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zenodo36/100

Pleiotropic Expression Quantitative Trait Loci Are Enriched in Enhancers and Transcription Factor Binding Sites and Impact More Genes

<p>This dataset comprises two files that accompany the article (link to be added upon publication).</p> <h2>1. gwas2eqtl_colocalization_full.tar.gz</h2> <p>This file contains the complete colocalization dataset generated using the code from the following GitHub repository: gwas2eqtl. This dataset is used as input for the pleiotropic eQTL analysis available at gwas2eqtl_pleiotropy, which produces the figures in the article.</p> <p><strong>File structure:</strong></p> <blockquote> <p>.<br>└── gwas417<br>&nbsp; &nbsp; └── coloc<br>&nbsp; &nbsp; &nbsp; &nbsp; ├── ebi-a-GCST000998<br>&nbsp; &nbsp; &nbsp; &nbsp; │ &nbsp; └── pval_5e-08<br>&nbsp; &nbsp; &nbsp; &nbsp; │ &nbsp; &nbsp; &nbsp; └── r2_0.1<br>&nbsp; &nbsp; &nbsp; &nbsp; │ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── kb_1000<br>&nbsp; &nbsp; &nbsp; &nbsp; │ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── window_1000000<br>&nbsp; &nbsp; &nbsp; &nbsp; │ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── Alasoo_2018_ge_macrophage_IFNg+Salmonella.tsv<br>&nbsp; &nbsp; &nbsp; &nbsp; │ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── Alasoo_2018_ge_macrophage_IFNg.tsv<br>...</p> </blockquote> <p>Each TSV file contains the following columns:</p> <blockquote> <p>chrom &nbsp; &nbsp;pos &nbsp; &nbsp;rsid &nbsp; &nbsp;ref &nbsp; &nbsp;alt &nbsp; &nbsp;eqtl_gene_id &nbsp; &nbsp;gwas_beta &nbsp; &nbsp;gwas_pval &nbsp; &nbsp;gwas_id &nbsp; &nbsp;eqtl_beta &nbsp; &nbsp;eqtl_pval &nbsp; &nbsp;eqtl_id &nbsp; &nbsp;PP.H4.abf &nbsp; &nbsp;SNP.PP.H4 &nbsp; &nbsp;nsnps &nbsp; &nbsp;PP.H3.abf &nbsp; &nbsp;PP.H2.abf &nbsp; &nbsp;PP.H1.abf &nbsp; &nbsp;PP.H0.abf &nbsp; &nbsp;coloc_variant_id &nbsp; &nbsp;coloc_region<br>1 &nbsp; &nbsp;109272258 &nbsp; &nbsp;rs4970834 &nbsp; &nbsp;C &nbsp; &nbsp;T &nbsp; &nbsp;ENSG00000168765 &nbsp; &nbsp;-0.12874.25001047052626e-09 &nbsp; &nbsp;ebi-a-GCST000998 &nbsp; &nbsp;-0.250697 &nbsp; &nbsp;0.0893351 &nbsp; &nbsp;Alasoo_2018_ge_macrophage_IFNg+Salmonella &nbsp; &nbsp;0.108283426725895 &nbsp; &nbsp;0.0520205502224409 &nbsp; &nbsp;6 &nbsp; &nbsp;0.000552728832012655 &nbsp; &nbsp;2.09397277427793e-07 &nbsp; &nbsp;0.890881419183881 &nbsp; &nbsp;0.000282215860934432 &nbsp; &nbsp;1_109279544_G_A &nbsp; &nbsp;1:108779544-109779543<br>1 &nbsp; &nbsp;109274968 &nbsp; &nbsp;rs12740374 &nbsp; &nbsp;G &nbsp; &nbsp;T &nbsp; &nbsp;ENSG00000168765 &nbsp; &nbsp;-0.103341 &nbsp; &nbsp;1.63998546891446e-09 &nbsp; &nbsp;ebi-a-GCST000998 &nbsp; &nbsp;-0.197397 &nbsp; &nbsp;0.172673Alasoo_2018_ge_macrophage_IFNg+Salmonella &nbsp; &nbsp;0.108283426725895 &nbsp; &nbsp;0.0857585178966856 &nbsp; &nbsp;6 &nbsp; &nbsp;0.000552728832012655 &nbsp; &nbsp;2.09397277427793e-07 &nbsp; &nbsp;0.890881419183881 &nbsp; &nbsp;0.000282215860934432 &nbsp; &nbsp;1_109279544_G_A &nbsp; &nbsp;1:108779544-109779543<br>1 &nbsp; &nbsp;109275216 &nbsp; &nbsp;rs660240 &nbsp; &nbsp;T &nbsp; &nbsp;C &nbsp; &nbsp;ENSG00000168765 &nbsp; &nbsp;0.1044492.78997299740827e-09 &nbsp; &nbsp;ebi-a-GCST000998 &nbsp; &nbsp;0.214165 &nbsp; &nbsp;0.139318 &nbsp; &nbsp;Alasoo_2018_ge_macrophage_IFNg+Salmonella &nbsp; &nbsp;0.108283426725895 &nbsp; &nbsp;0.0557749486050279 &nbsp; &nbsp;6 &nbsp; &nbsp;0.000552728832012655 &nbsp; &nbsp;2.09397277427793e-07 &nbsp; &nbsp;0.890881419183881 &nbsp; &nbsp;0.000282215860934432 &nbsp; &nbsp;1_109279544_G_A &nbsp; &nbsp;1:108779544-109779543<br>1 &nbsp; &nbsp;109275684 &nbsp; &nbsp;rs629301 &nbsp; &nbsp;G &nbsp; &nbsp;T &nbsp; &nbsp;ENSG00000168765 &nbsp; &nbsp;0.1054716.129993302249e-10 &nbsp; &nbsp;ebi-a-GCST000998 &nbsp; &nbsp;0.197397 &nbsp; &nbsp;0.172673 &nbsp; &nbsp;Alasoo_2018_ge_macrophage_IFNg+Salmonella &nbsp; &nbsp;0.108283426725895 &nbsp; &nbsp;0.22229240584331 &nbsp; &nbsp;6 &nbsp; &nbsp;0.000552728832012655 &nbsp; &nbsp;2.09397277427793e-07 &nbsp; &nbsp;0.890881419183881 &nbsp; &nbsp;0.000282215860934432 &nbsp; &nbsp;1_109279544_G_A &nbsp; &nbsp;1:108779544-109779543<br>1 &nbsp; &nbsp;109278889 &nbsp; &nbsp;rs602633 &nbsp; &nbsp;T &nbsp; &nbsp;G &nbsp; &nbsp;ENSG00000168765 &nbsp; &nbsp;0.1034352.15998134341707e-09 &nbsp; &nbsp;ebi-a-GCST000998 &nbsp; &nbsp;0.226329 &nbsp; &nbsp;0.102673 &nbsp; &nbsp;Alasoo_2018_ge_macrophage_IFNg+Salmonella &nbsp; &nbsp;0.108283426725895 &nbsp; &nbsp;0.0782482718431504 &nbsp; &nbsp;6 &nbsp; &nbsp;0.000552728832012655 &nbsp; &nbsp;2.09397277427793e-07 &nbsp; &nbsp;0.890881419183881 &nbsp; &nbsp;0.000282215860934432 &nbsp; &nbsp;1_109279544_G_A &nbsp; &nbsp;1:108779544-109779543<br>...</p> </blockquote> <p>&nbsp;</p> <p>The dataset provides colocalization statistics for GWAS-eQTL pairs, including posterior probabilities and variant annotations.</p> <h2>2. gwas2eqtl0.1.3.tsv.gz</h2> <p>This file is a filtered version of the colocalization dataset, refined based on cutoffs of PP.H4.abf &ge; 0.75 and SNP.PP.H4 &ge; 0. This subset is utilized in the gwas2eqtl web application for data visualization.</p> <p>Sample Columns:</p> <blockquote> <p>chrom &nbsp; pos19 &nbsp; pos38 &nbsp; cytoband &nbsp; &nbsp; &nbsp; &nbsp;rsid &nbsp; &nbsp;ref &nbsp; &nbsp; alt &nbsp; &nbsp; gwas_trait &nbsp; &nbsp; &nbsp;gwas_class &nbsp; &nbsp; &nbsp;gwas_beta &nbsp; &nbsp; &nbsp; eqtl_gene_symbol &nbsp; &nbsp; &nbsp; &nbsp;eqtl_beta &nbsp; &nbsp; &nbsp; eqtl_id eqtl_gene_id &nbsp; &nbsp;gwas_id gwas_pval &nbsp; &nbsp; &nbsp; eqtl_pval &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;pp_h4_abf &nbsp; &nbsp; &nbsp; snp_pp_h4 &nbsp; &nbsp; &nbsp; tophits_variant_id &nbsp; &nbsp; &nbsp;nsnps<br>1 &nbsp; &nbsp; &nbsp; 1163804 1228424 1p36.33 rs7515488 &nbsp; &nbsp; &nbsp; C &nbsp; &nbsp; &nbsp; T &nbsp; &nbsp; &nbsp; Inflammatory bowel disease &nbsp; &nbsp; &nbsp;Autoimmune dis. 0.0874308 &nbsp; &nbsp; &nbsp; ANKRD65 -0.175816 &nbsp; &nbsp; &nbsp; BrainSeq_ge_brain &nbsp; &nbsp; &nbsp; ENSG00000235098 ebi-a-GCST003043 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 2.85292266979231e-10 &nbsp; &nbsp;0.000841046 &nbsp; &nbsp; 0.978425254116226 &nbsp; &nbsp; &nbsp; 6.18454060493069e-12 &nbsp; &nbsp;1_1312114_T_C &nbsp; 3<br>1 &nbsp; &nbsp; &nbsp; 1163804 1228424 1p36.33 rs7515488 &nbsp; &nbsp; &nbsp; C &nbsp; &nbsp; &nbsp; T &nbsp; &nbsp; &nbsp; Inflammatory bowel disease &nbsp; &nbsp; &nbsp;Autoimmune dis. 0.0874308 &nbsp; &nbsp; &nbsp; ANKRD65 -0.175816 &nbsp; &nbsp; &nbsp; BrainSeq_ge_brain &nbsp; &nbsp; &nbsp; ENSG00000235098 ieu-a-294 &nbsp; &nbsp; &nbsp; 2.85292266979231e-10 &nbsp; &nbsp; &nbsp; 0.000841046 &nbsp; &nbsp; 0.974019788384412 &nbsp; &nbsp; &nbsp; 7.52286530905187e-12 &nbsp; &nbsp;1_1312114_T_C &nbsp; 4<br>1 &nbsp; &nbsp; &nbsp; 1163804 1228424 1p36.33 rs7515488 &nbsp; &nbsp; &nbsp; C &nbsp; &nbsp; &nbsp; T &nbsp; &nbsp; &nbsp; Inflammatory bowel disease &nbsp; &nbsp; &nbsp;Autoimmune dis. 0.0874308 &nbsp; &nbsp; &nbsp; ANKRD65 -0.293529 &nbsp; &nbsp; &nbsp; CommonMind_ge_DLPFC_naive &nbsp; &nbsp; &nbsp; ENSG00000235098 ebi-a-GCST003043 &nbsp; 2.85292266979231e-10 &nbsp; &nbsp;2.30452e-06 &nbsp; &nbsp; 0.953333690803618 &nbsp; &nbsp; &nbsp; 6.9758581004380506e-15 &nbsp;1_1312114_T_C &nbsp; 6<br>1 &nbsp; &nbsp; &nbsp; 1163804 1228424 1p36.33 rs7515488 &nbsp; &nbsp; &nbsp; C &nbsp; &nbsp; &nbsp; T &nbsp; &nbsp; &nbsp; Inflammatory bowel disease &nbsp; &nbsp; &nbsp;Autoimmune dis. 0.0874308 &nbsp; &nbsp; &nbsp; ANKRD65 -0.293529 &nbsp; &nbsp; &nbsp; CommonMind_ge_DLPFC_naive &nbsp; &nbsp; &nbsp; ENSG00000235098 ieu-a-294 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;2.85292266979231e-10 &nbsp; &nbsp;2.30452e-06 &nbsp; &nbsp; 0.951092499048109 &nbsp; &nbsp; &nbsp; 7.0876793540547e-15 &nbsp; &nbsp; 1_1312114_T_C &nbsp; 7<br>1 &nbsp; &nbsp; &nbsp; 1163804 1228424 1p36.33 rs7515488 &nbsp; &nbsp; &nbsp; C &nbsp; &nbsp; &nbsp; T &nbsp; &nbsp; &nbsp; Inflammatory bowel disease &nbsp; &nbsp; &nbsp;Autoimmune dis. 0.0874308 &nbsp; &nbsp; &nbsp; ANKRD65 -0.510549 &nbsp; &nbsp; &nbsp; FUSION_ge_adipose_naive ENSG00000235098 ebi-a-GCST003043 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 2.85292266979231e-10 &nbsp; &nbsp;1.2212e-06 &nbsp; &nbsp; &nbsp;0.974412793352836 &nbsp; &nbsp; &nbsp; 1.70246427912903e-11 &nbsp; &nbsp;1_1312114_T_C &nbsp; 6</p> </blockquote> <p>&nbsp;</p> <p>This filtered dataset focuses on high-confidence colocalization events for functional exploration of genetic associations and regulatory mechanisms.</p>

