using statistical tests. Our results indicate significant differences in sequencing depth across the four structural partitions as well as between the coding and non-coding sections of the genomes, a significant correlation between sequencing evenness and the number of ambiguous nucleotides, and a significant difference in sequencing evenness between several DNA sequencing platforms. These findings highlight that many publicly accessible plastid genomes are based on sequence data with highly variable sequencing depth and evenness and that this variation is influenced, at least partially, by genome structure and methodological factors.</p>
Whole Genome Sequencing of birches (Betulaceae: Betula) from the Kenai Peninsula, Alaska
<p>We sought to learn more about the identity of <em>Betula kenaica</em> W.H.Evans, the Kenai birch, which has remained somewhat enigmatic since its description in 1899.</p> <p>Tissue samples were collected from two birch specimens on August 12, 2019. Cuttings were taken from a <em>B. kenaica</em> individual ("BK01" in this dataset, <a href="https://arctos.database.museum/guid/KNWR:Herb:11553">https://arctos.database.museum/guid/KNWR:Herb:11553</a>) from Kasilof Beach, Kasilof, Alaska, one of the two type localities of <em>B. kenaica</em>. Cuttings were also taken from a <em>Betula pendula</em> subsp. <em>mandshurica</em> (Regel) Ashburner & McAll<em>.</em> individual ("BP01" in this dataset, <a href="https://arctos.database.museum/guid/KNWR:Herb:11556">https://arctos.database.museum/guid/KNWR:Herb:11556</a>) from Soldotna, Alaska.</p> <p>The cuttings were shipped to SNPsaurus (Eugene, Oregon, USA, <a href="https://www.snpsaurus.com/">https://www.snpsaurus.com/</a>) for whole genome sequencing.</p> <p>This dataset includes specimen collection data, the sample sample submission form, and the three files delivered by SNPsaurus.</p>
Xpresso: Predicting gene expression levels from genomic sequences
<p>Xpresso: Predicting gene expression levels from genomic sequences<br> <br> More info at:<br> Publication: https://doi.org/10.1016/j.celrep.2020.107663<br> Website: https://xpresso.gs.washington.edu/<br> Github: https://github.com/vagarwal87/Xpresso</p>
The Genome Sequence of Citrus Melanose Pathogen Diaporthe citri and Two Citrus related Diaporthe species
<p>The melanose diseases is one the most widespread and economically important fungal diseases of citrus worldwide. The causative agent is filamentous fungus <em>Diaporthe citri</em> Wolf (syn. <em>Phomopsis citri</em> H.S. Fawc.). Here, we report genome assemblies of three strains of <em>D. citri</em>, namely strains ZJUD2, ZJUD14 and Q7, generated using a combination of PacBio Sequel long-read and Illumina paired-end sequencing data. The assembled genomes of <em>D. citri</em> ranged 53.97 Mb to 63.64 Mb in genome size, containing 15,977 ~ 16,622 protein-coding genes. In addition, we sequenced and annotated the genome sequences of two Citrus related <em>Diaporthe </em>species, including <em>D. citriasiana</em> and <em>D. citrichinensis</em>. The described genome sequences and annotations can provide a useful resource in the study of fungal biology, pathogen-host interaction, molecular diagnostic marker development, and population genomic analyses of Citrus-related <em>Diaporthe</em> species.</p>
NEXUS file describing the taxonomic relationships of the 466 species for which genome sequencing was underway at Tree of Life, Wellcome Sanger Institute, at 31 December 2020
<p>This NEXUS file shows the taxonomic relationships of 466 species of eukaryote. The taxonomy derives from the NCBI TaxonomyDB. The species are those for which genome sequencing is underway at the Tree of Life programme, Wellcome Sanger Institute, as of 31st Decemnber 2020. The NEXUS file includes a figtree block generated in FigTree [<strong><a href="https://github.com/rambaut/figtree">https://github.com/rambaut/figtree</a>] </strong>that informs display of the data as a circular tree with species coloured by taxonomic Family, and Families with more than one species represented as triangles. The figure is used in publications and presentations describing the activities of the Tree of Life programme and the projects in which Tree of Life is involved, especially the Darwin Tree of Life project [https://darwintreeoflife.org].</p>
Data from: CIDER-Seq: unbiased virus enrichment and single-read, full length genome sequencing
<p>Raw and finished sequence data produced in the study: </p> <p>Mehta D, Hirsch-Hoffmann M, Patrignani A, Gruissem W, Vanderschuren H (2017) CIDER-Seq: unbiased virus enrichment and single-read, full length genome sequencing. <em><strong>bioRxiv</strong></em>. doi: https://doi.org/10.1101/168724</p>
FIG. 1 in Analysis of Genomic Sequence Data Reveals the Origin and Evolutionary Separation of Hawaiian Hoary Bat Populations
FIG. 1.—Map of the Hawaiian Islands with collection sitesfor Hawaiian hoary bat tissues used inthis study. Sites with n> 1 are denoted with an asterisk.
ScienceDex guides
Understand access before you commit
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.