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FIGURE 1 in Population genetics of the endangered catfish Pseudoplatystoma magdaleniatum (Siluriformes: Pimelodidae) based on species-specific microsatellite loci
FIGURE 1 | Sampling sites of Pseudoplatystoma magdaleniatum in the Magdalena-Cauca basin.
Dataset 1. Functional genomic profiling of O-GlcNAc reveals its context-specific interplay with RNA polymerase II.
<p>This dataset is used in the bioformatic methods of the article <strong>Functional genomic profiling of O-GlcNAc reveals its context-specific interplay with RNA polymerase II </strong>(doi: 10.1186/s13059-025-03537-2). The corresponding code can be found in the repository https://github.com/descostesn/chromglycomethods.git</p>
Shared evolutionary origin and clade-specific signatures of symbiosis in lifestyle distinctive fungi
<p>Data repository established for the appendix dataset documented in Scarlet M. Au’s MPhil in Biological Science (Plant Sciences) thesis, submitted to the University of Cambridge, UK. </p> <p><strong>Appendix A.</strong> List of 182 species from the Mucoromycota subphylum</p> <p><strong>Appendix B.</strong> List of 535 BUSCO single copy genes identified for single copy phylogenies. </p> <p>1) Appendix B-1 contains BUSCO genes mostly annotated relative to the <em>Rhizophagus irregularis </em>DAOM_181602_v1.0. For genes that were missing, annotations were drawn from the Lyc-1 and the <em>Thamnidium elegans</em> genomes. </p> <p>2) Appendix B-2 contains BUSCO genes annotated relative to the <em>Rhizophagus irregularis </em>DAOM_181602_v1.0 genome. </p> <p>3) Appendix B-3 contains BUSCO genes annotated relative to the Lyc-1 genome.</p> <p>4) Appendix B-4 contains BUSCO genes annotated relative to the <em>Thamnidium elegans</em> genome. </p> <p><strong>Appendix C.</strong> Hierarchical clustering by orthogroup absence/presence and abundance. </p> <p>1) Full hierarchical clustering by row (species) and columns (orthogroups) for binary matrix. </p> <p>2) Full hierarchical clustering by row (species) and columns (orthogroups) for normalised matrix. </p> <p>3) Full hierarchical clustering by row (species) only for binary matrix. </p> <p>4) Full hierarchical clustering by row (species only) for normalised matrix.</p> <p><strong>Appendix D. </strong>36 out of 72 orthogroups shared between MFRE and AM fungal genomes contain known functional annotations. </p> <p> </p>
Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks
<p>Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks” </p> <p>Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle </p> <p>Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health</p> <p>Description: </p> <p>This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include:</p> <ul> <li> <p>all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study. </p> </li> <li> <p>all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration </p> </li> <li> <p>precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction.</p> </li> <ul> <li> <p>The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds</p> </li> <li> <p>For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X. </p> </li> <li> <p>We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds”</p> </li> <li> <p>The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables. </p> </li> </ul> <li> <p>WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors. </p> </li> </ul>
Effect of ion-specific water structures at metal surfaces on hydrogen production
<p>Here lies the tabulated data used to create the Figures for the Nature Communications manuscript NCOMMS-23-54843B titled "Effect of ion-specific water structures at metal surfaces on hydrogen production".</p>
Virus Specific Markers
<p>CC by 4.0, <strong>Antonio Camargo</strong>, I am only redistributing part of the amazing work by A. Camargo and others, as described in their publication:</p> <p>Camargo, A. P., Roux, S., Schulz, F., Babinski, M., Xu, Y., Hu, B., Chain, P. S. G., Nayfach, S., & Kyrpides, N. C. — <em>Nature Biotechnology</em> (2023), DOI: 10.1038/s41587-023-01953-y.</p> <p> </p> <p>--</p> <p>This is the virus specific markers data set, the fraction of markers that are labeled as being at least specific for viruses, therefore labeled as `*V' or `V*' in the MMSeqs2 profile database file `genomad db v1.7.tar.gz' from https://zenodo.org/records/10594875, CC by 4.0, Antonio Camargo.<br>MMseqs2 Version: 15.6f452 was used to generate the database using the subcommand `createdb'.</p>
Data table 5 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<div>An Excel table contains a list of proteins identified by MS from pull-down experiment using cerebellar cortex lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 2_cortex isogenic mouse IgG control dynabeads.xlsx" </div> <div> </div>
Data table 4 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<div>An Excel table contains a list of proteins identified by MS from a pull-down experiment using cerebellum lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 3_cerebellum isogenic mouse IgG control dynabeads.xlsx<span><br></span></div>
Data table 2 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<p>An excel table containing a list of proteins identified by MS from samples after a pull-down experiment using cerebral cortex lysates with Dyna beads coated with control isogenic mouse IgG. The proteins from the list were considered false positive interactors of ataxin-3 in the cortex.</p>
Data table 1 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<p>An excel table contains LFQ intensity and other raw MS data for proteins identified in fractions 3,4,5 (i), 11, 12, 13, (ii) 18, 19, and 20 (iii) from ki150 and ki21 model brains. These fractions showed enrichment in ATXN3 protein. The fractions are visualized in Figure 4 (<a href="http://doi.org/10.3389/fnmol.2023.1122308" target="_blank" rel="noopener">doi.org/10.3389/fnmol.2023.1122308</a>)</p>
Silva, Harder et al. Detectability of runs of homozygosity is influenced by analysis parameters and population-specific demographic history Data
<p>Dataset used in the "Detectability of runs of homozygosity is influenced by analysis parameters and population-specific demographic history" paper by Silva, Harder et al.</p> <p>This dataset includes Fasta files for Simulated dataset and VCF files for both Empirical and Simulated datasets.</p>
Specific Humidity at 10-m
Open the record for dataset details and reuse information.
