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19,342 results for “Sequence”
Figure 1 in Evaluation of the taxonomy of Helix cincta (Muller, 1774) and Helix nucula (Mousson, 1854); insights using mitochondrial DNA sequence data
Figure 1. Map showing the localities of samples used in the present study representing the morphologically defined species and the distribution of Helix cincta (dash line, light grey) and Helix nucula (continuous line, dark grey).
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>
ID linking table for all samples that have been sequenced in the CRUK-ICGC Prostate Group projects
<p>Prostate cancer somatic genomic sequencing data generated from 2011 onwards under auspices of the <a href="https://icgc.org/icgc/cgp/70/508/71331">International Cancer Genome Consortium Prostate Cancer UK consortium</a> (CRUK-ICGC Prostate Group), co-led by Colin Cooper and Ros Eeles, with other Principal Investigators (Brewer, Neal, Bova, McDermott, Wedge, Lynch, Massie, and Foster) and others as listed in study publications. Funded by Cancer Research UK and other funders as listed in study publications. Contents and publications related to each dataset are described with each dataset (EGAD) entry.</p> <p>Our study was funded with the ambition of collecting Whole Genome DNA sequence data from 250 prostate cancers, with matching transcriptome and methylome data.</p> <p>The original aims of the project were:</p> <p>1. To understand the significance of multifocal prostate cancer. </p> <p>2. To understand the clinical heterogeneity of prostate cancer to devise markers for predicting outcome and for drug targeting.</p> <p>3. To understand the molecular basis of development and spread of castration-resistant and metastatic disease </p> <p>4. To understand the aetiology of prostate cancer particularly the large variation in incidences that occur in different populations and ethnic groups. </p> <p>This table contains links between all the IDs, in particular between those that have been published in research articles and the The European Genome-phenome Archive (EGA) where the raw sequencing data is held (https://ega-archive.org/studies/EGAS00001000262).</p>
WorldCOM Deliverable 1: Prevalence of ESBL subtypes in bacterial pathogens and a sequence database of selected alleles
<p><strong>OHEJP Project: WorldCOM, Deliverable 1, Work Package 1.</strong></p> <p>This dataset is connected to Work Package 1, Task1 of the WorldCOM consortium grant within the One Health EJP group. The aim was to analyse publicly available sequences for antimicrobial resistance genes associated with <em>Salmonella</em>, <em>Campylobacter</em> and <em>E. coli</em>. For the initial phase of this work package, we have focused on ESBL-related AMR genes. As these genes are absent from <em>Campylobacter</em>, we have not included this bacterium in these analyses, and have used the important pathogens <em>Klebsiella</em> and <em>Acinetobacter</em>. All types and subtypes of Extended Spectrum β-Lactamases (ESBLs) and plasmid-mediated colistin resistance genes have been analysed for frequency among reported and extracted sequences. High frequency resistant genes subtypes have been highlighted for further sequence analysis to illustrate geographic distribution and geographic-specific single nucleotide polymorphisms (SNPs). The data shown are work in progress. </p>
Genbank accession numbers of sequences used in the phylogenetic analysis
<p><em>Phylogenetic analysis: </em>Sequences were assembled using Lasergene v15 (DNASTAR, Inc. Madison, USA), and combined with sequences obtained from Genebank</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>
Software Product Line Traceability and Product Configuration in Class and Sequence Diagrams: an Empirical Study
<p>Software Product Line Traceability and Product Configuration in Class and Sequence Diagrams: an Empirical Study</p>
High-quality large curated dataset of protein sequences (1.83 million) and their corresponding Position Specific Scoring Matrices
