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897 results for “Therapeutic targets”
Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention
<p>Single cell RNA seq datasets used for analysis in the Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention</p>
Multi-omic Insights into Molecular Mechanism and Therapeutic Targets in Spinocerebellar Ataxia type 7
<p>The molecular mechanism in spinocerebellar ataxia type 7 is currently poorly understood. To provide understandings, a multi-omic study was performed using SCA7266Q/5Q mice. At week 12, entire brain tissue samples were collected and RNA sequencing, methylation analysis, and proteomic analysis were performed. Results were integrated to identify genes with identical trends in expression. Data was also compared with SCA patient serum proteomic analysis, and based on common differentially expressed proteins, a Naïve Bayesian network model was constructed to predict nilotinib treatment response. Data from RNA sequencing and methylation analysis revealed 58 significantly hypomethylated-upregulated genes and 62 hypermethylated-downregulated genes, mostly enriched in GO terms of regulation of axonogenesis, channel activity, and monoamine signaling. In the proteomic analysis, 211 upregulated and 281 downregulated DEPs associated mostly with immune response and cellular mobility were identified. Two genes, Fam107b and Tph2, showed differential expression in both transcriptomic and proteomic analysis. Forty-two overlapping proteins were identified compared with SCA patient serum, and Bayesian network analysis revealed that nilotinib treatment response was associated with the protein expression of CLU, CA2, GLUL, PRDX6, C1QA, PLXNB1, and age. These findings will serve as an important reference for future studies on the pathogenesis and discovery of druggable targets. </p>
Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation
<p>Compressed directories containing code and data files sufficient to reproduce analysis from Bloom et al paper on <em>Lactobacillus iners</em> (<em>Nature Microbiology</em>). An earlier, non-peer-reviewed manuscript version containing largely the same analysis was posted as a pre-print in <em>bioRxiv</em> at (https://doi.org/10.1101/2021.06.12.448098). Three compressed directory for analyses of:</p> <ol> <li>Vaginal <em>Lactobacillus </em>genome catalog characterization and gene content analysis.</li> <li>Analysis of relationship between cervicovaginal microbiota composition and cysteine concentrations in vaginal fluid from a South African cohort</li> <li>Analysis of results of <em>in vitro </em>mixed culture competition assays including: <ol> <li>Pairwise competition between <em>L. iners</em> and <em>Lactobacillus crispatus</em> in <em>Lactobacillus</em> MRS broth containing L-cysteine +/- S-methyl-L-cysteine (SMC)</li> <li>Defined bacterial-vaginosis (BV)-like communities including <em>L. iners</em>, <em>L. crispatus</em>, and BV-associated species <em>Gardnerella vaginalis</em>, <em>Prevotella bivia</em>, and <em>Atopobium (Fannyhessea) vaginae</em> cultured in S-broth with or without SMC and/or metronidazole.</li> </ol> </li> </ol>
Data files: Single-cell RNA profiling of Plasmodium vivax-infected hepatocytes reveals parasite- and host- specific transcriptomic signatures and therapeutic targets
<p>Scripts, preprocessed count matrices, and single-cell data objects generated in <strong>“Single-cell RNA profiling of <em>Plasmodium vivax</em><em>-</em>infected hepatocytes reveals parasite- and host- specific transcriptomic signatures and therapeutic targets” </strong></p>
Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq
<p><strong><a href="https://gitlab.com/bu_cnio/Beyondcell">Beyondcell</a> </strong>is a methodology for the identification of drug vulnerabilities in single cell RNA-seq data. To this end, <strong>Beyondcell</strong> focuses on the analysis of drug-related commonalities between cells by classifying them into distinct therapeutic clusters. We have validated the tool in a population of MCF7-AA cells exposed to 500nM of bortezomib and collected at different time points: t0 (before treatment), t12, t48 and t96 (72h treatment followed by drug wash and 24h of recovery) obtained from <a href="https://www.nature.com/articles/s41586-018-0409-3"><strong><em>Ben-David U, et al., Nature, 2018</em></strong></a>. Here, you can find the integrated Seurat object obtained from this analysis. This object is meant to help users follow <strong>Beyondcell's</strong> <a href="https://gitlab.com/bu_cnio/Beyondcell/-/tree/master/tutorial/analysis_workflow">analysis workflow</a>.</p> <p> </p>
SUPPLEMENTARY (For MD) An integrative pan-genome and subtractive proteomics approach for the identification of potential novel therapeutic drug target against antibiotic resistant honeybee pathogen Paenibacillus larvae
<p><strong>Parameters</strong></p><p>Force field: AMBER ff19SB</p><p>Water type: TIP3P</p><p>Ions: NaCl </p><p>Ligand topology force field: GAFF2</p><p>Temperature: 298k</p><p>Pressure: 1 bar</p><p>minimization step: 20000 on 5 nanoseconds</p><p>initial velocity is changed by changing "ntx" and "ig"</p><p>C2: ntx = 5 , ig = 8</p><p>C3: ntx = 2 , ig = 5</p><p> </p><p><strong>Uploads</strong>- </p><p>1. Zip file of all 3 main files</p><p>2. Unzip file of C1 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>3. Zip file of C1</p><p>4. Unzip file of C2 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>5. Zip file of C2</p><p>6. Unzip file of C3 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>7. Zip file of C3</p><p>8. Zip and unzip file of <strong>Initial</strong> PDB of complex prior to MD simulation with <strong>Post</strong> MD PDB (C1, C2, C3)</p><p>9. Zip file of <strong>topology</strong> files for C1, C2, and C3</p>
Global Functional Genomics Reveals GRK5 as a Therapeutic Target for Cystic Fibrosis
<p>Cystic Fibrosis (CF) is a life-shortening disease affecting >90,000 individuals worldwide predominantly with respiratory symptoms. About 80% of individuals with CF have the F508del mutation that causes the CF transmembrane conductance regulator (CFTR) protein to misfold and be targeted for premature degradation by the endoplasmic reticulum (ER) quality control (ERQC), thus preventing its plasma membrane (PM) traffic. Despite the recent approval of a ‘highly effective’ drug rescuing F508del-CFTR, maximal lung function improvement is ~14% and the drug-targeted genes remain unknown.</p> <p>To identify global modulators of F508del traffic, we performed a high-content siRNA microscopy-based screen of >9,000 genes and monitored F508del-CFTR PM rescue in human airway cells. This primary screen identified 227 F508del-CFTR traffic regulators, of which 35 could be validated by additional siRNAs. Subsequent mechanistic studies established GRK5 as a robust regulator whose inhibition rescues F508del-CFTR PM traffic, thus emerging as a novel potential drug target for CF.</p>
Analyses of metabolite profiling of Drosophila Parkinson's Disease model for identifying novel glial-based therapeutic targets
<p>Analysis for genetic screening and metabolomics identify glial adenosine metabolism as a therapeutic target in Parkinson’s disease</p> <p>This project contains the analysis of metabolite abundance measurements obtained with four different liquid chromatography mass spectrometry methods of synuclein expressing or control or fly brains in a wilde type or Adk1 knockout background.