Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
468
datasets available to search
ShareScore release 0.9.0
Dataset results
468 results for “Deep sequencing”
Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial – BCPP/ Ya Tsie trial
<p><strong>Background:</strong> Mathematical models predict that community-wide access to HIV testing-and-treatment can rapidly and substantially reduce new HIV infections. Yet several large universal test-and-treat HIV prevention trials in high-prevalence epidemics demonstrated variable reduction in population-level incidence.</p> <p><strong>Methods:</strong> To elucidate patterns of HIV spread in universal test-and-treat trials we quantified the contribution of geographic-location, gender, age and randomized-HIV-intervention to HIV transmissions in the 30-community Ya Tsie trial in Botswana (estimated trial population: 175,664).</p> <p><strong>Results:</strong> Deep-sequence phylogenetic analysis revealed that most inferred HIV transmissions within the trial occurred within the same or between neighboring communities, and between similarly-aged partners. Transmissions into intervention communities from control communities were more common than the reverse post-baseline (30% [12.2 – 56.7] versus 3% [0.1 – 27.3]) than at baseline (7% [1.5 – 25.3] versus 5% [0.9 – 22.9]) compatible with a benefit from treatment-as-prevention.</p> <p><strong>Conclusion:</strong> Our findings suggest that population mobility patterns are fundamental to HIV transmission dynamics and to the impact of HIV control strategies.</p> <p><strong>Funding:</strong> This study was supported by the National Institute of General Medical Sciences (U54GM088558); the Fogarty International Center (FIC) of the U.S. National Institutes of Health (D43 TW009610); and the President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention (CDC) (Cooperative agreements U01 GH000447 and U2G GH001911).</p>
T-cell receptor Vβ (TCRVB) deep sequencing on human T cells isolated from humanized mice engrafted with human PBMC and treated or not with PTCy post-transplantation
<p>Nucleotide sequence data of TCRVB sequencing performed on T cells isolated from the pre-transplantation hPBMC (donor T cells) or from mice organs at day 21 post-transplantation (injected or not with PTCy at day 3) to determine the impact of PTCy on the T cell V beta (TCRVB) receptor repertoire diversity.</p> <p>NSG mice were engrafted with human PBMC to develop xeno-GVHD and treated or not with 100 mg/kg PTCy. Spleens and lungs from 10 NSG mice per group were pooled and stained to sorted human CD4+ and CD8+ . DNA of human CD4+ and CD8+ T cells sorted from each organ and from the PBMC donor was extracted. One hundred fifty µg from each sample were used for T-cell receptor Vβ (TCRVB) deep sequencing performed by Adaptive Biotechnologies.<br> </p>
Data from: Affordable de novo generation of fish mitogenomes using amplification-free enrichment of mitochondrial DNA and deep sequencing of long fragments
<p>Biomonitoring surveys from environmental DNA make use of metabarcoding tools to describe the community composition. These studies match their sequencing results against public genomic databases to identify the species. However, mitochondrial genomic reference data are yet incomplete, only a few genes may be available, or the suitability of existing sequence data is suboptimal for species-level resolution. Here we present a dedicated and cost-effective workflow with no DNA amplification for generating complete fish mitogenomes for the purpose of strengthening fish mitochondrial databases. Two different long-fragment sequencing approaches using Oxford Nanopore sequencing coupled with mitochondrial DNA enrichment were used. One where the enrichment is achieved by preferential isolation of mitochondria followed by DNA extraction and nuclear DNA depletion ('mitoenrichment'). A second enrichment approach takes advantage of the CRISPR-Cas9 targeted scission on previously dephosphorylated DNA ('targeted mitosequencing'). The sequencing results varied between tissue, species, and integrity of the DNA. The mitoenrichment method yielded 0.17-12.33 % of sequences on target and a mean coverage ranging from 74.9 to 805-fold. The targeted mitosequencing experiment from native genomic DNA yielded 1.83-55 % of sequences on target and a 38 to 2123-fold mean coverage. This produced complete the mitogenome of species with homopolymeric regions, tandem repeats, and gene rearrangements. We demonstrate that deep sequencing of long fragments of native fish DNA is possible and can be achieved with low computational resources in a cost-effective manner, opening the discovery of mitogenomes of non-model or understudied fish taxa to a broad range of laboratories worldwide.</p>
Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing
<p>This Zenodo record contains the dataset and model weights for "Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing".</p> <p> </p> <p>This repository contains the following files:</p> <ul> <li> <p>For the human dataset by Wang et al.:</p> <ul> <li> <p>train_val_test_split.csv containing the mapping of the correct peptide by MaxQuant to either train, validation or test set</p> </li> <li> <p>psms_train_val_test.csv containing the mapping of correct PSMs (scan number, raw file and correct peptide by MaxQuant) to either train, validation or test set</p> </li> <li> <p>updated_spectralis_test_out.csv as before containing Spectralis-EA predictions and scores on test set, as well as initial peptides and scores by Casanovo and Novor and now containing also correct peptides by MaxQuant and Spectralis-scores on the combination of Casanovo and Novor sequences (column named spectralis_score_onlyRescoring)</p> </li> <li> <p>spectralis_test_out_heart_analysis.csv subset of 20220822_spectralis_test_out.csv containing only PSMs for the tissue heart with the computation of precision and recall values</p> </li> <li> <p>spectralis_test_out_pointnovo_deepnovo.csv containing predictions by DeepNovo and PointNovo with original scores and Spectralis-score, as well as correct peptides by MaxQuant</p> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>For the nine-species dataset by Tran et al.:</p> <ul> <li> <p>spectralis_ninespecies_out.csv containing spectrum identifiers, correct peptides by PEAKSDB, predicted peptides by the different de novo sequencing tools as well as original scores and Spectralis-scores for the different PSMs.</p> </li> </ul> </li> </ul>
Feces - deep sequencing data
Open the record for dataset details and reuse information.
Respiratory-triggered MRCP acquisition at 3T using a T2-weighted TSE (3D SPACE) sequence for Deep Learning-based reconstruction of MRCP
<h1>Description</h1> <p>This dataset is the sample data for MRCP_DLRecon (<a href="https://github.com/JinhoKim46/MRCP_DLRecon">GitHub</a>). <br>Place this dataset in the "Sample_data/" directory along with the "dataset.csv" file. </p> <h1>Data</h1> <p>The provided 3D MRCP data were acquired at 3T (Skyra, Siemens Healthineers AG, Erlangen) using the 3D-SPACE (3D T2w TSE) sequence for a single healthy volunteer. We provide two 2x and one 6x 3D MRCP data to ensure various training and testing scenarios. Each data in the HDF5 format contains the following structures:</p> <ul> <li>Datasets <ul> <li><strong>grappa</strong>: target data (y * x *<em> </em>slice)</li> <li><strong>kdata_raw</strong>: Raw <em>k</em>-space data (x2 or x6) (nCoil * PE * RO * slice)</li> <li><strong>kdata_fs</strong>: Fully-sampled k-space data from <strong>kdata_raw</strong> using GRAPPA (nCoil ×× PE ×× RO ×× Slice)</li> <li><strong>sm_espirit</strong>: ESPIRiT-based sensitivity maps (nCoil * y * x * slice)</li> </ul> </li> </ul> <h1>Citation</h1> <p>Please cite the following <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.70002" target="_blank" rel="noopener">paper</a> if this dataset is helpful for your research :)</p> <blockquote> <p>Kim, J., Nickel, M. and Knoll, F. (2025), Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data. NMR in Biomedicine, 38: e70002. https://doi.org/10.1002/nbm.70002 </p> </blockquote>
Data for 'NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning'
<p>The uploaded files include two archives for <a href="https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00300" target="_blank" rel="noopener">NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning</a>. The '<em>mgf_data</em>' archive contains all MGF files used in the study, while the '<em>sample_data</em>' archive includes sequencing data, clustering data generated using <code>MSCluster</code>, and XCorr calculation data computed with <code>CometX</code>, all of which were used in the research.</p>
Training data for "Identification of allelic variants in SARS-CoV-2 from deep sequencing reads"