opencc-by-4.0Nov 2024View details →
zenodo36/100

Annotated transcription of the list of books that are recorded in Pieter de Graeff's probate inventory of Ilpenstein (1709)

<p>Annotated transcription of the list of books that are recorded in the probate inventory of Ilpenstein belonging to Pieter de Graeff (1638-1707), Amsterdam patrician and VOC director.</p> <p>Amsterdam City Archives, Inventaris van het Archief van de Notarissen ter Standplaats Amsterdam (nr. 5075), inv. nr. 5001, pp. 425&ndash;493, notary Michiel<br>Servaes (nr. 199), 8 March 1709, pp. 510-557.</p> <p>These books are analysed and discussed in C. Piccoli, <a href="https://brill.com/display/title/69863">Pieter de Graeff (1638-1707) and his <em>treffelyke bibliotheek</em>. Exploring and Reconstructing an Early Modern Private Library as a Book Collection and as a Physical Space</a> (Leiden: Brill, 2025). This dataset is published as supplementary material of this monograph.</p>

opencc-by-4.0Nov 2024View details →
zenodo36/100

Annotated transcription of Pieter de Graeff's book auction catalogue (9 July 1709)

<p>Annotated transcription of Pieter de Graeff's book auction catalogue (9 July 1709). Full title: <em>Catalogus Librorum Viri Amplissimi Petri De Graaff (dum viveret) Toparchae In Zuydt-Polsbroeck, Purmerland, Ilpendam, &amp;c. &amp;c. Urbis Amstelodamensis Scabini &amp;c. Quorum auctio habebitur in Aedibus Viduae T. Boom, &amp; haeredum H. Boom. Op de Cingel by de Jan-Roonpoorts Tooren. Die Martis 9. Julii MDCCIX. Amstelodami, In Officina Bomiana Ubi Catalogi distribuuntur. </em>National Library of Russia, St Petersburg (NL: 16.133.9.36). Scanned copy: <a href="http://primarysources.brillonline.com/browse/book-sales-catalogues-online/catalogus-librorum-collected-by-an-amsterdam-magistrate-amsterdam-widow-dirk-i-boom-heirs-hendrick-boom-1709;bscobsc02687" target="_blank" rel="noopener">Brill Book Sales Catalogue Online</a>.&nbsp;</p> <p>The transcription lists the books&rsquo; identified full titles with the authors&rsquo; names accompanied by their <a href="https://viaf.org/">VIAF</a> ID's to facilitate identification and disambiguation. Similarly, the <a href="https://www.getty.edu/research/tools/vocabularies/tgn/">Getty Thesaurus of Geographic Names (TGN)</a> has been used to facilitate the identification of the publication places. Matches with the <a href="https://www.ustc.ac.uk/">Universal Short Title Catalogue (USTC)</a> and the <a href="https://data.cerl.org/stcn/_search">Short-Title Catalogue Netherlands (STCN)</a> are also provided.</p> <p>This dataset is analysed and discussed in C. Piccoli, <a href="https://brill.com/display/title/69863">Pieter de Graeff (1638-1707) and his <em>treffelyke bibliotheek</em>. Exploring and Reconstructing an Early Modern Private Library as a Book Collection and as a Physical Space</a> (Leiden: Brill, 2025), and published as supplementary material of this monograph.</p> <p>Author statement:<br>Chiara Piccoli: Conceptualization; research &amp; analysis; books' identification (Theologici, Juridici, Medici &amp; Philosophici; Miscellanei); USTC, STCN and TNG matches; groupings by and identifications of formats, languages, subjects; notes; links to digitized copies; data curation.<br>Bart Reuvekamp: First transcription; books' identification (Theologici).</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2024View details →
zenodo36/100

Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery

<p>Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of diseasecompound combinations is unfeasible. To overcome this limitation, here we introduce PRnet, a perturbation-conditioned deep generative model that predicts transcriptional responses to novel chemical perturbations that have never experimentally perturbed at bulk and single-cell levels. Evaluations indicate that PRnet outperforms alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and in-silico drug screening for diseases based on gene signatures. PRnet further identifies and experimentally validates novel compound candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generates a large-scale integration atlas of perturbation profiles, covering 88 cell lines, 52 tissues, and various compound libraries. PRnet provides a robust and scalable candidate recommendation workflow and successfully recommends drug candidates for 233 diseases. Overall, PRnet is an effective and valuable tool for gene-based therapeutics screening.</p>

opencc-by-4.0Oct 2024View details →
zenodo36/100

Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage

<p>Processed data and code for "Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage," Naqvi et al 2025.</p> <p>Directory is organized into the following subfolders, each tar'ed and gzipped:</p> <p><strong>data_analysis.tar.gz - Processed data for modulation of TWIST1 levels and calculation of RE responsiveness to TWIST1 dosage</strong></p> <ul> <li>atac_design.txt - design matrix for ATAC-seq TWIST1 titration samples</li> <li>all.sub.150bpclust.greater2.500bp.merge.TWIST1.titr.ATAC.counts.txt - ATAC-seq counts from all samples over all reproducible ATAC-seq peak regions, as defined in Naqvi et al 2023</li> <li>atac_deseq_fitmodels_moded50.R - R code for calculating new version of ED50 and response to full depletion from TWIST1 titration data (note, uses drm.R function from <a href="https://doi.org/10.5281/zenodo.7689948">10.5281/zenodo.7689948</a>,&nbsp;install drc() with this version to avoid errors)</li> </ul> <p><strong>baseline_models.tar.gz - Code and data for training baseline models to predict RE responsiveness to SOX9/TWIST1 dosage</strong></p> <ul> <li>{sox9|twist1}.{0v100|ed50}.{train|valid|test}.txt - Training/testing/validation data (ED50 or full TF depletion effect for SOX9 or TWIST1), split into train/test/validation folds</li> <li>HOCOMOCOv11_core_HUMAN_mono_jaspar_format.all.sub.150bpclust.greater2.500bp.merge.minus300bp.p01.maxscore.mat.cpg.gc.basemean.txt.gz - matrix of predictors for all REs. Quantitative encoding of PWM match for all HOCOMOCO motifs + CpG + GC content, plus unperturbed ATAC-seq signal</li> <li>train_baseline.R - R code to train baseline (LASSO regression or random forest) models using predictor matrix and the provided training data. <ul> <li>Note: training the random forest to predict full TF depletion is computationally intensive because it is across all REs, if doing this run on CPU for ~6 hrs.&nbsp;</li> </ul> </li> </ul> <p><strong>chrombpnet_models.tar.gz - Remainder of code, data, and models for fine-tuning and interpreting ChromBPNet mdoels to </strong><strong>predict RE responsiveness to SOX9/TWIST1 dosage</strong></p> <ul> <li>Fine-tuning code, data, models <ul> <li>{all|sox9.direct|twist1.bound.down}.{train|valid|test}.{ed50|0v100.log2fc}.txt - Training/testing/validation data (ED50 or full TF depletion effect for SOX9 or TWIST1), split into train/test/validation folds</li> <li>pretrained.unperturbed.chrombpnet.h5 - Pretrained model of unperturbed ATAC-seq signal in CNCCs, obtained by running ChromBPNet (https://github.com/kundajelab/chrombpnet) on DMSO-treated SOX9/TWIST1-tagged ATAC-seq data</li> <li>finetune_chrombpnet.py - code for fine-tuning the pretrained model for any of the relevant prediction tasks (ED50/ effect of full TF depletion for SOX9/TWIST1)</li> <li>best.model.chrombpnet.