<p>As part of his master thesis at the Rostlab, which is located at the Technical University of Munich (TUM), Mr. Issar Arab developed the first language model that encodes evolutionary information of proteins explicitly. The pre-training involved the creation of a novel high-quality dataset of protein sequences (around 1.83 million proteins, or ~0.8 Billion amino acids) with their corresponding Position Specific Scoring Matrices (PSSMs). Those matrices reflect the relative frequency of each amino acid at each position in a protein and is derived from evolutionarily related proteins.</p> <p>Mr. Arab makes this work publicly available to help other researchers speed up their work to leverage AI to learn the representation of protein evolutionary information more explicitly. The set of sequences was derived by extracting all PSSMs from the <a href="https://predictprotein.org/">PredictProtein</a> (PP) cache, which were also part o the UniProt Reference Cluster with 50% sequence identity (uniref50 2019_12). The overlap between PP and uniref50 was further filtered to only include high-quality samples, e.g. only multiple sequence alignments with a certain number of aligned sequences were considered. The processing led to a training set of 1.83 Million sequences, a validation set of 879 instances, and a test set of 879 entries. The training data of proteins is reduced to 40% sequence identity, with respect to the validation/test sets, and contains sequences ranging between 18 and 9858 residues in length.</p> <p>Refer to the Jupyter notebook for a detailed description of the files' structure and a Python code snippet to correctly manipulate this data.</p> <p>To access the full original work, please visit the following link: <a href="https://mediatum.ub.tum.de/node?id=1579236">Manuscript</a> <br><br><strong>Note:</strong> The dataset was recently used to fine tune a protein sequence language model (<a href="https://github.com/issararab/PEvoLM">PEvoLM</a>). The work was presented at the CIBCB'23 conference. If you use PEvoLM or this dataset in your work, please cite the following publication:</p> <p>- Issar Arab, <strong>PEvoLM: Protein Sequence Evolutionary Information Language Model</strong>, <em>IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Eindhoven, Netherlands</em>, (2023), pp. 1-8, doi:<a href="https://ieeexplore.ieee.org/document/10264890">10.1109/CIBCB56990.2023.10264890</a></p>
16S gene and ASV sequences of bacteria isolated from soil and the phyllosphere of Arabidopsis thaliana
<p>Data from "Induction of antibiotic specialized metabolism by co-culturing in a collection of phyllosphere bacteria" by Qi et al. </p> <p>- 16S gene sequences of bacteria isolated from soil and the phyllosphere of Arabidopsis thaliana in FASTA format. </p> <p>- Filtered OTU (ASV) table across all samples </p> <p>- ASV sequences</p>
Supporting data for Novel functional sequences uncovered through a bovine multi-assembly graph
<p><strong>Description of the datasets</strong></p> <p>Data are organized as a folder and compressed with tar.gz.</p> <p>You need to unzip the folder using the command <em>tar -xz</em><em>v</em><em>f</em> data.tar.gz. Unzipping will output a folder named <em>data_tidy</em>, which is organized as follow:</p> <ul> <li>graph.gfa : Graph in GFA format constructed from 6 cattle assemblies</li> <li>nonref.fa : Non-reference sequences extracted from the graph</li> <li>nonref.fa.masked: Hard masked repetitive regions version of nonref.fa</li> <li>nonref_woflanking.fa: Nonref.fa without flanking sequences</li> <li>nonref_woflanking.fa.masked: Masked version of nonref_woflanking.fa</li> <li>augustus_predict.gtf: Annotated gene models of Augustus from non-ref sequences</li> <li>augustus_prot.fa: Protein fasta of the predicted gene models from Augustus</li> <li>breeds_assembled.gtf: Annotation of the StringTie assembled across-breed transcriptome</li> <li>breeds_expressed.tsv: Expression data of breeds_assembled.gtf</li> <li>de_assembled.gtf: Annotation of the StringTie assembled differentially-expressed transcriptome on non-ref sequences</li> <li>de_expression.tsv: Differential expression results from de_assembled.gtf</li> <li>variant_nonref.tsv: Variants called from non-ref sequences (-1, 0, 1, 2 indicates no call, hom ref, het, and hom alt respectively)</li> </ul>
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>
Protein codes for tetrameric and dimeric 6-phosphogluconate dehydrogenases and their signature sequence relevant to the cofactor specificity in the 6PGDH family.