<p>Effectively monitoring global infectious disease crises, such as the COVID-19 pandemic, requires capacity to generate and analyze large volumes of sequencing data in near real time. These data have proven essential for monitoring the emergence and spread of new variants, and for understanding the evolutionary dynamics of the virus.</p> <p>Two sequencing platforms in combination with several established library preparation strategies are predominantly used to generate SARS-CoV-2 sequence data. However, data alone do not equal knowledge: they need to be analyzed. The Galaxy community developed analysis workflows to support the <strong>identification of allelic variants (AVs) in SARS-CoV-2 from deep sequencing reads</strong>.</p> <p>These workflows allow one to identify AVs and lineages in SARS-CoV-2 genomes with variant allele frequencies ranging from 5% to 100% (i.e., they detect variants with intermediate frequencies as well.</p> <p>In this tutorial we will see how to run these workflows for the different types of input data:</p> <ul> <li>Single end data derived from Illumina-based RNAseq experiments</li> <li>Paired end data derived from Illumina-based RNAseq experiments</li> <li>Paired-end data generated with Illumina-based Ampliconic (ARTIC) protocols</li> <li>ONT fastq files generated with Oxford nanopore (ONT)-based Ampliconic (ARTIC) protocols</li> </ul> <p>To illustrate the tutorial, we took some example datasets (paired-end data generated with Illumina-based Ampliconic (ARTIC) protocols) from COG-UK, the COVID-19 Genomics UK Consortium.</p>
Deep sequencing datasets from: Witnessing the structural evolution of an RNA enzyme
<p>An RNA polymerase ribozyme that has been the subject of extensive directed evolution efforts has attained the ability to synthesize complex functional RNAs, including a full-length copy of its own evolutionary ancestor. During the course of evolution, the catalytic core of the ribozyme has undergone a major structural rearrangement, resulting in a novel tertiary structural element that lies in close proximity to the active site. Through a combination of site-directed mutagenesis, structural probing, and deep sequencing analysis, the trajectory of evolution was seen to involve the progressive stabilization of the new structure, which provides the basis for improved catalytic activity of the ribozyme. Multiple paths to the new structure were explored by the evolving population, converging upon a common solution. Tertiary structural remodeling of RNA is known to occur in nature, as evidenced by the phylogenetic analysis of extant organisms, but this type of structural innovation had not previously been observed in an experimental setting. Despite prior speculation that the catalytic core of the ribozyme had become trapped in a narrow local fitness optimum, the evolving population has broken through to a new fitness locale, raising the possibility that further improvement of polymerase activity may be achievable.</p> <p> </p>
Saved model and preprocessed data for "CRMnet:a deep learning model for predicting gene expression from large regulatory sequence datasets"
<p>Saved TUNet model and preprocessed training data for "CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets"</p> <p>To load the trained model:</p> <pre><code class="language-python">import tensorflow as tf tf.keras.models.load_model("path to the model folder")</code></pre> <p>for more information please find our repository: https://github.com/jiayuwen/CRMnet</p>
Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose
<p><span>Copy-number and point mutations form the basis for most evolutionary novelty through the process of gene duplication and divergence. While a plethora of genomic sequence data reveals the long-term fate of diverging coding sequences and their cis-regulatory elements, little is known about the early dynamics around the duplication event itself. In microorganisms, selection for increased gene expression often drives the expansion of gene copy-number mutations, which serves as a crude adaptation, prior to divergence through refining point mutations. Using a simple synthetic genetic system that allows us to distinguish copy-number and point mutations, we study their early and transient adaptive dynamics in real-time in <em>Escherichia</em> <em>coli</em>. We find two qualitatively different routes of adaptation depending on the level of functional improvement selected for: In conditions of high gene expression demand, the two types of mutations occur as a combination. Under</span><span> low gene expression demand, negative epistasis between the two types of mutations renders them mutually exclusive. Thus, owing to their higher frequency, adaptation is dominated by copy-number mutations. Ultimately, due to high rates of reversal and pleiotropic cost, copy-number mutations may not only serve as a crude and transient adaptation but also <a>constrain</a></span><span> sequence divergence over evolutionary time scales.</span></p>