</p> <p> </p> <div> <h2>Table of contents</h2> <a href="https://github.com/jravilap/Olsen_Analyses#table-of-contents"></a></div> <div> <h3>Prerequisites</h3> <a href="https://github.com/jravilap/Olsen_Analyses#prerequisites"></a></div> <ul> <li>R (version 4.3.1 or higher)</li> <li>RStudio (optional, but recommended)</li> </ul> <div> <h3>R Packages</h3> <a href="https://github.com/jravilap/Olsen_Analyses#r-packages"></a></div> <p>The following R packages are required. You can install them using the commands below:</p> <div> <pre>install.packages(c(<span><span>"</span>readxl<span>"</span></span>, <span><span>"</span>calibrate<span>"</span></span>, <span><span>"</span>dplyr<span>"</span></span>, <span><span>"</span>ggplot2<span>"</span></span>))</pre> <div> </div> </div> <div> <h3>Package versions</h3> <a href="https://github.com/jravilap/Olsen_Analyses#package-versions"></a></div> <ul> <li>ggplot2_3.5.1</li> <li>dplyr_1.1.4</li> <li>yaml_2.3.8</li> <li>calibrate_1.7.7</li> <li>readxl_1.4.3</li> </ul> <div> <h2>Project Structure</h2> <a href="https://github.com/jravilap/Olsen_Analyses#project-structure"></a></div> <ul> <li><code>code/</code>: Contains the R scripts for the analysis.</li> <li><code>data/</code>: Processed data files. <ul> <li><code>22_0322_alphaSyn_fly_pilot_Classes.xlsx</code>: metabolite profiling data</li> <li><code>dup_metabs_decision.csv</code>: Table defining which metabolites profiled in more than one method should be used.</li> </ul> </li> <li><code>results/</code>: Output files, including plots and tables.</li> <li><code>common_functions/</code>: Custom R functions used in the analysis.</li> <li><code>config.yml</code>: Configuration file for setting paths.</li> </ul>
Pan-cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics
<p>CPTAC PAN-cancer Data Repository</p> <p>Welcome to the CPTAC PAN-cancer Data Repository! This repository serves as a data repository for the CPTAC PAN-cancer effort, which focuses on cancer target discovery. It contains various data sets related to protein abundance estimation, derived TMT-TPA, iBAQ, iBAQ-derived copy number, and differential protein expression for CPTAC ten indications.</p> <p>## Contents</p> <p>The repository includes the following data:</p> <p>- FragPipe Output: Protein abundance estimation data generated using the FragPipe software.<br> - Derived TMT-TPA: Data derived from Tandem Mass Tag (TMT) based Total Protein Approach (TPA).<br> - iBAQ: Data representing intensity-based absolute quantification (iBAQ) of proteins.<br> - iBAQ-derived Copy Number: Data derived from iBAQ analysis for copy number estimation.<br> - Differential Protein Expression: Data indicating differential expression of proteins between tumor and NAT.</p> <p>## Data Organization</p> <p>The data in this repository is organized in a structured manner to facilitate easy access and navigation. The repository structure is as follows:</p> <p>FragPipe/<br> [fragpipe_data_files]<br> Derived_TMT_TPA/<br> [derived_tmt_tpa_data_files]<br> iBAQ/<br> [ibaq_data_files]<br> iBAQ-derived_copy_number/<br> [ibaq_copy_number_data_files]<br> Differential_protein_expression/<br> [differential_expression_data_files]</p>
Data from: Bayesian estimation of muscle mechanisms and therapeutic targets using variational autoencoders
Open the record for dataset details and reuse information.
Multiomics analyses reveal a Type III-associated immune response in immunotherapy-induced toxicity in melanoma and identifies potential new therapeutic targets
<p>Immune checkpoint inhibitors (ICIs) are standard-of-care for the treatment of advanced melanoma, but their use is limited by immune-related adverse events (irAEs). Proteomic analyses and multiplex cytokine/chemokine assays from serum at baseline and at irAEs onset in melanoma patients indicated aberrant T-cell activity with differential expression of Type I and III immune signatures. This was in line with an increase in the proportions of monocytes and decrease of IL-17A-producing CD4+ T-cells in the peripheral blood using single cell RNA sequencing. Multiplex immunohistochemistry and spatial transcriptomics on ICI-induced skin rash and colitis showed an increase in the proportion of CD4+ T-cells with IL-17A expression. </p> <p>Anti-IL-17A mAbs were administered in two patients with myocarditis, colitis and skin rash with resolution of the irAE. This study highlights the potential role of Type III CD4+ T-cells in irAEs development and provides proof-of-principle evidence for a corresponding clinical trial using anti-IL17A for treating irAEs.</p> <p>Code to generate the figures are located at https://github.com/pcheng84/AE_analysis/</p> <p> </p>