Specifying cellular context of transcription factor regulons for exploring context-specific gene regulation programs
<p>This repository contains the raw and processed files used in Minaeva et al. 2024.</p> <p>In this version, we have revised the regulon construction pipeline and expanded the dataset to cover 40 common cell lines.</p> <p>The code used to generate these files is available at <a href="https://github.com/LappalainenLab/chip_seq_regulons" target="_new" rel="noreferrer">GitHub - LappalainenLab/chip_seq_regulons</a>.</p> <p>The descriptions of the files contained within each subdirectory are as follows:</p> <h3>1-dataset_stats</h3> <ul> <li><code>per_gene_stats_{approach}_{cell_line}.tsv</code>: Number of TFs regulating a gene according to the respective approach (S2Mb, M2Kb, or S2Kb) in a given cell line.</li> <li><code>per_tf_stats_{approach}_{cell_line}.tsv</code>: Number of target genes regulated by a TF according to the respective approach (S2Mb, M2Kb, or S2Kb) in a given cell line.</li> </ul> <h3>1-network_enrichment</h3> <ul> <li><code>enrich_scores_remap_all_tfs_K562.tsv</code>: Results of fitting logistic regression for testing the enrichment of the K562 regulon in other biological networks (PPI, coexpression, experimental trans-networks).</li> </ul> <h3>2-plot_decoupler_comparison_benchmark_across_cells</h3> <ul> <li><code>{cell_line}_comparison_benchmark.tsv</code>: Results of benchmarking S2Mb, M2Kb, CollecTri, Dorothea, ChIP-Atlas, RegNet, and TRRUST regulons using the decoupler package and the KnockTF database. Cell lines considered are K562, HepG2, and MCF7 (see Methods for benchmarking pipeline details).</li> </ul> <h3>2-plot_decoupler_filter_benchmark_across_methods</h3> <ul> <li><code>{cell_line}_filtering_benchmark.tsv</code>: Results of benchmarking S2Mb, M2Kb, and S2Kb regulons with different filters applied using the decoupler package and the KnockTF database. Cell lines considered are K562, HepG2, and MCF7 (see Methods for benchmarking pipeline details).</li> </ul> <h3>3-tf_activity</h3> <ul> <li><code>aml_k562_activity_{regulon}_sc.tsv</code>: Results of TF activity analysis based on a respective regulon between healthy hematopoietic stem cells (HSCs) and abnormal AML progenitor cells following the decoupler pipeline. Regulons considered are K562-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>aml_activity_estimates_hsc_sc.tsv</code>: Summary of the TF activity analysis for statistically significantly dysregulated TFs between healthy HSCs and abnormal AML progenitor cells across regulons.</li> <li><code>aml_dhsc_ahsc_activity_{regulon}_sc.tsv</code>: Results of TF activity analysis based on a respective regulon between leukemic activated and dormant HSCs following the decoupler pipeline. Regulons considered are K562-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>aml_activity_estimates_dhsc_ahsc_sc.tsv</code>: Summary of the TF activity analysis for statistically significantly dysregulated TFs between leukemic activated and dormant HSCs across regulons.</li> <li><code>bc_bas_activity_{regulon}.tsv</code>: Results of TF activity analysis based on a respective regulon between healthy epithelial breast cells and malignant epithelial cells from basal breast cancer following the decoupler pipeline. Regulons considered are MCF7-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>bc_activity_estimates_bas.tsv</code>: Summary of the TF activity analysis for statistically significantly dysregulated TFs between healthy epithelial breast cells and malignant epithelial cells from basal breast cancer across regulons.</li> <li><code>bc_lum_activity_{regulon}.tsv</code>: Results of TF activity analysis based on a respective regulon between healthy epithelial breast cells and malignant epithelial cells from luminal type A breast cancer following the decoupler pipeline. Regulons considered are MCF7-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>bc_activity_estimates_lum.tsv</code>: Summary of the TF activity analysis for statistically significantly dysregulated TFs between healthy epithelial breast cells and malignant epithelial cells from luminal type A breast cancer across regulons.