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2,848 results for “sequence data”
Pre-processed B-cell receptor amplicon sequencing data from SRR1842411
<p>An example dataset containing B-cell receptor (BCR) gene sequences. This dataset is intended to be used for testing software tools developed to annotate (i.e. map Variable, Diversity and Joining segments) and perform clonal analysis of BCR sequencing data.</p> <p><strong>Sequencing:</strong></p> <p>Libraries prepared using 5'RACE from PBMCs of a healthy donor. Input molecules were tagged with unique molecular identifiers (UMIs). Sequencing was ran on MiSeq , 300+300bp reads.</p> <p><strong>Contents:</strong></p> <p>The dataset contains both raw sequencing reads and high-quality consensus sequences assembled using unique molecular tagging (UMI) approach. Consensus assembly corrects for sequencing errors and eliminates sequencing artifacts.</p> <ul> <li>age_ig_s7_R1.fastq.gz and age_ig_s7_R2.fastq.gz contain raw reads</li> <li>age_ig_s7_R1.t10.cf.fastq.gz and age_ig_s7_R2.t10.cf.fastq.gz contain consensus sequences</li> </ul> <p>All files contain an UMI tag sequence in their header, in form UMI:NNNN:QQQQ where N is the base character and Q is the quality character (for assembled consensuses the total number of reads is given instead of Q string).</p> <p>Note that consensus sequences were assembled using only raw sequences that correspond to UMI tags supported by at least 10 sequencing reads. That means that consensus sequence files contain a subset of all UMI tags found in raw sequences. Thus, if one wants to assess software performance on raw sequencing reads using assembled consensus sequences as a high-quality data standard, raw sequencing reads should be filtered to contain only those UMI tags that are present in consensus sequence file.</p> <p><strong>Citations:</strong></p> <p>The whole dataset was used to benchmark MiXCR software and was originally referenced in Bolotin DA, et al. MiXCR: software for comprehensive adaptive immunity profiling Nature methods 12(5):380-381, 2015.</p> <p>Data pre-processing was carried out using MIGEC software, Shugay M et al. Towards error-free profiling of immune repertoires. Nature Methods 11(6):653-655, 2014.</p> <p><strong>Contributors:</strong></p> <p>The dataset was generated in Prof. Chudakov lab (Adaptive Immunity Group in Masaryk University, Brno and Genomics of Adaptive Immunity Lab in Institute of Bioorganic Chemistry, Moscow). Sample preparation and sequencing was performed by Dr. Olga Britanova and Dr. Maria Turchaninova. Raw sequencing reads were pre-processed and uploaded by Dr. Mikhail Shugay.</p>
Data from: Identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning
<h4>This repository contains the data and Docker image to reproduce the results of our paper: <strong>identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning</strong></h4> <p>Authors: Hengchuang Yin, Shufang Wu, Jie Tan, Qian Guo, Mo Li, Jinyuan Guo, Yaqi Wang, Xiaoqing Jiang, and Huaiqiu Zhu*</p> <p><strong>This work has been accepted by GigaScience. </strong></p> <p><strong>Hengchuang Yin, Shufang Wu, Jie Tan, Qian Guo, Mo Li, Jinyuan Guo, Yaqi Wang, Xiaoqing Jiang, and Huaiqiu Zhu. "IPEV: Identification of Prokaryotic and Eukaryotic Virus-Derived Sequences in Virome Using Deep Learning." GigaScience 13 (2024): giae018. <a href="https://doi.org/10.1093/gigascience/giae018" rel="nofollow">https://doi.org/10.1093/gigascience/giae018</a>.</strong></p> <div> </div> <p> </p> <p><strong>Background: </strong>The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses.</p> <p><strong>Findings: </strong>We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2D convolutional neural network combining trinucleotide pair relative distance and frequency. Cross-validation assessments of IPEV demonstrate its state-of-the-art precision, significantly improving the F1-score by approximately 22% on an independent test set compared to existing methods when query viruses share less than 30% sequence similarity with known viruses. Furthermore, IPEV outperforms other methods in accuracy on marine and gut virome samples based on annotations by sequence alignments. IPEV reduces runtime by at most 1,225 times compared to existing methods under the same computing configuration. We also utilized IPEV to analyze longitudinal samples and found that the gut virome exhibits a higher degree of temporal stability than previously observed in persistent personal viromes, providing novel insights into the resilience of the gut virome in individuals. </p> <p><strong>Conclusions: </strong>IPEV is a high-performance, user-friendly tool that assists biologists in identifying and classifying prokaryotic and eukaryotic viruses within viromes. The tool is available at https://github.com/basehc/IPEV.