Liver-targeted polymeric prodrugs delivered subcutaneously improve tafenoquine therapeutic window for malaria radical cure
<p>Approximately 3.3 billion people live with the threat of Plasmodium vivax malaria. Infection can result in liver-localized hypnozoites, which when reactivated cause relapsing malaria. This is the first demonstration of an enzyme-cleavable polymeric prodrug of tafenoquine (TQ) that addresses key requirements for a mass administration, eradication campaign: excellent subcutaneous bioavailability, complete parasite control after a single dose, improved therapeutic window compared to the parent oral drug, and low Cost of Goods Sold (COGS) at less than $1.50/dose. Liver-targeting and subcutaneous dosing resulted in improved liver:plasma exposure profiles, with increased efficacy and reduced Glucose 6-Phosphate Dehydrogenase (G6PD)-dependent hemotoxicity in validated preclinical models. A COGS and manufacturability analysis demonstrated global scalability, affordability, and the ability to redesign this fully synthetic polymeric prodrug specifically to increase global equity and access. Taken together, this polymer prodrug platform is a candidate for evaluation in human patients and shows potential for P. vivax eradication campaigns.</p>
Generative AI in the Advancement of Viral Therapeutics for Predicting and Targeting Immune-Evasive SARS-CoV-2 Mutations
<p>This dataset <strong>encompasses</strong> and describes the following features:</p> <ul> <li>Mutations in viruses like SARS-CoV-2 can make them escape vaccines and treatments.</li> <li>Accurately predicting these mutations is crucial for developing effective countermeasures.</li> <li>The study uses a type of AI called a Generative Adversarial Network (GAN) to analyze the virus's spike protein, which plays a key role in infection.</li> <li>The GAN generates protein sequences similar to natural ones, but which are also likely to evade immune responses.</li> <li>By analyzing these generated sequences, the researchers improve their AI model's ability to predict real-world escape mutations.</li> <li>This improved prediction could help design better vaccines and treatments, and prepare for future viral threats.</li> </ul>
Replication Data for: Precision Oncology, Cell Signaling and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics
<p>In recent decades, there has been a deluge in the large-scale production of anticancer agents, primarily due to advances in genomic technologies enabling precise targeting of oncogenic pathways involved in disease progression. This initiated a paradigm shift in cancer research and therapeutics based on the ability to study molecular changes throughout the genome. It provided a unique opportunity in the field of translational cancer research and have led to the concept of precision medicine in cancer therapy, raising hopes of developing better diagnostic and therapeutic means for the management of cancer. The purpose of this article is to briefly review the tools and techniques involved in precision oncology research and their applications in the field of cancer treatment. </p>
BRAT1 - a new therapeutic target for glioblastoma
<p>Proteomic and phosphoproteomic raw data files (Excel) used for further analysis of the research on BRAT1 as a new therapeutic target for glioblastoma.</p>
Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target