{0v100|ed50}.{sox9|twist1}.h5 - output of finetune_chrombpnet.py, best model after 10 training epochs for the indicated task</li> <li>chrombpnet.{0v100|ed50}.{sox9|twist1}.contrib.{h5|bw} - contribution scores for the indicated predictive model, obtained by running chrombpnet contribs_bw on the corresponding model h5 file.</li> <li>chrombpnet.{0v100|ed50}.{sox9|twist1}.contrib.modisco.{h5|bw} - TF-MoDIsCo output from the corresponding contribution score file</li> </ul> </li> <li>Interpretation code, data, models <ul> <li>contrib_h5_to_projshap_npy.py - code to convert contrib .h5 files into .npy files containing projected SHAP scores (required because the CWM matching code takes this format of contribution scores)</li> <li>sox9.direct.10col.bed, twist1.bound.down.10col.uniq.bed - regions over which CWMs will be matched (likely direct targets of each TF)</li> <li>match_cwms.py - Python code to match individual CWM instances. Takes as input: modisco .h5 file, SHAP .npy file, bed file of regions to be matched. Output is a bed file of all CWM matches (not pruned, contains many redundant matches).</li> <li>chrombpnet.ed50.{sox9|twist1}.contrib.perc05.matchperc10.allmatch.bed - output of match_cwms.py&nbsp;</li> <li>take_max_overlap.py - code to merge output of match_cwms.py into clusters, and then take the maximum (length-normalized) match score in each cluster as the representative CWM match of that cluster. Requires upstream bedtools commands to be piped in, see example usage in file.&nbsp;</li> <li>chrombpnet.ed50.{sox9|twist1}.contrib.perc05.matchperc10.allmatch.maxoverlap.bed - output of &nbsp;take_max_overlap.py. These CWM instances are the ones used throughout the paper.</li> </ul> </li> </ul> <p><strong>modisco_reports.zip -</strong><strong> TF-MoDIsCo reports from running on the fine-tuned ChromBPNet models</strong></p> <ul> <li>modisco_report_{sox9|twist1}_{0v100|ed50}: folders containing images of discovered CWMs and HTMLs/PDFs of summarized reports from running TF-MoDisCo on the indicated fine-tuned ChromBPNet model</li> </ul> <p><strong>chrombpnet_models_supp.tar.gz - Alternative ChromBPNet mdoels to </strong><strong>predict SOX9/TWIST1 ED50 using varying definitons of direct targets</strong></p> <ul> <li> <p>best.model.chrombpnet.ed50.twist1.3hdn.h5 - TWIST1 direct targets defined using response to full 3h depletion (as was done for SOX9 throughout the rest of the paper)</p> </li> <li> <p>best.model.chrombpnet.ed50.sox9.v5chip.h5 - SOX9 direct targets defined using V5 ChIP-seq from SOX9-tagged lines (as was done for TWIST1 throughout the paper)</p> </li> </ul> <p><strong>mirny_model.tar.gz - Code and data for analyzing and fitting Mirny model of TF-nucleosome competition to observed RE dosage response curves</strong></p> <ul> <li>twist1.strong.multi.only.ed50.cutoff.true.hill.txt - ED50 and signed hill coefficients for all TWIST1-dependent REs with only buffering Coordinators (mostly one or two) and no other TFs' binding sites. "ed50_new" is the ED50 calculation used in this paper.&nbsp;</li> <li>twist1.strong.weak{1|2|3}.ed50.cutoff.true.hill.txt - ED50 and signed hill coefficients for all TWIST1-dependent REs with only buffering Coordinators (mostly one or two) and the indicated number of sensitizing (weak) Coordinators and no other TFs' binding sites. "ed50_new" is the ED50 calculation used in this paper.&nbsp;</li> <li>MirnyModelAnalysis.py - Python code for analysis of Mirny model of TF-nucleosome competition. Contains implementations of analytic solutions, as well as code to fit model to observed ED50 and hill coefficients in the provided data files.</li> </ul> <p><strong>nucleoatac.tar.gz - Output files from running NucleoATAC on merged ATAC-seq from each of 5 TWIST1 dosages</strong></p> <ul> <li>TWIST1_{dosage}_merge.nucmap_combined.bed.gz - see NucleoATAC docs for output format</li> </ul>

