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11 results for “immunopeptidome”
CARMEN immunopeptidomics publication associated dataset
<h1>CARMEN: CAnceR imMunopeptidogENomics</h1> <blockquote> <div>An immunopeptidomic dataset accompanying the publication "<a href="http://dx.doi.org/10.1101/2025.05.08.651510" target="_blank" rel="noopener">Expanding the definition of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers with CARMEN</a>" containing peptides determined by mass-spectrometry associated with MHC Class I bindings from 72 publications (2323 samples).</div> </blockquote> <div> </div> <div> <p><strong>Authors:</strong> <a href="mailto:aleksander.palkowski@gmail.com" target="_blank" rel="noopener">Aleksander Palkowski</a>*, <a href="mailto:mwaleron@gmail.com" target="_blank" rel="noopener">Michal Waleron</a>*, <a href="mailto:emilia.daghir@gmail.com" target="_blank" rel="noopener">Emilia Daghir-Wojtkowiak</a>*, <a href="mailto:ashwinkallor@gmail.com" target="_blank" rel="noopener">Ashwin Adrian Kallor</a>*, <a href="mailto:javier.alfaro@proteogenomics.ca" target="_blank" rel="noopener">Javier Antonio Alfaro</a></p> <p>* <em>These authors contributed equally to this work</em></p> </div> <div> </div> <div>The entire dataset consist of four table files in the <a href="https://parquet.apache.org" target="_blank" rel="noopener">Apache Parquet</a> data file format:</div> <ul> <li>main</li> <li>mapped-protein-annotations-pogo</li> <li>mapped-protein-annotations-msfragger</li> <li>hla-sequences</li> </ul> <div> </div> <div><strong>Please refer to the README.md file for details.</strong></div> <div> </div>
Predicted Human Immunopeptidome
<p>Comprehensive peptide-MHC binding predictions were performed for all 8-11mer peptides in the human proteome to all class I MHC available for prediction using NetMHCpan 3.0.</p> <p>human_immunopeptidome_files.tar.gz, when decompressed, contains a directory of all individual HLA files each containing all self-peptides predicted to bind. Alternatively, if only a subset of HLA alleles are of interest to you, decompressed individual files are available for each HLA. Note that these are identical to the files contained within the .tar.gz archive file.</p> <p>Details on the creation of this dataset can be found here: https://github.com/scottdbrown/self-immunopeptidome_cancer</p>
Data from: Investigating the human and non-obese diabetic mouse MHC class II immunopeptidome using protein language modelling.
<p><strong>Background</strong>: Identifying peptides associated with the major histocompability complex class II (MHCII) is a central task in the evaluation of the immunoregulatory function of therapeutics and drug prototypes. MHCII-peptide presentation prediction has multiple biopharmaceutical applications, including the safety assessment of biologics and engineered derivatives in silico, or the fast progression of antigen-specific immunomodulatory drug discovery programs in immune disease and cancer. This has resulted in the collection of large–scale data sets on adaptive immune receptor antigenic responses and MHC-associated peptide proteomics. In parallel, recent deep learning algorithmic advances in natural language processing (NLP) and protein language modelling (PLM) have shown potential in leveraging large collections of sequence data and improve MHC presentation prediction. <strong>Methodology</strong>: We trained a compact transformer model (AEGIS) on human and mouse MHCII immunopeptidome data, including a preclinical murine model, and evaluated its performance on the peptide presentation prediction task. <strong>Data</strong>: The data and models used in AEGIS are contained in the uploaded tar files. <strong>Results</strong>: The transformer performs on par with existing deep learning algorithms and that combining datasets from multiple organisms increases model performance (see preprint). We trained variants of the model with and without MHCII information. In both alternatives, the inclusion of peptides presented by the I-Ag7 MHC class II molecule expressed by the non-obese diabetic (NOD) mice enabled the in silico prediction of presented peptides in a preclinical type 1 diabetes model organism, which has promising therapeutic applications.</p>
Immunopeptidomics on T. cruzi – infected MC57G murine fibroblasts
Open the record for dataset details and reuse information.
CDK4/6 Inhibition reprograms the Breast Cancer immunopeptidome via Rb-Dependent chromatin and transcriptomic remodeling
GEO Series GSE304997. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
Immunopeptidomic analyses of colorectal cancers with and without microsatellite instability
GEO Series GSE195985. Homo sapiens. 17 samples. Type: Expression profiling by high throughput sequencing.
Thousands of novel unannotated proteins expand the MHC I immunopeptidome in cancer
GEO Series GSE143263. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Characterization of the Fungal Immunopeptidome Involved in the Immunopathological Mechanisms of Psoriasis
ClinicalTrials.gov study NCT06610942. IPD Sharing: NO. Countries: 1. Publications: 0.
EMITT-1 (ERAP Mediated Immunopeptidome Targeting Trial - 1)
ClinicalTrials.gov study NCT06923761. IPD Sharing: NO. Countries: 4. Publications: 0.
CDK4/6 Inhibition reprograms the Breast Cancer immunopeptidome via Rb-Dependent chromatin and transcriptomic remodeling
GEO Series GSE287955. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
IFNγ modulates the immunopeptidome of triple negative breast cancer cells by enhancing and diversifying antigen processing and presentation
GEO Series GSE163067. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
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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)
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DANDI Archive for NWB datasets
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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.