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1,574 results for “genome sequencing”
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects with ancient samples
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects with high coverage sequenced ancient samples. The ancient samples are the Altai, Chagyrskaya, and Vindija Neanderthals, the Denisovan, and a high-coverage family of four from the Afanasievo Culture.</p> <p>Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred with <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1 and <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4, as described in <a href="http://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>. The files were 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_high_cov_ancients_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_high_cov_ancients_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>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>import json 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>
Results from the revision of MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data
<p>afr47041.zip, lat36378.zip, and eur115620.zip contain All of Us Summary Statistics used in the revised version of "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data". Summary statistics for three cohorts are included: Afr47k, Lat36k, and Eur116k. These cohorts have not been downsampled to have equal levels of missingness.</p> <p>pips.tsv contains fine-mapped variants with PIP > 0.01 via MultiSuSiE from the revised version of "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data". Subcohorts with the _unmatched suffix have not been downsampled to have equal levels of phenotyped missingness across ancestries. </p> <p>MultiSuSiE-main.zip contains the MultiSuSiE software packages (corresponds to the Github repo on 10/16/2025).</p> <p>Please cite:</p> <p>Rossen, Jordan, et al. "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data." <em>medRxiv</em> (2024): 2024-05.</p> <p> </p>
Genome sequencing and assembly of Lathyrus sativus
<p>The dataset contains the whole-genome assembly and protein sequences of <em>Lathyrus sativus</em> cultivar Pusa-24.</p>
Shotgun metagenomic sequencing dataset of a synthetic mock community containing 20 genomes spiked-in at even and staggered concentrations.
<p>Shotgun metagenomics (SM) sequencing is a popular method used in microbial ecology to obtain insights on microbial community structure and function potential in a given biological system without the need to cultivate microorganisms. The dataset described in this article describes technical triplicates of shotgun metagenomic sequence libraries generated from two purified and titrated mixes of 20 distinct reference bacterial genomes for which key characteristics such as genome size, sequence and spiked-in concentrations are known. In one of the genomic DNA mix, each genome is spiked-in at similar concentrations (representing an even microbial community) and in the other, genomes are spiked-in at different concentrations with some genomes highly abundant and other in low quantity, mimicking an uneven microbial community DNA extract. In order to be interpretable, SM sequencing data needs to be properly analyzed by complex analytical bioinformatic pipelines. Environments investigated with this method can range from simple to very complex. Typically, microbial communities contain microbes that are ubiquitous and some others much rarer. Analysis of rare microbes in a complex microbial community are challenging to perform as their sequencing signals get submerged by the microbial genomes that are more abundant. In this context, it is critical to have access to sequencing data of simple mock communities of mixes of well characterized genomes in order to develop and validate bioinformatic methods that aim to accurately analyze microbial communities.</p>
Data from: Cost-saving population genomic investigation of Daphnia longispina complex resting eggs using whole genome amplification and pre-sequencing screening
<p>This dataset contains all paired MiSeq sequences that were generated for the study "Cost-saving population genomic investigation of<em> Daphnia longispina</em> complex resting eggs using whole genome amplification and pre-sequencing screening" by Nickel and Cordellier.</p> <p>The sample names used in the study and the associated file names are explained in the table<strong> </strong>"Study_sample_names.xlsx"</p> <p> </p>
Genome-wide SNP discovery in native American and Hungarian Robinia pseudoacacia genotypes using next-generation double-digest restriction-site-associated DNA sequencing (ddRAD-Seq)
<p>Initial filtered ddRADseq dataset with highly variable SNP markers from native American and Hungarian <em>Robinia pseudoacacia</em> L. individuals</p>
Data corresponding to: Evaluation of sequencing and PCR-based methods for the quantification of the viral genome formula