</p> <p> </p> <p><strong>5_fold_cross_validation.zip:</strong> Dataset of cross-validation of IPEV</p> <p><strong>Eukaryotic_virus_CV_Dataset-1.csv:</strong> GI, and accession ID for the cross-validation Dataset-1 (eukaryotic virus)</p> <p><strong>Prokaryotic_virus_CV_Dataset-1.csv:</strong> GI, and accession ID for the cross-validation Dataset-1 (prokaryotic virus)</p> <p><strong>Test_Prokaryotic_virus_Dataset-1.fasta: </strong>An independent test set of IPEV (prokaryotic virus)</p> <p><strong>Test_Eukaryotic_virus_Dataset-1.fasta: </strong>An independent test set of IPEV (eukaryotic virus)</p> <p> </p> <p> </p> <p><strong>Dataset_sequencing_error.zip: </strong>Simulated dataset with sequencing errors</p> <p><strong>Cap_enzyme_sequence.fasta: </strong>Accession IDs of Receptor Binding Proteins (RBPs) in phages collected by our article</p> <p><strong>Dataset_runtime_evaluation.zip: </strong>Dataset for evaluating the runtime of IPEV</p> <p><strong>Receptor_binding_protein_accession_id:</strong> Accession IDs of Receptor Binding Proteins (RBPs) in phages collected by our article</p> <p> </p> <p><strong>archaea_ID.txt</strong> Accession ID information for the reference archaea dataset</p> <p><strong>bacteria_ID.txt</strong> Accession ID information for the reference bacterial dataset</p> <p><strong>marine_virome_id.csv:</strong> Ocean virome data information used in our paper</p> <p><strong>gut_virome.csv</strong>:Gur virome data information used in our paper</p> <p><strong>fungi.txt: </strong>Negative sequence information used to train, validate, and test the model in the decontamination function</p> <p><strong>bacteria.txt: </strong>Negative sequence information used to train, validate, and test the model in the decontamination function</p> <p> </p> <p> </p> <h4><strong>Reproduce the results of our paper from a Docker image.</strong></h4> <p> </p> <p>We also provide a Docker image file that does not require any environment configuration. You can reproduce the results of our paper (e.g., train and test our IPEV model) in a Docker image.</p> <p>Pull the<a href="https://hub.docker.com/r/dryinhc/ipev_v1"> <em>dryinhc/ipev_v1</em></a> image from Docker Hub. Open a terminal window and run the following command:</p> <p><em>docker pull dryinhc/ipev_v1</em></p> <p>This will download the image to your local machine.</p> <p>Run the <em>dryinhc/ipev_v1</em> image. In the same terminal window, run the following command:</p> <p><em>docker run -it --rm dryinhc/ipev_v1</em></p> <p>This will start a container based on the image and run the IPEV tool.</p> <p>And you can run cd train or cd other file folders in the container.</p> <p>To exit the container, press <em>Ctrl+D</em> or type <em>exit</em>.</p> <p>It contains 4 directories, namely 5 fold cross validation, independent set, marine virome, and gut virome. The 5-fold cross-validation directory holds the scripts required for implementing the 5-fold cross-validation method. The independent set directory contains scripts necessary for working with an independent set. Lastly, the marine virome and gut virome directories store scripts for analyzing real datasets.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p>We hereby confirm that the dataset associated with the research described in this work is made available to the public under the Creative Commons Zero (CC0) license.</p> <p> </p> <p> </p> <h4><strong>Contact </strong></h4> <p> </p> <p>If you have any questions, please don't hesitate to ask me: yinhengchuang@pku.edu.cn or hqzhu@pku.edu.cn</p>
Sequence data processing R script
<p>R script used for processing the 16S Illumina paired end read data using the DADA2 pipeline.</p><p> </p>
Whole genome sequencing data for Saccharomyces cerevisiae CBS 493.94
<p>SNP distance matrix.</p>
FIG. 3 in Analysis of Genomic Sequence Data Reveals the Origin and Evolutionary Separation of Hawaiian Hoary Bat Populations
FIG. 3.—Populationstructure inference based on STRUCTURE analysis of 199,921 sites for individual bats from four Hawaiian Islands. (A) Ad hoc statistic delta Kanalysis indicates a peak at the Κ = 5; (B) STRUCTURE population inference with Κ = 3, 4, 5. Sample information included in supplementary table S4, Supplementary Material online.