MinION plasmid deep long read sequencing for sequence verification
<p>We sequenced four plasmid constructs, each with a whole MinION flowcell, for use in developing and testing a sequence verification procedure. The resulting pipeline can sequence verify plasmid constructs, generating a consensus sequence with associated confidence of each base, adhering to strict acceptance criteria . The data contained herein are a subset of the complete data; 30 fast5 files for each plasmid, to be used as example data.</p> <p> </p> <p>datHL_001519: BCRxV.TF.1<br> datHL_001521: BCRxV.VSVG.1<br> datHL_001617: BCRxV.GagPolRev.1<br> datHL_001620: BCRxV.VSVG.1_mutant</p>
Inverse folding for antibody sequence design using deep learning
<p>Model weights of the <a href="https://arxiv.org/abs/2310.19513">AbMPNN model (arXiv:2310.19513)</a> presented at the <a href="https://icml-compbio.github.io/">2023 ICML Workshop on Computational Biology</a>, and csv files with the split between train, test and validation across the <a href="https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab/">SAbDab</a> and <a href="https://zenodo.org/record/7258553">ImmuneBuilder</a> datasets.</p><p>This model is based on <a href="https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1">ProteinMPNN</a> and can be run using the corresponding code: <a href="https://github.com/dauparas/ProteinMPNN">https://github.com/dauparas/ProteinMPNN</a>.</p>
Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial – BCPP/ Ya Tsie trial
Open the record for dataset details and reuse information.
Deep sequencing datasets from: Witnessing the structural evolution of an RNA enzyme
Open the record for dataset details and reuse information.
Data from: Affordable de novo generation of fish mitogenomes using amplification-free enrichment of mitochondrial DNA and deep sequencing of long fragments
Open the record for dataset details and reuse information.
Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose
Open the record for dataset details and reuse information.
Data from: Deep sequencing reveals extensive variation in the gut microbiota of wild mosquitoes from Kenya.
The mosquito midgut is a hostile environment that vector-borne parasites must survive in order to be transmitted. Commensal bacteria in the midgut can reduce the ability of mosquitoes to transmit disease, either by having direct anti-parasite effects or by stimulating basal immune responses of the insect host. As different bacteria have different effects on parasite development, the composition of the bacterial community in the mosquito gut is likely to affect the probability of disease transmission. We investigated the diversity of mosquito gut bacteria in the field using 454 pyrosequencing of 16S rRNA to build up a comprehensive picture of the diversity of gut bacteria in 8 mosquito species. We found that mosquito gut typically has a very simple gut microbiota that is dominated by a single bacterial taxon. Although different mosquito species share remarkably similar gut bacteria, individuals in a population are extremely variable and can have little overlap in the bacterial taxa present in their guts. This may be an important factor in causing differences in disease transmission rates within mosquito populations.
MetaChick: characterization of the chicken caecal metagenome by deep shotgun sequencing
<p></p><h1>Data sources</h1><br>This dataset was constructed using the samples of the MetaChick project (phase 1) corresponding to the cecal content of 340 animals. Sequencing data and associated metadata have been submitted to INSDC (bioproject: PRJEB38174).<br><h1>Sequencing data QC and metagenomic assembly</h1><br>First, sequencing adapters removal and read trimming was performed with fastxtend. Reads mapped on the host genome (GRCg7b GCA_016699485.1) with bowtie2 were removed with samtools. Finally, metagenomic assembly was performed with metaSPAdes v3.14.1. Contigs of less than 1500 bp were removed.