<p>Viruses show great diversity in their genome organisation. Multipartite viruses package their genome segments into separate particles, most or all of which are required to initiate infection in the host cell. The benefits of such seemingly inefficient genome organization are not well understood. One hypothesised benefit of multipartition is that it allows for flexible changes in gene expression by altering the frequency of each genome segment in different environments, such as encountering different host species. The ratio of the frequency of segments is termed the genome formula (GF). Thus far, formal studies quantifying the GF have been performed for well-characterised virus-host systems in experimental settings using RT-qPCR. However, to understand GF variation in natural populations or novel virus-host systems, a comparison of several methods for GF estimation including high-throughput sequencing (HTS) based methods is needed. Currently, it is unclear how HTS-methods compare a golden standard, such as RT-qPCR. Here we show a comparison of multiple GF quantification methods (RT-qPCR, RT-digital PCR, Illumina RNAseq and Nanopore direct RNA sequencing) using three host plants (<em>Nicotiana tabacum</em>, <em>Nicotiana benthamiana</em>, and <em>Chenopodium quinoa</em>) infected with cucumber mosaic virus (CMV), a tripartite RNA virus. Our results show that all methods give roughly similar results, though there is a significant method effect on genome formula estimates. While the RT-qPCR and RT-dPCR GF estimates are congruent, the GF estimates from HTS methods deviate from those found with PCR. Our findings emphasise the need to tailor the GF quantification method to the experimental aim, and highlight that it may not be possible to compare HTS and PCR-based methods directly. The difference in results between PCR-based methods and HTS highlights that the choice of quantification technique is not trivial.</p>
Genome report: Genome sequence of 1S1, a transformable and highly regenerable diploid potato for use as a model for gene editing and genetic engineering
<p>Generation of a genomic resource for a readily transformable diploid potato would provide a resource for high throughput functional analysis in potato. The heterozygous <em>Solanum tuberosum</em> Group Phureja clone 1S1 has a high regeneration rate, self-fertility, desirable tuber traits and is amenable to <em>Agrobacterium</em>-mediated transformation. To create a contiguous genome assembly, a homozygous doubled monoploid of 1S1 (DM1S1) was sequenced using 44 Gbp of long reads generated from Oxford Nanopore Technologies (ONT), yielding a 736 Mb assembly that encoded 31,145 protein-coding genes. The final assembly for DM1S1 represents a nearly complete genic space, shown by the presence of 99.6% (C:99.5%[S:97.8%, D:1.7%],F:0.1%,M:0.4%,n:1614) of the Benchmarking Universal Single Copy Orthologs. Variant analysis with Illumina reads from 1S1 was used to deduce its alternate haplotype using the variant calling tools Strelka2 (v2.9.10), GATK's Haplotypecaller (v4.1.4.1), and Freebayes (v1.3.2). These variants were used to create consensus fasta sequences with the DM1S1 assembly using bcftools (v1.9.64).</p>
FASTA consensus sequences obtained using amplicon-based genome sequencing of SARS-CoV-2
<p>Set of 22 FASTA consensus sequences that were produced during routine SARS-CoV-2 sequencing obtained using amplicon-based sequencing (ARTIC protocol). Those sequences were compared to those generated in NASCarD applications.</p>
Genome sequences and gene annotations for two Ophryocystis lineages
<p>Assembly, annotation, and gene sequences for the <em>Ophryocystis </em>lineages sequenced in "Genome sequence of <em>Ophryocystis elektroscirrha</em>, an apicomplexan parasite of monarch butterflies: cryptic diversity and response to host-sequestered plant chemicals." Each of the two lineages has three associated files: a genome sequence file (.fa), an annotation in .gff3 format, and gene sequences in .fna format. Sequences generated for <em>Ophryocystis elektroscirrha </em>come from direct DNA extraction and sequencing effort and are hosted elsewhere on NCBI as well. The other lineage, prefixed Ophryocystis-elektroscirrha_like, was bioinformatically extracted from the genome of an infected host. As such, we are less confident in its completeness and it is not archived elsewhere. </p>
COVFlow: performing virus phylodynamics analyses from selected SARS-CoV-2 genome sequences
<p>This upload contains pipeline configuration files, output data, scripts and data identifiers (GISAID EPI_ISL_ID) required to reproduce the results of the article entitled "COVFlow: performing virus phylodynamics analyses from selected SARS-CoV-2 genome sequences".</p>
Demogaphic Reconstruction of the Western Sheep Expansion from Whole-Genome sequences