<p>Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in </p> <blockquote> <p><strong>Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted.</strong></p> </blockquote> <p>The files will be made available upon publication. <br></p> <h4><strong>Description of the files</strong></h4> <ul> <li><strong>01_raw_counts: </strong>count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline</li> <li><strong>10_prepare_adata</strong>: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata.</li> <li><strong>20_scrnaseq_qc</strong>: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection.</li> <li><strong>30_merge_adata</strong>: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering</li> <li><strong>40_cluster_analysis</strong>: Identify and investigate subclusters representing cell-states that go beyond the major cell-types</li> <li><strong>50_de_analysis</strong>: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow)</li> <li><strong>70_downstream_analysis</strong>: Perform pathway analyses and generate figures for publication based on the data generated in the previous steps</li> <li><strong>containers:</strong> Conda environments used for the analysis packed up as singularity containers. </li> </ul>
"Global Functional Genomics Reveals GRK5 as a Therapeutic Target for Cystic Fibrosis", CFTR interactomes
<p><em>A tidy selection of published CFTR interactomes (or CFTR-related omics datasets)</em></p> <p>Collects data from the original online sources, converts gene/protein identifiers into a standard tidy format and updates the identifiers. The Uniprot ID is taken as reference and all other identifiers are genereated from them. This causes some datasets to end up with less genes/proteins than reported.</p> <p>Datasets included</p> <ul> <li><strong>Botelho (2021)</strong> - CFTR traffic regulators <ul> <li>Botelho <em>et al</em> (2022), submitted</li> </ul> </li> <li><strong>Pankow (2015)</strong> - CFTR interactome <ul> <li>Pankow <em>et al</em> (2015) deltaF508 CFTR interactome remodelling promotes rescue of cystic fibrosis. <em>Nature</em>. 528, 510--516. <a href="https://doi.org/10.1038/nature15729">https://doi.org/10.1038/nature15729</a></li> </ul> </li> <li><strong>Canato (2018)</strong> - CFTR interactome <ul> <li>Canato <em>et al</em> (2018) Proteomic interaction profiling reveals KIFC1 as a factor involved in early targeting of F508del-CFTR to degradation. <em>Cell Mol Life Sci</em>. 75(24):4495-4509. <a href="https://doi.org/10.1007/s00018-018-2896-7">https://doi.org/10.1007/s00018-018-2896-7</a></li> </ul> </li> <li><strong>Santos (2019)</strong> - CFTR interactome <ul> <li>Santos <em>et al</em> (2019) Folding Status Is Determinant over Traffic-Competence in Defining CFTR Interactors in the Endoplasmic Reticulum. <em>Cells</em>. 8(4):353. <a href="https://doi.org/10.3390/cells8040353">https://doi.org/10.3390/cells8040353</a></li> </ul> </li> <li><strong>Hutt (2018)</strong> - CFTR interactome <ul> <li>Hutt <em>et al</em> (2018) A Proteomic Variant Approach (ProVarA) for Personalized Medicine of Inherited and Somatic Disease. <em>J Mol Biol</em>. 430: 2951-2973. <a href="https://doi.org/10.1016/j.jmb.2018.06.017">https://doi.org/10.1016/j.jmb.2018.06.017</a></li> </ul> </li> <li><strong>Rauniyar (2014)</strong> - CFTR proteome <ul> <li>Rauniyar <em>et al</em> (2014) Quantitative Proteomic Profiling Reveals Differentially Regulated Proteins in Cystic Fibrosis Cells. <em>J Proteome Res</em>. 13(11): 4668-4675. <a href="https://doi.org/10.1021/pr500370g">https://doi.org/10.1021/pr500370g</a></li> </ul> </li> <li><strong>Almaça (2013)</strong> - ENaC regulome <ul> <li>Almaça <em>et al</em> (2013) High-content siRNA screen reveals global ENaC regulators and potential cystic fibrosis therapy targets. <em>Cell</em>. 154(6):1390-400. <a href="https://doi.org/10.1016/j.cell.2013.08.045">https://doi.org/10.1016/j.cell.2013.08.045</a></li> </ul> </li> <li><strong>Tomati (2018)</strong> - CFTR regulome <ul> <li>Tomati <em>et al</em> (2018) High-throughput screening identifies FAU protein as a regulator of mutant cystic fibrosis transmembrane conductance regulator channel. <em>J Biol Chem</em>. 293(4):1203-1217. <a href="https://doi.org/10.1074/jbc.m117.816595">https://doi.org/10.1074/jbc.m117.816595</a></li> </ul> </li> <li><strong>Simpson (2012)</strong> - Secretome <ul> <li>Simpson <em>et al</em> (2012) Genome-wide RNAi screening identifies human proteins with a regulatory function in the early secretory pathway. <em>Nature Cell Biology</em>. 