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.
19,342
datasets available to search
ShareScore release 0.9.0
Dataset results
19,342 results for “sequence”
Fig. 7 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 7. Consensus secondary structure of ITS2 helices II and III in various higher litostomatean taxa.
Fig. 2 in Morphology and Sequence Data of Mexican Populations of the Ciliate Parasite of Marine Fishes Trichodina rectuncinata (Ciliophora: Trichodinidae)
Fig. 2. Photomicrographs of silver-impregnated adhesive discs and diagrammatic drawings of the denticles of respective morphotypes studied in the present paper; a and a'. From Enneanectes reticulatus, San Carlos, Sonora. b and b'. From Enneanectes reticulatus, San Carlos, Sonora. c and c'. From Tomicodon zebra, Zihuatanejo, Guerrero. d and d'. From Tomicodon zebra, Cuatunalco, Oaxaca.
Fig. 3 in Morphology and Sequence Data of Mexican Populations of the Ciliate Parasite of Marine Fishes Trichodina rectuncinata (Ciliophora: Trichodinidae)
Fig. 3. Bayesian inference tree of sequences of the 18S gene of trichodinid species of the genus Trichodina and Trichodinella, emphasizing on Trichodina rectuncinata. Numbers near internal nodes show the support value. Codes: ♦ Cuatunalco; * Zihuatanejo; ● San Carlos.
Fig. 1 in Morphology and Sequence Data of Mexican Populations of the Ciliate Parasite of Marine Fishes Trichodina rectuncinata (Ciliophora: Trichodinidae)
Fig. 1. Map showing the location of Mexico, and localities where populations of Trichodina rectuncinata were obtained.
Fig. 2a in Prevalence of a new genetic group, MEAM-K, of the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) in Karnataka, India, as evident from mtCOI sequences
Fig. 2a. Phylogenetic tree showing the relationship of the Bemisia tabacimt mtCOI sequences collected in this study to consensus sequences of Dinsdale et al. (2010). Bayesian analyses were performed employing MrBayes under the best-fit model GTR+I+G of molecular evolution for 20 million generations and 25% discarded as burn-in. Posterior probabilities are shown above the branches and maximum likelihood scores from RaxML indicated below the branches. Asia-I condensed is shown in Fig. 2b.
Fig. 1 in Prevalence of a new genetic group, MEAM-K, of the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) in Karnataka, India, as evident from mtCOI sequences
Fig. 1. Map indicating different locations of Bemisia tabaci sampling and distribution of genetic groups in Karnataka, India.
Fig. 2b. Phylogenetic tree indicating the Asia-I in Prevalence of a new genetic group, MEAM-K, of the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) in Karnataka, India, as evident from mtCOI sequences
Fig. 2b. Phylogenetic tree indicating the Asia-I group, which was the most abundant genetic group in this study.