<p>Sheep (<em>Ovis aries</em>) were among the earliest livestock, domesticated in the Fertile Crescent about 12000-10000 years ago with a nearly worldwide distribution today. Most of our knowledge about the timing of their expansion stems from archaeological data but it is unclear how the genetic diversity of modern sheep fits with these dates. We used whole-genome sequencing data of 63 domestic breeds and their wild relatives, the Asiatic mouflon (<em>O. gmelini</em>), to explore the demographic history of sheep. <br> On the global scale, our analysis revealed geographic structuring among breeds with unidirectional recent gene flow from domestics into Asiatic mouflons. We then selected four representative breeds from Spain, Morocco, the UK and Iran to build a comprehensive demographic model of the western sheep expansion.<br> We inferred a single domestication event around 9,000 years ago, slightly later than archaeological evidence suggests which might reflect uncertainties in the generation time used for these estimates. The westward expansion is dated to approximately 5,000 years ago, later than the original Neolithic expansion of sheep and approximately matching the Secondary Product Revolution associated with woolly sheep. We see some signals of recent gene flow from an ancestral population into southern European breeds which could reflect admixture with feral European mouflon. Furthermore, our results indicate that many breeds experienced a reduction of their effective population size during the last centuries, probably associated with the breed development.<br> Our study provides insights into the complex demographic history of western Eurasian sheep, highlighting interactions between breeds and their wild counterparts.</p>
Genome sequencing of 2,000 canids advances the understanding of demography, genome function and architecture
<p><strong>Background: </strong>The international Dog10K project aims to sequence and analyze several thousand canine genomes. Incorporating 20x data from 1,987 individuals, including 1,611 dogs (321 breeds), 309 village dogs, 63 wolves and four coyotes, we identify genomic variation across the canid family, setting the stage for detailed studies of domestication, behavior, morphology, disease susceptibility and genome architecture and function.</p> <p><strong>Results: </strong>We report the analysis of >48M single nucleotide, indel, and structural variants spanning the autosomes, X chromosome and mitochondria. We discover more than 75% of variation for 239 sampled breeds. Allele sharing analysis indicates that 94.9% of breeds form monophyletic clusters and 25 major clades. German Shepherd Dogs and related breeds show the highest allele sharing with independent breeds from multiple clades. On average, each breed dog differs from the UU_Cfam_GSD_1.0 reference at 26,960 deletions and 14,034 insertions greater than 50bp, with wolves having 14% more variants. Discovered variants include retrogene insertions from 926 parent genes. To aid functional prioritization, single nucleotide variants were annotated with SnpEff and Zoonomia phyloP constraint scores. Constrained positions were negatively correlated with allele frequency. Finally, the utility of the Dog10K data as an imputation reference panel is assessed, generating high confidence calls across varied genotyping platform densities including for breeds not included in the Dog10K collection.</p> <p><strong>Conclusions:</strong> We have developed a dense dataset of 1,987 sequenced canids that reveals patterns of allele sharing, identifies likely functional variants, informs breed structure, and enables accurate imputation. Dog10K data are publicly available</p>
Genomic characterization and gene bank curation of Aegilops using genotyping-by-sequencing
<p>In this study, genotyping-by-sequencing (GBS) was performed on 1041 <em>Aegilops</em> accessions, representing 23 different species. These accessions have been maintained by the Wheat Genetics and Resource Center (WGRC) at Kansas State University. The GBS FASTQ files have been uploaded to the NCBI SRA public repository under the BioProject accession number # PRJNA985892. We have provided other files related to data analysis, such as the barcode key file, SNP matrices, and taxonomic information of the accessions in this Dryad repository, which can be accessed through the provided link. The aim of the study was to explore the genetic and genomic characteristics of wild wheat relatives, <em>Aegilops,</em> using a larger number of SNP markers. Here, we also curated the WGRC gene bank <em>Aegilops</em> collection via the identification of misclassified accessions and genetically identical redundant accessions. Further, we explored the genomic relationship between wheat and the different <em>Aegilops</em> species. </p>
The daft genome sequences for the isolated viruses.