</li> <li><code>hep_activity_{regulon}.tsv</code>: Results of TF activity analysis based on a respective regulon between neoplastic and healthy liver cells following the decoupler pipeline. Regulons considered are HepG2-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>hep_activity_estimates.tsv</code>: Summary of the TF activity analysis for statistically significantly dysregulated TFs between neoplastic and healthy liver cells across regulons.</li> </ul> <h3>3-tf_disease_enrichment</h3> <ul> <li><code>aml_{database}_enrich_{regulon}_hsc_sc.tsv</code>: Results of enrichment analysis of dysregulated TFs identified based on a respective regulon between healthy HSCs and abnormal AML progenitor cells following the decoupler pipeline. Databases considered are COSMIC, DisGeNet, OMIM, and KEGG. Regulons considered are K562-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>aml_{database}_enrich_{regulon}_dhsc_ahsc_sc.tsv</code>: Results of enrichment analysis of dysregulated TFs identified based on a respective regulon between leukemic activated and dormant HSCs following the decoupler pipeline. Databases considered are COSMIC, DisGeNet, OMIM, and KEGG. Regulons considered are K562-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>bc_{database}_enrich_{regulon}_bas.tsv</code>: Results of enrichment analysis of dysregulated TFs identified based on a respective regulon between healthy epithelial breast cells and malignant epithelial cells from basal breast cancer following the decoupler pipeline. Databases considered are COSMIC, DisGeNet, and OMIM. Regulons considered are MCF7-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>bc_{database}_enrich_{regulon}_lum.tsv</code>: Results of enrichment analysis of dysregulated TFs identified based on a respective regulon between healthy epithelial breast cells and malignant epithelial cells from luminal type A breast cancer following the decoupler pipeline. Databases considered are COSMIC, DisGeNet, and OMIM. Regulons considered are MCF7-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> <li><code>hep_{database}_enrich_{regulon}.tsv</code>: Results of enrichment analysis of dysregulated TFs identified based on a respective regulon between neoplastic and healthy liver cells following the decoupler pipeline. Databases considered are COSMIC, DisGeNet, OMIM, and KEGG. Regulons considered are HepG2-specific ChIP-Atlas and M2Kb regulons, and generalized CollecTri regulon.</li> </ul> <h3>regulons</h3> <ul> <li><code>{cell_line}_regulon.tsv</code>: S2Mb, M2Kb, and S2Kb regulons generated in this study with all acquired annotations (see Methods for details).</li> </ul> <p>External regulons used for comparison. Cell lines considered are K562, HepG2, MCF7, and GM12878:</p> <ul> <li><code>ChIP-Atlas_target_genes_{cell_line}.tsv</code>: Customized ChIP-Atlas regulons (see Methods for details).</li> <li><code>Revised_Supplemental_Table_S3_Normal.csv</code>: Dorothea regulon collected from supplementary materials of Garcia-Alonso et al. (2019).</li> </ul> <h3>s3-network_enrichment</h3> <ul> <li><code>enrich_scores_remap_all_tfs_{cell_line}.tsv</code>: Results of fitting logistic regression for testing the enrichment of cell-line-specific regulons in PPI networks (see Methods and corresponding GitHub repository for details). Cell lines considered are K562, HepG2, MCF7, and GM12878.</li> </ul> <p> </p>
Supplemental Dataset Excel files and Source Data Excel file for "START domains generate paralog-specific regulons from a single network architecture"
<p>Supplemental Dataset Excel files and Source Data Excel file for "START domains generate paralog-specific regulons from a single network architecture" in Nat Comms</p>
High-throughput behavioural phenotyping of 25 C. elegans disease models including patient-specific mutations
<p>This repository contains: all code, phenomic data, extracted features, calculated stats, normalised z-scores and timerseries data for all of the disease mutant phenologs and data in our paper: High-throughput behavioural phenotyping of 25 C. elegans disease models including patient-specific mutations.</p>
Annotated Dataset for Named Entity Recognition and Relation Extraction in French Building Technical Specifications (BTS)