Protein haplotype sequences obtained by ProHap from the 1000 Genomes Project data set
<p>Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the 1000 Genomes Project, aligned with the GRCh38 genome build (<a href="https://www.internationalgenome.org/data-portal/data-collection/grch38">https://www.internationalgenome.org/data-portal/data-collection/grch38</a>). We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts. The complete configuration file for each ProHap run is attached to this repository.</p> <p>This data set contains six compressed directories, five representing the superpopulations included in the 1000 Genomes Project (<a href="https://catalog.coriell.org/1/NHGRI/Collections/1000-Genomes-Project-Collection/1000-Genomes-Project">https://catalog.coriell.org/1/NHGRI/Collections/1000-Genomes-Project-Collection/1000-Genomes-Project</a>), and one created using all the samples included in the 1000 Genomes data set:</p> <ul> <li>AFR - African</li> <li>AMR - American</li> <li>EUR - European</li> <li>SAS - South Asian</li> <li>EAS - East Asian</li> <li>ALL - all participants in the 1000 Genomes Project</li> </ul> <p>Each of the directories contains the following files:</p> <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap, using alleles with at least 1 % frequency within the selected population</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>The file is provided in two formats - full and simplified. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the simplified fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> <p>For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>.</p> <p>For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>.</p> <p>When using these databases in your publication, please cite: Vašíček, J., Kuznetsova, K.G., Skiadopoulou, D. <em>et al.</em> ProHap enables human proteomic database generation accounting for population diversity. <em>Nat Methods</em> (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a></p>
RDP Classifier training files for 16S rRNA sequences from GTDB
<p>16S rRNA gene sequences from the <a href="https://gtdb.ecogenomic.org/">Genome Taxonomy Database</a> (GTDB release 220) were used to retrain the <a href="https://github.com/rdpstaff/classifier">RDP Classifier</a> (version 2.13). Two sets of training files are provided:</p> <ul> <li><code>genus.zip</code> - Genus level</li> <li><code>species.zip</code> - Species level</li> </ul> <p>The code in <code>prepare_files.R</code> was used to prepare the GTDB sequence and taxonomy files for retraining the RDP Classifier. Notes:</p> <ul> <li>Steps to retrain the RDP Classifier are adapted from <a href="https://john-quensen.com/tutorials/training-the-rdp-classifier/">https://john-quensen.com/tutorials/training-the-rdp-classifier/</a></li> <li>Python scripts (lineage2taxTrain.py and addFullLineage.py) are available at <a href="https://github.com/rdpstaff/classifier/issues/18">https://github.com/rdpstaff/classifier/issues/18</a></li> <li>The first 1000 training sequences (<code>train_nodups_1000.fasta</code>) are used for benchmarking the classification accuracy (see results at end of <code>prepare_files.R</code>).</li> </ul>
Simulated exome-sequencing data for a family study of lymphoid cancer
<p>This repository contains all the data files for a simulated exome-sequencing study of 150 families, ascertained to contain at least four members affected with lymphoid cancer. Please note that previous versions of this repository omitted a key file linking the genotypes of individuals to their family and individual IDs; this file, geno_key.txt, is now included. All other files remain the same as in previous versions.</p> <p>The simulated data can be found in the files section below. The files are:</p> <ol> <li>SLiM_output.txt - contains the SLiM-simulated, exome-wide, SNV data generated under an American-admixture demographic model, for the American-admixed sub-population only.</li> <li>SLiM_output_chr8&9.txt - contains the SLiM-simulated data above for all source populations as well as the American-admixed sub-population, but only for chromosomes 8 and 9.</li> <li>sample_info.txt - contains pedigree information of all the disease-affected individuals and individuals connecting them along a line of descent, for all 150 ascertained pedigrees.</li> <li>Genotypes.zip - a zipfile that contains 22 text files of genotypes for each chromosome. The genotypes are for simulated single-nucleotide variants on the exome and are in gene-dosage format. </li> <li>geno_key.txt – a plain-text file that links the genotyped individuals to their family and individual IDs.</li> <li>SNVmaps.zip - a zipfile that contains 22 text files giving the single-nucleotide variant information for each chromosome. </li> <li>familial_cRV.txt - contains the familial causal rare variants for all 150 ascertained pedigrees.</li> <li>study_peds.txt - contains the 150 pedigrees ascertained to contain four or more relatives affected with lymphoid cancer.</li> <li>PLINKfiles.zip - a zipfile that contains PLINK .fam, .bim and .bed files for all 22 of the chromosomes.</li> </ol> <p>All the scripts used to generate these data can be found in the GitHub repository archived at <a href="../records/12694914">https://zenodo.org/records/12694914</a></p> <p>We have also uploaded one intermediate .Rdata file, Chromwide.Rdata, to save the user substantial time when running the associated RMarkdown script for the simulation. We recommend loading Chromwide.Rdata into your R work-space rather than generating it from scratch.</p>
FIGURE 1 in Eocene planktonic foraminifera from the north Eastern Desert, Egypt: Biostratigraphic, paleoenvironmental and sequence stratigraphy implications
FIGURE 1. Location and geological maps of the studied sections (Modified after Saber and Salama 2017).