<p>Here we present the Uniprot or Genbank code for tetrameric and dimeric 6-phosphogluconate dehydrogenases collected from different organisms. Besides, we include the sequence of the <span class="math-tex">\(\beta2-\alpha2\)</span> motif regarding the cofactor specificity in the 6PGDH family. </p> <p>The sequence data allows generating a phylogenetic tree of the 6PGDH family. The enzymes cluster first by their oligomerization state and then by their sequence in the <span class="math-tex">\(\beta2-\alpha2\)</span> motif, as is shown in the annexed figure. </p>
Vector sequences in early WIV SRA sequencing data of SARS-CoV-2 inform on a potential large-scale security breach at the beginning of the COVID-19 pandemic
<p>DESCRIPTION</p> <p>Sequences identified as Influenza A virus, Spodoptera frugiperda rhabdovirus and Nipah henipavirus have been previously identified within the early HiSeq 1000 and HiSeq 3000 sequencing data of SARS-CoV-2, SRR11092059,SRR11092060,SRR11092061 and SRR11092062, and were being used to support the hypothesis that a "simultaneous outbreak of multiple zoonotic viruses" have happened in the Huanan Seafood market. https://doi.org/10.31219/osf.io/s4td6</p> <p>However, a closer examination of these sequences revealed that they were not sequences of actual wild viruses, but were in stead fragments left behind from PCR products and cloning vectors harboring both cDNA clones and infectious clones of such viruses, with evidence of viral sequences being joined directly to DNA sequences of vector and non-human origin within the same short reads.</p> <p>Here are the vector sequences and PCR product-like sequences recovered from the earliest WIV SRA sequencing data of Human SARS-CoV-2 from dataset SRR11092059,SRR11092060,SRR11092061,SRR11092062.</p> <p>Sequences associated with Vectors and PCR products from 3 distinct viral species have been obtained: The 3'-end of a Nipah Henipahvirus with fusion to a Hepatitis D virus Ribozyme, a T7 terminator and a Tetracycline resistance gene, The 5'-end of the same Nipah Henipahvirus with fusion to sequences found in diverse vectors, A complete vector genome encoding the HA gene of Influenza A virus subtype H7N9 under a CMV promoter and a bgH polyA terminator, and 221 Contiguous sequences corresponding to the Spodoptera frugiperda rhabdovirus reference genome fused to sequences that were homologous to multiple Plastid sequences and Notably Mitochondrial sequences of Rodents.</p> <p>As sequences corresponding to a rescued infectious clone of a BSL-4 organism (Nipah Henipahvirus) were found in sample sequences that supposedy represents patient samples that were obtained from Hospital ICU and sequenced in a pathogen diagnosis laboratory (which is separate from the Virology Research laboratory which is implied by the context of an Infectious Clone of such an organism, evident by the 3'-HDV ribozyme and T7 terminator fused directly to the 3'-terminus of the Nipah Henipahvirus reads), The discovery of artifact-containing sequences of at least 3 different pathogen species that are phylogenetically and methodologically distinct from each other in samples that were supposedly submitted by a laboratory that is Separate from the virological research laboratories that could have hosted such clone sequences imply extensive crosstalk and cross-contamination between the various laboratories within the Wuhan Institute of Virology, which includes at least one BSL-4 laboratory with evidence of containment breach of a BSL-4 organism and it's subsequent introduction into RNA-seq samples that were processed by a laboratory of distinct and separate purposes than the basic virological research evidenced by the Infectious Clone of the Hipah Henipahvirus.</p> <p>Such a discovery therefore likely imply a major security breach happening within the Wuhan institute of Virology at the time when the first sequences of SARS-CoV-2 was sampled and sequenced, which have important implications on the origins of the SARS-CoV-2 virus itself.</p> <p>METHODS</p> <p>The metagenomic sequencing datasets, SRR11092059,SRR11092060,SRR11092061 and SRR11092062 were first analyzed using the NCBI phylogenetic analysis tool, which identified viral sequences that is not related to SARS-CoV-2 itself. These include Influenza A virus (IAV, subtype H7N9), Spodoptera frugiperda rhabdovirus and Nipah Henipahvirus.</p> <p>The datasets were then subjected to BLAST search using MEGABLAST against the reference sequences of such viruses to verify the existence of the viral sequences and determine the exact sybtype of such viruses and the closest sequences on GenBank that corresponds to the reads. There seuqences are MH926031.1 for the Spodoptera frugiperda rhabdovirus, KY199425.1 for the Influenza A virus and AY988601.1 for the Nipah Henipahvirus.