Supplementary material 2 from: Reid BN, Servis JA, Timmers M, Rohwer F, Naro-Maciel E (2022) 18S rDNA amplicon sequence data (V1–V3) of the Palmyra Atoll National Wildlife Refuge, Central Pacific. Metabarcoding and Metagenomics 6: e78762. https://doi.org/10.3897/mbmg.6.78762
Figure S2
Supplementary material 5 from: Reid BN, Servis JA, Timmers M, Rohwer F, Naro-Maciel E (2022) 18S rDNA amplicon sequence data (V1–V3) of the Palmyra Atoll National Wildlife Refuge, Central Pacific. Metabarcoding and Metagenomics 6: e78762. https://doi.org/10.3897/mbmg.6.78762
Table S3
Supplementary material 6 from: Reid BN, Servis JA, Timmers M, Rohwer F, Naro-Maciel E (2022) 18S rDNA amplicon sequence data (V1–V3) of the Palmyra Atoll National Wildlife Refuge, Central Pacific. Metabarcoding and Metagenomics 6: e78762. https://doi.org/10.3897/mbmg.6.78762
Table S4
Supplementary material 1 from: Reid BN, Servis JA, Timmers M, Rohwer F, Naro-Maciel E (2022) 18S rDNA amplicon sequence data (V1–V3) of the Palmyra Atoll National Wildlife Refuge, Central Pacific. Metabarcoding and Metagenomics 6: e78762. https://doi.org/10.3897/mbmg.6.78762
Figure S1
Supplementary material 4 from: Reid BN, Servis JA, Timmers M, Rohwer F, Naro-Maciel E (2022) 18S rDNA amplicon sequence data (V1–V3) of the Palmyra Atoll National Wildlife Refuge, Central Pacific. Metabarcoding and Metagenomics 6: e78762. https://doi.org/10.3897/mbmg.6.78762
Table S2
Supplementary material 3 from: Reid BN, Servis JA, Timmers M, Rohwer F, Naro-Maciel E (2022) 18S rDNA amplicon sequence data (V1–V3) of the Palmyra Atoll National Wildlife Refuge, Central Pacific. Metabarcoding and Metagenomics 6: e78762. https://doi.org/10.3897/mbmg.6.78762
Table S1
Figure 8 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 8. Relationship of diagnosability to Nei's net nucleotide divergence (dA, Nei and Kumar 2000). Colors indicate taxonomic level of comparison: species (blue), subspecies (green), and populations (red). Vertical lines indicate the binomial 95% CI around diagnosability. Note that x-axis for dA is log10-scaled.
Supplementary Data for "A Holocene n-alkane stable isotope sequence from Wonderwerk Cave, 1 South Africa and its implications for the Later Stone Age record"
Open the record for dataset details and reuse information.
Curated RNA Sequencing Data for Zebrafish (Danio rerio) snoRNA Expression Analysis
Open the record for dataset details and reuse information.