<br><h1>MAGs recovery</h1><br>MAGs were generated with MetaBAT 2 (multi-coverage mode) and MAGs quality was assessed with CheckM. MAGs with completeness < 70% or contamination > 5% or N50 < 8Kb were discarded. Pairwise Average Nucleotide Identity (ANI) was computed for all recovered MAGs with fastANI and dereplication at species level (ANI cutoff = 95%).<br><h1>Non-redundant gene catalog</h1><br>Genes were predicted on all contigs from metagenomic assemblies with Prodigal (parameters : -m -p meta). Genes were pooled and clustered with cd-hit-est (parameters -c 0.95 -aS 0.90 -G 0 -d 0 -M 0 -T 0) by choosing those from the longest contigs as representatives.<br><h1>MSPs recovery</h1><br>A raw gene abundance table (13,6M genes quantified in 340 samples) was generated with meteorMeteor. Then, co-abundant genes were binned in Metagenomic Species Pan-genomes (MSPs, i.e. gene clusters that likely belong to the same microbial species) using MSPminer.<br><h1>MAGs and MSPs taxonomic annotation</h1><br>Dereplicated MAGs were annotated with GTDB-Tk based on GTDB r214. Then, MAGs taxonomic annotation was propagated to the corresponding MSPs.<br><h1>Construction of the phylogenetic tree</h1><br>39 universal phylogenetic markers genes were extracted from the dereplicated MAGs with fetchMGs. Then, the markers were separately aligned with MUSCLE. The 40 alignments were merged and trimmed with trimAl (parameters: -automated1). Finally, the phylogenetic tree was computed with FastTreeMP (parameters: -gamma -pseudo -spr -mlacc 3 -slownni).<h1>Mapping rate distribution across public cohorts</h1>We generated mapping rate distribution plots using Meteor2 (default parameters), comparing performance between: PRJEB38174 (cohort used in catalogue assembly) and PRJEB29033, PRJEB33338, PRJEB53667 (independent cohort not used in assembly).<p></p>
MicroReset: characterization of the rabbit (Oryctolagus cuniculus) fecal metagenome and resistome by deep shotgun sequencing
<p></p><h1>Data source</h1><br>The dataset was generated from 30 rabbit fecal samples subjected to deep shotgun metagenomic sequencing. The sequencing data is available under the BioProject PRJEB50625.<br>Metagenomic Assembly<br>Raw sequencing reads were first pre-processed using fastp for adapter removal and quality trimming. Host-derived reads were filtered out by mapping to the rabbit reference genome (GCF_000001635.27) using Bowtie2 and removing mapped reads with Samtools. Each sample was individually assembled using metaSPAdes. Contigs shorter than 1,500 bp were excluded from downstream analysis.<br><h1>MAG Recovery</h1><br>Reads from each sample were mapped to all 30 assemblies (30×30 mappings) using Bowtie2. The resulting alignments were sorted and indexed with Samtools. Contig coverage across all samples was computed using `jgi_summarize_bam_contig_depths`. Binning was performed with MetaBAT 2 and SemiBin v1.3. MAG quality was assessed with CheckM. Only high-quality MAGs (≥70% completeness, ≤5% contamination, N50 ≥ 8 kb) were retained.<br>Non-Redundant Gene Catalog<br>Gene prediction was carried out using Prodigal on all contigs from the current study (with `-m -p meta`). Genes shorter than 90 bp or lacking start/stop codons were discarded. The remaining genes from both sources were pooled and clustered using CD-HIT-EST (parameters: `-c 0.95 -aS 0.90 -G 0 -d 0 -M 0 -T 0`). The longest contigs were used to select representative genes.<br><h1>MSP Recovery</h1><br>Shotgun reads from the 30 samples were aligned to the non-redundant gene catalog using the Meteor suite, generating a gene abundance matrix (5.7 million genes × 30 samples). Co-abundant genes were grouped into 1,053 Metagenomic Species Pan-genomes (MSPs) using MSPminer.<br><h1>Taxonomic Annotation of MSPs</h1><br>MAGs representing each species were taxonomically annotated using GTDB-Tk with GTDB release r214. The resulting taxonomy was propagated to the corresponding MSPs.<br><h1>Phylogenetic Tree Construction</h1><br>A set of 39 universal phylogenetic marker genes was extracted from the 1,053 MSPs (or their corresponding MAGs, when available) using fetchMGs. Each marker was independently aligned using MUSCLE, and the alignments were concatenated and trimmed using trimAl (parameter: `-automated1`). A maximum-likelihood phylogenetic tree was constructed with FastTreeMP (parameters: `-gamma -pseudo -spr -mlacc 3 -slownni`).<h1>Mapping rate distribution across public cohorts</h1>We generated mapping rate distribution plots using Meteor2 (default parameters) for PRJEB50625 (cohort used in catalogue assembly).<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.