</p> <p>A second round BLAST analysis with these identified sequences were then performed, which unexpectedly revealed numerous reads corresponding to Cloning vectors and non-human Mitochondrial and Plastid sequences being fused directly to the sequences of the identified viral species. Reads were then downloaded and subjected to assembly using the CAP3 sequence assembly program and the EGASSEMBLER tool. Contig sequences were then queried against the NCBI nr/nt database which unanimously identified the original sample sequences as viral sequences inserted into cloning vectors.</p> <p>The complete sequence of the Influenza A virus Haemagluttinin (HA) gene clone was obtained from SRR11092061,SRR11092062 using multiple rounds of BLAST search and sequence assembly expansion on the existing vector-virus junction contigs, and a partial sequence corresponding the 3'-end of Nipah Henipahvirus AY988601.1 fused to a 3'-HDV ribozyme, T7 terminator and a Tet resistance gene was obtained from SRR11092059. In addition, 221 Contig sequences corresponding to the Rhabdovirus MH926031.1 fused to Chloroplast sequence MN524635.1 and Rodent Mitochondrial sequence MT241668.1 have been recovered from SRR11092061.</p> <p>We then performed a BLAST search using the identified vector sequences on SRR11092059,SRR11092060,SRR11092061 and SRR11092062, which confirms the existence of these two vetor sequences in all 4 datasets.</p>
Data for: Faster rates of molecular sequence evolution in reproduction-related genes and in species with hypodermic sperm morphologies
<p>This repository contains a record of analysis scripts and sequence alignments used for the analyses presented in the manuscript.</p> <p>Some of the R scripts depend on supplementary tables associated with the manuscript.</p>
Multiple sequence alignments of sensor histidine kinases and response regulators
<p>The two FASTA files contain multiple sequence alignments of sensor histidine kinase and response regulator sequences. The source sequences were obtained by BLAST, clustered with usearch and aligned with muscle. More details to be found in Multamäki et al. 2021.</p>
High-Throughput Sequencing of Human Immunoglobulin Variable Regions with Subtype Identification
<p>Raw Illumina MiSeq data in zipped FASTQ format. The data set includes demultiplexed samples from three different time points (_wk*_) of patient ZA159 (159_*), samples from four different preps of a healthy donor (HD1_*), and samples from IgG subtype sorted cells of a healthy donor (HD3_*). Every sample consists of forward (_R1_), reverse (_R2_) and index read 1 (_I1_). </p>
Unbiased metagenomic sequencing complements specific routine diagnostic methods and increases chances to detect rare viral strains
<p>Raw Illumina MiSeq data in zipped FASTQ format.</p> <p>Files are named by sample type and time point (weeks after transplantation).</p>
Robustness of massively parallel sequencing platforms
<p>The improvements in high throughput sequencing technologies (HTS) made clinical sequencing projects such as ClinSeq and Genomics England feasible. Although there are significant improvements in accuracy and reproducibility of HTS based analyses, the usability of these types of data for diagnostic and prognostic applications necessitates a near perfect data generation. To assess the usability of a widely used HTS platform for accurate and reproducible clinical applications in terms of robustness, we generated whole genome shotgun (WGS) sequence data from the genomes of two human individuals in two different genome sequencing centers. After analyzing the data to characterize SNPs and indels using the same tools (BWA, SAMtools, and GATK), we observed significant number of discrepancies in the call sets. As expected, the most of the disagreements between the call sets were found within genomic regions containing common repeats and segmental duplications, albeit only a small fraction of the discordant variants were within the exons and other functionally relevant regions such as promoters. We conclude that although HTS platforms are sufficiently powerful for providing data for first-pass clinical tests, the variant predictions still need to be confirmed using orthogonal methods before using in clinical applications. </p>
Raw NGS data for the study 'Spouse-to-spouse Transmission and Evolution of Hypervariable Region 1 and 5’ Untraslated Region of Hepatitis C Virus Analyzed by Next-generation Sequencing'
<p>This file contains the original next-generation sequencing data (raw sequences in fastq format) which were analyzed in the study titled: "Spouse-to-spouse Transmission and Evolution of Hypervariable Region 1 and 5’ Untraslated Region of Hepatitis C Virus Analyzed by Next-generation Sequencing".</p> <p> </p> <p> </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.