Black hole - neutron star initial data sequences
<p>Black hole - neutron star initial data sequences, obtained as solutions to the constraint equations in the eXtended Conformal Thin-Sandwich formulation under the assumption of quasi-equilibrium. The data has been obtained using the v2 version of the publically available elliptic data solver FUKA. <br><br>Update as of 10th Feb 2025:</p> <p>Equations of state used to produce the QE sequences (and necessary for the reader executable) have been added. </p>
UCE and Sanger sequenced data for phylogenetic analysis of jumping spiders (Baviini and Nungia, Salticidae)
<p>The systematics and taxonomy of the tropical Asian jumping spiders of the tribe Baviini is reviewed, with a molecular phylogenetic study (UCE sequence capture, traditional Sanger sequencing) guiding a reclassification of the group's genera. The well-studied members of the group are placed into six genera: <i>Bavia</i> Simon, 1877, <i>Indopadilla</i> Caleb & Sankaran, 2019, <i>Padillothorax</i> Simon, 1901, <i>Piranthus</i> Thorell, 1895, <i>Stagetillus</i> Simon, 1885, and one new genus, <i>Maripanthus</i> Maddison. The identity of <i>Padillothorax</i> is clarified, and <i>Bavirecta</i> Kanesharatnam & Benjamin, 2018 synonymized with it. <i>Hyctiota</i> Strand, 1911 is synonymized with <i>Stagetillus</i>. The molecular phylogeny divides the baviines into three clades, the <i>Piranthus</i> clade with a long embolus (<i>Piranthus</i>, <i>Maripanthus</i>), the genus <i>Padillothorax</i> with a flat body and short embolus, and the <i>Bavia</i> clade with a higher body and (usually) short embolus (remaining genera). In general, morphological synapomorphies support or extend the molecularly-delimited groups. Eighteen new species are described (all with taxonomic authority W. Maddison): <i>Bavia nessagyna</i>, <i>Indopadilla bamilin</i>, <i>I. kodagura</i>, <i>I. nesinor</i>, <i>I. redunca</i>, <i>I. redynis</i>, <i>I. sabivia</i>, <i>I. vimedaba</i>, <i>Maripanthus draconis</i> (type species of <i>Maripanthus</i>), <i>M. jubatus</i>, <i>M. reinholdae</i>, <i>Padillothorax badut</i>, <i>P. mulu</i>, <i>Piranthus api</i>, <i>P. bakau</i>, <i>P. kohi</i>, <i>P. mandai</i>, and <i>Stagetillus irri</i>. The distinctions between baviines and the astioid <i>Nungia</i> Żabka, 1985 are reviewed, leading to four species being moved into <i>Nungia</i> from <i>Bavia</i> and other genera<i>. </i>Fifteen new combinations are established, and one combination is restored. Five of these new or restored combinations correct previous errors of placing species in genera that have superficially similar palps but extremely different body forms, in fact belonging in distantly related tribes — emphasizing that the general shape of male palps should be used with caution in determining relationships. A little-studied genus, <i>Padillothorus</i> Prószyński, 2018, is tentatively assigned to the Baviini. <i>Ligdus</i> Thorell, 1895 is assigned to the Ballini.</p>
Mullus surmuletus environmental DNA intraspecific metabarcoding Next-Generation Sequencing data
<p>Four 250-liter aquariums were bleached clean one day prior to be used (filled with seawater; fish transfer) in Montpellier (France). Seawater collected by the French Research Institute for Exploitation of the Sea at Palavas-les-Flots (France) was first stored in a 1,000 L tank for two weeks, under UV treatment to avoid any contamination. The aquariums were then filled with 120 L of this water. Each aquarium had a closed-circuit water circulation and was equipped with an air bubbles exhauster in a tube that brought up the water on a neutral synthetic foam filter. The aquariums were thus oxygenated and the coarsest suspended matter was filtered out. The remaining seawater in the tank was used as a negative control (Aquarium 1). Nine to eleven fish were added to each of the four aquariums (Fig. 1). The aquarium water was sampled six hours after introducing the fish into the aquariums using an Athena peristaltic pump (SPYGEN, Le Bourget-du-Lac, France) with a nominal flow of 1.0 L/min to filter 30 L, and VigiDNA 0.22 μm crossflow filtration capsules (SPYGEN) with disposable sterile tubing. After filtration, 80 mL of CL1 conservation buffer (SPYGEN) was added before storing the samples at ambient temperature.</p> <p> </p> <p>We reanalyzed here two eDNA samples of 30 L replicate each, collected in the Mediterranean Sea, at Banyuls (France, coordinates: 42.41568, 3.17110) and Calvi (France, coordinates: 42.62964, 8.89161) published in a previous metabarcoding analysis and known to contain <em>M. surmuletus</em> sequences (detected with the metabarcode teleo 12S) (Boulanger <em>et al.