14, 764-774. <a href="https://doi.org/10.1038/ncb2510">https://doi.org/10.1038/ncb2510</a></li> </ul> </li> <li><strong>Wang (2006)</strong> - CFTR interactome <ul> <li>Wang <em>et al</em> (2006) Hsp90 Cochaperone Aha1 Downregulation Rescues Misfolding of CFTR in Cystic Fibrosis. <em>Cell</em>. 127(4):803-815. <a href="https://doi.org/10.1016/j.cell.2006.09.043">https://doi.org/10.1016/j.cell.2006.09.043</a></li> </ul> </li> <li><strong>Reilly (2017)</strong> - CFTR interactome <ul> <li>Reilly <em>et al</em> (2017) Targeting the PI3K/Akt/mTOR signalling pathway in Cystic Fibrosis. <em>Sci Rep</em>. 9;7(1):7642. <a href="https://doi.org/10.1038/s41598-017-06588-z">https://doi.org/10.1038/s41598-017-06588-z</a></li> </ul> </li> <li><strong>Gilchrist (2006)</strong> - Secretome <ul> <li>Gilchrist <em>et al</em> (2006) Quantitative Proteomics Analysis of the Secretory Pathway. <em>Cell</em>. 127(6):1265-1281. <a href="https://doi.org/10.1016/j.cell.2006.10.036">https://doi.org/10.1016/j.cell.2006.10.036</a></li> </ul> </li> <li><strong>Pankow (2019)</strong> - CFTR interactome <ul> <li>Pankow <em>et al</em> (2019) A posttranslational modification code for CFTR maturation is altered in cystic fibrosis. <em>Science Signaling</em>. 12(562):eaan7984. <a href="https://doi.org/10.1126/scisignal.aan7984">https://doi.org/10.1126/scisignal.aan7984</a></li> </ul> </li> <li><strong>Dang (2020)</strong> - CF lung disease modifier genes <ul> <li>Dang <em>et al</em> (2020) Mining GWAS and eQTL data for CF lung disease modifiers by gene expression imputation. <em>PLoS One</em>. 15(11):e0239189. <a href="https://doi.org/10.1371/journal.pone.0239189">https://doi.org/10.1371/journal.pone.0239189</a></li> </ul> </li> <li><strong>Hodos (2020)</strong> - CF genomic meta-analysis <ul> <li>Hodos <em>et al</em> (2020) Integrative genomic meta-analysis reveals novel molecular insights into cystic fibrosis and deltaF508-CFTR rescue. <em>Sci Rep</em> 10(1):20553. <a href="http://dx.doi.org/10.1038/s41598-020-76347-0">http://dx.doi.org/10.1038/s41598-020-76347-0</a></li> </ul> </li> </ul>
Molcular Dynamics Data for Therapeutic High Affinity T Cell Receptor Targeting a KRAS G12D Cancer Neoantigen
<p>This folder contains the starting structures and input scripts required to simulate the wild-type and G12D KRAS peptide bound TCR-pHLA complexes, as performed in this study.</p> <p><br> Starting_Structures - This folder contains the amber parameter/topology files used to simulate each system (.prmtop) and the coordinates of the starting structure both as amber coordinate file (.rst) and PDB file (.pdb).<br> MD_Inputs - This folder contains the amber MD inputs used to run the md simulations. <br> MMPBSA_inputs - This folder contains the input files for running MMPBSA with the MMPBSA.py script in amber. The mmpbsa.in script was used for calculating overall binding energy whereas the mmpbsa_decomp.in script was used for calculating the per-residue contribution to binding energy. </p>
KidDO progress presentation 2022: Metabolic Targets for Therapeutic Intervention in Kidney Disease
<p>This is a recorded talk with head of the KidDO project - Robert Fenton - where he presents the latest project progress.</p> <p>The talk was given at one of ODIN's (the Open Discovery Innovation Network) Knowledge Sharing Events in May 2022.</p> <p> </p> <p> </p>
Imbalanced expression of cation-chloride cotransporters as a potential therapeutic target in an Angelman Syndrome mouse model
<p>We provide 6 files; 1. Data for western blot analysis (WB_NKCC1_KCC2_Fig1A.xlsx), 2. Data for [Cl-]i(Intracelluar‗Cl_Fig1B.xlsx), 3. Electrophysilogical data for mIPSC and tonic current (mIPSC_Tonic current_Fig2B_C_E.xlsx), 4. Data for behavior analysis (Behavior_analysis_Fig3A_C .xlsx), 5. Data for seizure threshhold (seizure_threshold_Fig4A.xlsx), 6. Data for EEG spike number and band power (EEG_analysis_Fig4B_C.xlsx)</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.