ArticleNucleic acids research2018
The SysteMHC Atlas project.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
89 citing papers in PubMed.
- TransBindpMHCI: a transformer-based model for pan-specific MHC-I peptide binding prediction.BMC bioinformatics · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Finding and filling the knowledge gaps in mechanisms of T cell-mediated TB immunity to inform vaccine design.Nature reviews. Immunology · 2025Review
- MetE: a promising protective antigen for tuberculosis vaccine development.Frontiers in immunology · 2025Article
- Article
- Towards designing improved cancer immunotherapy targets with a peptide-MHC-I presentation model, HLApollo.Nature communications · 2024Article
- Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines.Nature communications · 2024Article
- ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis.Nature communications · 2024Article
- Article
- NeoMUST: an accurate and efficient multi-task learning model for neoantigen presentation.Life science alliance · 2024Article
- ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.Briefings in bioinformatics · 2024Article
- The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics.Nucleic acids research · 2024Article
- Data-Independent Acquisition Peptidomics.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
- Computational immunogenomic approaches to predict response to cancer immunotherapies.Nature reviews. Clinical oncology · 2024Review
- TOFNature communications · 2023Article
- Article
- Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery.Journal for immunotherapy of cancer · 2023Review
- Antigen presentation in cancer - mechanisms and clinical implications for immunotherapy.Nature reviews. Clinical oncology · 2023Review
- Recurrent Myocarditis in Patients With Desmosomal Pathogenic Variants: Is Self Antigen Presentation the Link?JACC. Clinical electrophysiology · 2023Review
29 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
46 authors.
Funding
Abstract
Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.