</em> 2021). These two Mediterranean eDNA samples were amplified and sequenced using the primers developed for this study and then analyzed using the best-performing pipeline as determined by our evaluation. These two samples were used as proof of concept of the possibility to estimate within site variability in real conditions.</p> <p> </p> <p> </p> <p>DNA extraction and amplification from eDNA samples were performed by the company SPYGEN (Le Bourget du Lac, France) in separate, dedicated rooms following the protocol described by Polanco Fernández <em>et al.</em> (2020). The amplification was performed in a final volume of 25 μL including 1 U of AmpliTaq Gold DNA Polymerase (Applied Biosystems, Foster City, CA, USA), 10 mM of Tris-HCl, 50 mM of KCl, 2.5 mM of MgCl2, 0.2 mM of each dNTP, 0.2 μM of each primer, 0.2 μg/μL of bovine serum albumin (Roche Diagnostics, Basel, Switzerland) and 3 μL of DNA template. The PCR mixture was denatured at 95°C for 10 min, followed by 50 cycles of 30 s at 95°C, 30 s at 47°C and 1 min at 72°C and a final elongation step at 72°C for 7 min. The primers were 5’-labelled with an eight-nucleotide tag unique to each DNA sample, allowing each sequence to be assigned to the corresponding sample during the sequence analysis. Twelve replicate PCRs were run per sample. Two libraries were prepared using the MetaFast protocol (Fasteris 2020, <a href="https://www.fasteris.com/dna/">https://www.fasteris.com/dna/</a>) and the sequencing was performed by Fasteris (Geneva, Switzerland) on two separate runs on an Illumina MiSeq (2x250 bp) (Illumina, San Diego, CA, USA) and the Miseq Kit v3 (Illumina) following the manufacturer’s instructions. Two negative extraction controls and one negative PCR control (12 replicates of ultrapure water) were amplified and sequenced to monitor for possible contaminants (Polanco Fernández <em>et al.</em>, 2020).</p> <p> </p> <p> </p> <p> </p>
Oxylobus (Asteraceae) analysis DNA sequence data
<p>A molecular phylogenetic investigation was carried out to clarify aspects of the systematics of <i>Oxylobus</i>, a primarily Mexican alpine genus of Eupatorieae. Analysis of sequence data from two nuclear (nrDNA ITS, ETS) and three plastid markers (<i>rbcL</i>, <i>ndhF</i>, <i>matK</i>) confirmed the monophyly of <i>Oxylobus</i> and placed species of <i>Ageratina</i> as its sister group. A survey of 56 samples of <i>Oxylobus</i> using nrDNA ITS and ETS provided support for the currently accepted species, and showed the recently described <i>O. coyulensis</i> to be distinct and the sister group to the rest of the genus. The results also confirmed the placement of <i>O. juarezensis</i> in synonymy with <i>O. subglabrus</i>. The results of a broad survey of <i>Ageratina</i> for ITS data showed that it is likely not monophyletic as currently circumscribed. The phylogenetic results also highlighted the distinctiveness of <i>Piqueria</i> and <i>Piqueriopsis</i> as a distinct clade at the base of Eupatorieae.</p>
Data set for the paper "Temperatures and cooling rates recorded by the New Caledonia ophiolite: implications for cooling mechanisms in young forearc sequences"
<p>Tables 1-3 and Tables S1-S4</p>
Alignments of Sequence Data for Phylogenetic Analysis of Damsel
<p>Initially described in 1882, <i>Chromis enchrysurus</i>, the Yellowtail Reeffish, was redescribed in 1982 to account for an observed color morph that possesses a white tail instead of a yellow one, but morphological and geographic boundaries between the two color morphs were not well understood. Taking advantage of newly collected material from submersible studies of deep reefs and photographs from rebreather dives, we sought to determine whether the white-tailed <i>Chromis</i> is actually a color morph of <i>Chromis enchrysurus</i> or a distinct species. These alignments for mitochondrial genes cytochrome b and cytochrome c oxidase subunit I were used to generate phylogenetic trees that separated <i>Chromis enchrysurus</i> and the white-tailed <i>Chromis</i> into two reciprocally monophyletic clades. Genetic, morphological, and biogeographic data all indicate that the white-tailed <i>Chromis</i> is a distinct species, herein described as <i>Chromis vanbebberae </i>sp. nov. The discovery of a new species within a conspicuous group such as damselfishes in a well-studied region of the world highlights the importance of deep-reef exploration in documenting undiscovered biodiversity.</p>
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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.