<p>This dataset contains 233 raw requirements extracted from French Building Technical Specifications (BTS), referred to as "<a href="https://www.aglo.ai/cctp/#:~:text=Le%20CCTP%20(Cahier%20des%20Clauses%20Techniques%20Particuli%C3%A8res)%20est%20un%20document,code%20du%20march%C3%A9%20public%20donc."><em>Cahier des Clauses Techniques Particulières (CCTP)</em></a>", specifically focused on carpentry ("<em>lot menuiserie</em>") in public French construction projects. The requirements have been collected from 72 CCTP documents, resulting in a total of 19,725 sentences and 651,948 words.</p> <p>The dataset has been annotated using <a title="Open-source text annotation tool" href="https://github.com/doccano/doccano">Doccano </a>for Named Entity Recognition (NER) and Relation Extraction (RE). The annotations involve identifying entities and the relationships between them within the domain of building requirements. This dataset is intended for research on Natural Language Processing (NLP) models for Requirements Engineering (RE) in the Architecture, Engineering, and Construction (AEC) sector. Potential applications include requirements extraction, compliance analysis, and knowledge management in construction.</p> <p>The dataset includes the following components:</p> <ol> <li><strong>CCTP Documents</strong>: The original CCTP files from which the raw requirements were extracted.</li> <li><strong>Annotated Dataset</strong>: A JSONLines file containing the annotated dataset, including labels for Named Entity Recognition (NER) and Relation Extraction (RE).</li> </ol> <p>Key features of the dataset:</p> <ul> <li>Language: French</li> <li>Number of requirements: 233</li> <li>Number of sentences: 19,725</li> <li>Number of words: 651,948</li> <li>Annotation tasks: Named Entity Recognition (NER) and Relation Extraction (RE)</li> </ul> <p>This dataset is relevant for NLP research focused on structured information extraction from domain-specific texts in the construction industry.</p>
DT-LEMBAS cell line specific models
<p>This contains 33 ensembles of 50 DT-LEMBAS models each. The models were trained on 33 cell line transcriptomic data from the L1000 dataset.</p>
Laptop Ratings and Specifications Extracted from PCBox
<p>This dataset contains detailed information on 100 laptops extracted from the online store PCBox using web scraping techniques. The data includes the product name, price, category, brand, average rating, number of reviews, as well as the highest and lowest ratings with associated review comments. This information enables analysis and comparison of different laptop models based on price, popularity, and user ratings.</p>
Data set for Site Specific Lewy Pathology Circuits
<p>Processed dataset and example code related to manuscript titled: Site-Specific Seeding of Lewy Pathology Induces Distinct Pre-Motor Cellular and Dendritic Vulnerabilities in the Cortex</p>
Table 1 in Demodex bialoviensis sp. nov. (Acariformes, Demodecidae) a new, specific parasite of the European bison Bison bonasus (Artiodactyla, Bovidae)
<p><b>Table 1</b> Body size (micrometers) for adults and deutonymphs of <i>Demodex bialoviensis</i> sp. nov.</p><table><tbody><tr><th>Morphologic features</th><th>Males (n =12)</th><th>Females (n =</th><th>Deutonymph (n</th></tr></tbody><tbody><tr><th></th><td></td><td>34)</td><td>=5)</td></tr><tr><th>Length of</th><td>17 (15–20), SD 1</td><td>22 (20–26), SD 2</td><td>16 (13–20), SD 3</td></tr><tr><th>gnathosoma</th><td></td><td></td><td></td></tr><tr><th>Width of gnathosoma</th><td>15 (13–20), SD 2</td><td>21 (17–25), SD 2</td><td>14 (11–17), SD 3</td></tr><tr><th>(at base)</th><td></td><td></td><td></td></tr><tr><th>Length of podosoma</th><td>50 (45–58), SD 4</td><td>62 (55–68), SD 4</td><td>53 (50–58), SD 3</td></tr><tr><th>Width of podosoma</th><td>31 (30–35), SD 2</td><td>35 (30–40), SD 2</td><td>36 (35–38), SD 1</td></tr><tr><th>Length of</th><td>109 (93–125),</td><td>154 (125–178),</td><td>133 (125–145),</td></tr><tr><th>opisthosoma</th><td>SD 11</td><td>SD 13</td><td>SD 9</td></tr><tr><th>Width of opisthosoma</th><td>29 (25–33), SD 2</td><td>33 (30–39), SD 3</td><td>31 (30–33), SD 1</td></tr><tr><th>Aedeagus</th><td>21 (18–29), SD 3</td><td>–</td><td>–</td></tr><tr><th>Vulva</th><td>–</td><td>12 (10–17), SD 2</td><td>–</td></tr><tr><th>Total length of body</th><td>176 (158–198),</td><td>239 (200–268),</td><td>202 (188–218),</td></tr><tr><th></th><td>SD 13</td><td>SD 15</td><td>SD 12</td></tr></tbody></table>
ScienceDex guides
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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.