opencc-by-4.0May 2024View details →
zenodo36/100

Paulinella micropora KR01 assembled transcripts

<p>Description of files:</p> <p><strong>5RACE_Combined_D_L_assembly.fa</strong></p> <p>Transcripts assembled from 5-prime enriched RNA sequencing reads (cap-switching transcripts). Read data available from NCBI&rsquo;s SRA repository (BioProject ID&nbsp;PRJNA730897).</p> <p><strong>Standard_RNAseq_transcripts.fa</strong></p> <p>Transcripts assembled from standard RNA sequencing reads (standard transcripts) that align to the <em>P. micropora&nbsp;</em>KR01 genome. Read data available from NCBI&rsquo;s SRA repository (BioProject ID&nbsp;PRJNA568118).</p> <p><strong>5RACE_Combined_D_L_assembly_mapped.seqnames.txt</strong></p> <p>Names of the cap-switching transcripts that align to the <em>P. micropora </em>KR01 genome.</p> <p><strong>5RACE_Combined_D_L_assembly_unmapped.seqnames.txt</strong></p> <p>Names of the cap-switching transcripts that no not align to the <em>P. micropora </em>KR01 genome.</p> <p><strong>5RACE_SL_Transcripts.seqnames.txt</strong></p> <p>Names of the cap-switching transcripts that align to the <em>P. micropora </em>KR01 genome and encode a <em>Paulinella</em> spliced-leader sequence.</p> <p><strong>5RACE_SL_Transcripts_unique2RACE.seqnames.txt</strong></p> <p>Names of the cap-switching transcripts that align to the <em>P. micropora </em>KR01 genome, encode a <em>Paulinella</em> spliced-leader sequence, and are unique to the cap-switching dataset (i.e., are not encoded in the standard transcripts).</p> <p><strong>Standard_RNAseq_SL_transcripts.seqnames.txt</strong></p> <p>Names of the standard transcripts that align to the <em>P. micropora </em>KR01 genome and encode a <em>Paulinella</em> spliced-leader sequence.</p>

opencc-by-4.0Nov 2021View details →
zenodo36/100

DisFull_Lnc: discover full-length long noncoding RNAs without transcriptional initiation profiles

<p>DisFull_Lnc aims&nbsp;to identify full-length lncRNAs directly from raw RNA-seq data without H3K4me3 profiles from the corresponding&nbsp;sample.</p>

opencc-by-4.0Nov 2021View details →
zenodo36/100

Distinct and essential roles of bZIP transcription factors in stress response and pathogenesis in Alternaria alternata

<p>The ability to cope with environmental abiotic stress and biotic stress is crucial for the survival of plants and microorganisms, which enable them to occupy multiple niches in the environment. Previous studies have shown that transcription factors play crucial roles in regulating various biological processes including multiple stress tolerance and response in eukaryotes. This work identified multiple critical transcription factor genes, metabolic pathways and gene ontology (GO) terms related to abiotic stress response were broadly activated by analyzing the transcriptome of phytopathogenic fungus Alternaria alternata un- der metal ions stresses, oxidative stress, salt stresses, and host-pathogen interaction. We determined the biological functions and regulatory roles of the bZIP transcriptional factor (TF) genes in the phytopathogenic fungus A. alternata by analyzing targeted gene deletion mutants. Morphological analysis provides evidence that bZIPs including Gcn4, MeaB, Atf1, Hac1 and Ada1 are required for morphogenesis as the colony morphology of these gene deletion mutants was significantly different from that of the wild-type. In addition, bZIPs are involved in the resistance to multiple stresses such as oxidative stress (Ada1, Yap1, MetR) and virulence (Hac1, MetR, Yap1, Ada1) at varying degrees. Transcriptome data demonstrated that the inactivation of bZIPs (Hac1, Atf1, Ada1 and Yap1) significantly affected many genes in multiple critical metabolism pathways and gene ontology (GO) terms. Moreover, the &Delta;Hac1 mutants displayed reduced aerial hypha and are hypersensitivity to endoplasmic reticulum disruptors such as tunicamycin and dithiothreitol. Transcriptome analysis showed that inactivation of Hac1 significantly affected the proteasome process and its downstream unfolded protein binding, indicating that Hac1 participates in the endoplasmic reticulum stress response through the conserved unfolded protein response. Taken together, our findings identified many crucial transcription factor genes and pathways related to cell development, abiotic stress response and pathogenesis, and expand our understanding of how microbial pathogens utilize these genes to deal with environmental stresses and achieve successful infection in the host plant.</p>

opencc-by-4.0Nov 2021View details →
zenodo36/100

Assembled transcripts: Another lesson from unmapped reads – in depth analysis of RNA-Seq reads from various horse tissues