Data from: Sequence-based detection of emerging antigenically novel influenza A viruses
<p>The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe NIAViD, an unsupervised machine learning tool, adept at identifying these transitions, using HA1 sequence and associated physicochemical properties. NIAViD, performed with 88.9% (95% CI, 56.5%–98.0%) and 72.7% (95% CI,43.4%– 90.3%) sensitivity in training and validation respectively, outperforming the uncalibrated null model – 33.3% (95% CI,12.1%–64.6%) and does not require the need for potentially biased, time-consuming and costly laboratory assays. The pivotal role of Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modeling of hemagglutinin inhibition titers. Our approach holds immense promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.</p>
Fig. 4 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)
Fig. 4. Sequence alignment of putative lectin from Scorpaena plumieri. Alignment of a lectin-like EST in silico translated sequence from S. plumieri (ClustalW2 EBI) with fish-egg lectin from Oplegnathus fasciatus (BAL618145), Dicentrarchus labrax (CBK52298), Maylandia zebra (XP_004574029), and Oreochromis niloticus (XP003443389). The recombinant clone was isolated with antibody fraction derived from S. plumieri venom. * identifies and identical residue;: identifies a conserved residue. Underlined residues represent invariable sites, underlined IRLS = N-acetylation site.
Fig. 3 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)
Fig. 3. The classification of EST from Scorpaena plumieri based on their putative fractions. Three-hundred fifty-six EST edited sequences were initially analyzed with Blast and Swiss protein databanks. The consensus sequence was attributed a function based on the strongest match.
Fig. 2 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)
Fig. 2. Agarose gel electrophoresis of DNA isolated from clones. White colonies containing insert were grown and the plasmidial DNA isolated and digested with EcoRI enzyme. An aliquot from each clone (1-27) was electrophoresed on 1% Agarose gel and stained with ethidium bromide.
Fig. 1 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)
Fig. 1. Agarose- formaldehyde electrophoresis of RNA from Scorpaena plumieri. A) 1) 2 µg of E. coli tRNA; 2) 2 µg de rRNA de Rattus norvegicus; 3) and 4) 2 µg total RNA from S. plumieri spine gland. B) 1) 2 µg de total RNA from S. plumieri; 2) the same sample incubated 2 h a 37ºC before electrophoresis.
Illumina Sequencing Data for "Elucidating human gut microbiota interactions that robustly inhibit diverse Clostridioides difficile strains across different nutrient landscapes"
<p>Illumina Sequencing Data for Sulaiman et al., "Elucidating human gut microbiota interactions that robustly inhibit diverse Clostridioides difficile strains across different nutrient landscapes".</p>
FIG. 4 in A journey through Cyanobacteria in Brazil: a review of novel genera and 16S rRNA sequences
FIG. 4. — Phylogenetic analysis of Brasilonema Fiore, Sant'Anna, de Paiva Azevedo, Komarek, Kaštovský, Sulek & Lorenzi and other Cyanobacteria reference strains. Brazilian strains are in bold.
FIG. 5 in A journey through Cyanobacteria in Brazil: a review of novel genera and 16S rRNA sequences
FIG. 5. — Phylogenetic analysis of Capilliphycus T.A.Caires, Sant'Anna & J.M.Nunes and other Cyanobacteria reference strains. Brazilian strains are in bold.
FIG. 3 in A journey through Cyanobacteria in Brazil: a review of novel genera and 16S rRNA sequences
FIG. 3. — Phylogenetic reconstruction of 16S rDNA of Brazilian strains and reference strains of Cyanobacteria. The strains marked in green are Brazilian genera. The stripe colors represent taxonomical orders.
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.