<p>The daft genome sequences for the viruses isolated from Lake Biwa, Japan. The genomes were sequenced by MinION and assembled by Flye, Miniasm, and Raven, followed by short-read polish by using Pilon. </p>
Alliance of Genome Resources Sequence Variants
<p>Variant Call Format (VCF) formatted spreadsheets of sequence variants associated with phenotypic alleles from the Alliance of Genome Resources. Variants are in Human Genome Variation Society (HGVS) nomenclature syntax.</p> <p>Files include variants in VCF format for the following organisms:</p> <ul> <li>Caenorhabditis elegans (nematode; NCBI:txid 6239)</li> <li>Danio rerio (zebrafish;NCBI:txid 7955)</li> <li>Drosophila melanogaster (fruit fly; NCBI:txid 7227)</li> <li>Mus musculus (mouse; NCBI:txid10090)</li> <li>Rattus norvegicus (rat; NCBI:txid 10116)</li> </ul>
Whole-genome sequences of Cercospora beticola isolates from Germany and Italy.
<p>Sequences used in the manuscript <strong>"Large-scale analyses reveal the contribution of adaptive evolution in pathogenic and non-pathogenic fungal species"</strong></p>
Supplemental material of "An annotated whole-genome multilocus sequence typing schema for scalable high resolution typing of Streptococcus pyogenes"
<p>This supplemental material includes the genome assemblies, associated metadata and analysis results for five datasets used to define a publicly available annotated wgMLST schema for <em>S. pyogenes</em> and to evaluate its suitability for high resolution typing. A brief description for each file in the dataset is available in the included README file. Raw sequencing data and sample metadata for the 265 isolates included in Dataset1 have been deposited in the European Nucleotide Archive (ENA) under Project <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB49967?show=reads">PRJEB49967</a>.</p> <p>The wgMLST schema was created with <a href="https://github.com/B-UMMI/chewBBACA">chewBBACA</a> and is publicly available at <a href="https://chewbbaca.online/species/1/schemas/1">chewie-NS</a>, where a more detailed description of schema creation, annotation and curation can be found.</p>
Whole genome sequence and annotation dataset of rare actinobacteria, Barrientosiimonas humi gen. nov., sp. nov. 39T from Antarctica
<p>The present data files are the source files of the annotation output from the whole genome sequencing of rare actinobacteria, <em>Barrientosiimonas humi gen. nov., sp. nov.</em> 39<sup>T</sup> from Antarctica.</p> <p>The dataset of the whole-genome sequence of <em>B. humi</em> had been deposited in European Nucleotide Archive (ENA) repository under the accession number PRJEB44986 / ERP129097, direct URL to data:<strong> </strong><a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB44986">https://www.ebi.ac.uk/ena/browser/view/PRJEB44986</a></p>
Integrative QTL mapping and selection signatures in Groningen White Headed cattle inferred from whole-genome sequences
<p>Here, we aimed to identify and characterize genomic regions that differ between Groningen White Headed (GWH) breed and other cattle, and in particular to identify candidate genes associated with coat color and/or eye-protective phenotypes. Firstly, whole genome sequences of 170 animals from eight breeds were used to evaluate the genetic structure of the GWH in relation to other cattle breeds by carrying out principal components and model-based clustering analyses. Secondly, the candidate genomic regions were identified by integrating the findings from: a) a genome-wide association study using GWH, other white headed breeds (Hereford and Simmental), and breeds with a non-white headed phenotype (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel, Dutch Belted, and Holstein Friesian); b) scans for specific signatures of selection in GWH cattle by comparison with four other Dutch traditional breeds (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel and Dutch Belted) and the commercial Holstein Friesian; and c) detection of candidate genes identified via these approaches. The alignment of the filtered reads to the reference genome (ARS-UCD1.2) resulted in a mean depth of coverage of 8.7X. After variant calling, the lowest number of breed-specific variants was detected in Holstein Friesian (148,213), and the largest in Deep Red (558,909). By integrating the results, we identified five genomic regions under selection on BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 and 20.0–22.7 Mb), and BTA25 (0.5–9.2 Mb). These regions contain positional and functional candidate genes associated with retinal degeneration (e.g., <em>CWC27</em> and <em>CLUAP1</em>), ultraviole<em>t</em> protection (e.g., <em>ERCC8</em>), and pigmentation (e.g. <em>PDE4D</em>) which are probably associated with the GWH specific pigmentation and/or eye-protective phenotypes, e.g. Ambilateral Circumocular Pigmentation (ACOP). Our results will assist in characterizing the molecular basis of GWH phenotypes and the biological implications of its adaptation.</p>
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.