<p>Assembled transcripts &ndash; putative transcripts de novo assembled with Trinity software</p> <p>&nbsp;</p> <p>Data: loin adipose - AD, hoof lamina - LM, liver - LI, longissimus muscle - LO, left lung - LU, heart left ventricle - LV, ovary- OV and parietal cortex &ndash; PC<br> 1 &ndash; horse ECA_UCD_AH1<br> 2 &ndash; horse ECA_UCD_AH2</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2021View details →
zenodo36/100

Neurotoxicity of an HBV Transcript Inhibitor in 13-Week Rat and Monkey Studies

<p>Supplemental Tables 1-7&nbsp;for the manuscript<br> &quot;Neurotoxicity of an HBV Transcript Inhibitor in 13-Week Rat and Monkey Studies&quot;</p>

opencc-by-4.0Jan 2022View details →
zenodo36/100

Data from Schaffter, S.W. and Strychalski, E.A. "Co-transcriptionally encoded RNA strand displacement circuits" Science Advances (2022)

<p>The <strong>ctRSD_data_upload.xlsx</strong> file contains both the raw and normalized fluorescence data from this manuscript.</p> <p>In the Excel file, rows highlighted in green indicate the times when T7 RNAP was added to the samples. Plots of data in the manuscript designate this time as time = 0 min.</p> <p>The <strong>figure2D_fluorescence_data_plotting.py</strong> file is an example showing how the data was imported from the Excel file and plotted in the manuscript.</p> <p>In the script, the save_loc variable will need to be changed to the path pointing to where the ctRSD_data_upload.xlsx file (Data S1) is saved on a user&rsquo;s local computer.</p>

opencc-by-4.0Jan 2022View details →
zenodo36/100

transcription of a male with transcortical-motor aphasia

<p>A transcription of a re-telling of a story of a cartoon &quot;Shaun the Sheep&quot; within exploring the relationship between speech and gestures in persons with aphasia: Evidence from the Czech perspective.</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

transcription of a male with Broca's aphasia

<p>A transcription of a re-telling of a story of a cartoon &quot;Shaun the Sheep&quot; within exploring the relationship between speech and gestures in persons with aphasia: Evidence from the Czech perspective.</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

transcription of a male with Broca's aphasia

<p>A transcription of a re-telling of a story of a cartoon &quot;Shaun the Sheep&quot; within exploring the relationship between speech and gestures in persons with aphasia: Evidence from the Czech perspective.</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

transcription of a male with Wernicke's aphasia

<p>A transcription of a re-telling of a story of a cartoon &quot;Shaun the Sheep&quot; within exploring the relationship between speech and gestures in persons with aphasia: Evidence from the Czech perspective.</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

transcription of a female with transcortical-motor aphasia

<p>A transcription of a re-telling of a story of a cartoon &quot;Shaun the Sheep&quot; within exploring the relationship between speech and gestures in persons with aphasia: Evidence from the Czech perspective.</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

transcription of males without aphasia

<p>A transcription of a re-telling of a story of a cartoon &quot;Shaun the Sheep&quot; within exploring the relationship between speech and gestures in persons with aphasia: Evidence from the Czech perspective.</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

transcription of females without aphasia

<p>A transcription of a re-telling of a story of a cartoon &quot;Shaun the Sheep&quot; within exploring the relationship between speech and gestures in persons with aphasia: Evidence from the Czech perspective.</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

LoGov Italy Interview Transcript n°2

<p>The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project &ldquo;LoGov - Local Government and the Changing Urban-Rural Interplay&rdquo; as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Italy.</p> <p>To receive more information about the project, please visit: <a href="https://www.logov-rise.eu/">https://www.logov-rise.eu/</a>. This project has received funding from the European Union&rsquo;s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.</p>

opencc-by-4.0Dec 2021View details →
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LoGov Italy Interview Transcript n°3

<p>The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project &ldquo;LoGov - Local Government and the Changing Urban-Rural Interplay&rdquo; as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Italy.</p> <p>To receive more information about the project, please visit: <a href="https://www.logov-rise.eu/">https://www.logov-rise.eu/</a>. This project has received funding from the European Union&rsquo;s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.</p>

opencc-by-4.0Dec 2021View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record