| --- |
| pretty_name: pMHC Data Compendium |
| license: other |
| license_name: mixed-upstream |
| license_link: https://huggingface.co/datasets/isalgo/pmhc_data#licensing |
| language: |
| - en |
| tags: |
| - immunology |
| - immunopeptidomics |
| - neoantigen |
| - mhc |
| - hla |
| - t-cell |
| - cancer-vaccine |
| - proteomics |
| size_categories: |
| - 10M<n<100M |
| --- |
| |
| # pMHC Data Compendium |
|
|
| Harmonized peptide–MHC data for neoantigen scoring: presentation, immunogenicity labels, |
| self/foreign reference peptidomes, expression, and clinical-outcome cohorts. Human and mouse. |
|
|
| Every table here is derived from public sources. Per-file provenance, filtering rules, verified |
| PubMed citations and row-level audits live in the `SOURCES.md` of each directory — that is the |
| authoritative record, not this page. |
|
|
| ## Contents |
|
|
| | Directory | What is in it | |
| |---|---| |
| | [`pmhc/`](pmhc) | Epitope–MHC binding records — the presentation panel (`pmhc_full`, and `pmhc_shortlist` at ≥2 references) | |
| | [`immunogenicity/`](immunogenicity) | T-cell immunogenicity corpora: immunogenic vs **self** ligands and vs **non-self**, each with an HLA-matched variant; labelled set; HLA population frequencies | |
| | [`neoantigens/`](neoantigens) | Tested and candidate neoantigens — minigene, epitope-level and per-mutation screens, human and mouse | |
| | [`ligandome/`](ligandome) | Foreign and tissue reference peptidomes — viral ligands, pan-viral ORFs, cancer-testis targets, per-tissue corpora | |
| | [`thymus/`](thymus) | Thymic self-peptidome and thymic expression, human and mouse — the central-tolerance reference | |
| | [`expression/`](expression) | Reference expression, human and mouse, plus a single-pipeline table and mouse protein abundance | |
| | [`proteome/`](proteome) | Human and mouse reference proteomes plus 16 bacterial/viral proteomes for mimicry scans | |
| | [`homology/`](homology) | Cross-species homology mappings | |
| | [`tcga/`](tcga) | Expressed mutant-peptide–HLA binders across 8,505 donors | |
| | [`hla_loh/`](hla_loh) | HLA-I loss of heterozygosity and genetic immune escape over 6,319 tumours, with three-caller typing concordance | |
| | [`icb_cohorts/`](icb_cohorts) | Harmonized checkpoint-blockade cohorts with outcome | |
| | [`vaccines/`](vaccines) | Shipped personalized-vaccine cassettes and their immune monitoring | |
|
|
| Formats are mixed by design: gzipped TSV for the large harmonized tables, parquet where the schema |
| is wide or typed, `.npz` for dense matrices, gzipped FASTA for proteomes. |
|
|
| ## Shared schema |
|
|
| The immunogenicity tables share one schema: |
|
|
| ``` |
| peptide, mhc_a, mhc_b, mhc_class, mhc_species, source_species, host_species, |
| assay_type, immunogenicity{1,0,null}, expression, affinity, affinity_wt, |
| agretopicity, foreignness, validated, dataset_origin, reference_id, n_references |
| ``` |
|
|
| plus `patient_id, cancer_type` on the neoantigen tables. **Canonical columns hold as-reported |
| values only**; anything predicted lives in a `pred_*` column, so `affinity` is always experimental. |
| `affinity` units differ by source and MHC alleles are as-reported. |
|
|
| ## Two things to know before filtering |
|
|
| - **`neoantigens/neoag_tested.tsv.gz` contains mouse rows labelled as human.** 6,186 CEDAR rows |
| carry mouse MHC alleles (`H2-Db` 1,020, `H2-Kb` 932, `H2-b class I` 719, `H2-Kd` 525, |
| `H2-d class II` 505, …) while their `mhc_species` reads `HomoSapiens` and `host_species` reads |
| `human`. `SIINFEKL`, the canonical H-2Kb ovalbumin epitope, is among them. Filtering that table |
| on `mhc_species` therefore silently admits mouse pMHC. The defect is upstream and is left |
| unfixed; `neoantigens/neoag_tested_mmu.tsv.gz` is the correctly-labelled murine set. |
| - **`neoag_tested_{mmu,hsa}` overlap `neoag_tested.tsv.gz` by design** — all 2,273 peptides appear |
| in both, since both are IEDB-derived. Dedupe on peptide × MHC when joining. |
|
|
| ## Loading |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import pandas as pd |
| |
| path = hf_hub_download("isalgo/pmhc_data", "pmhc/pmhc_shortlist.tsv.gz", repo_type="dataset") |
| df = pd.read_csv(path, sep="\t") |
| ``` |
|
|
| Grab one directory rather than the whole repo: |
|
|
| ```bash |
| hf download isalgo/pmhc_data --repo-type dataset --include "neoantigens/*" --local-dir ./pmhc_data |
| ``` |
|
|
| [`mhcmatch`](https://pypi.org/project/mhcmatch/) consumes this compendium directly — `mhcmatch |
| bootstrap` stages the panel and reference tables from here. |
|
|
| ## Upstream sources |
|
|
| Which table came from which, and under what filtering, is in the `SOURCES.md` of each directory. |
|
|
| | | | |
| |---|---| |
| | IEDB | https://www.iedb.org | |
| | CEDAR | https://cedar.iedb.org | |
| | HLA Ligand Atlas | https://hla-ligand-atlas.org | |
| | UniProt | https://www.uniprot.org | |
| | GTEx | https://gtexportal.org | |
| | Human Protein Atlas | https://www.proteinatlas.org | |
| | FANTOM5 | https://fantom.gsc.riken.jp/5/ | |
| | UCSC Xena | https://xenabrowser.net | |
| | GDC / TCGA | https://portal.gdc.cancer.gov | |
| | ICGC / PCAWG | https://dcc.icgc.org | |
| | Hartwig Medical Foundation | https://www.hartwigmedicalfoundation.nl | |
| | Allele Frequency Net Database | https://www.allelefrequencies.net | |
| | PRIDE | https://www.ebi.ac.uk/pride/ | |
| | MassIVE | https://massive.ucsd.edu | |
| | GEO | https://www.ncbi.nlm.nih.gov/geo/ | |
|
|
| ## Licensing |
|
|
| Each upstream source carries its own terms; this compendium redistributes derived, harmonized |
| tables and does not relicense them. Check the `SOURCES.md` of the directory you are using before |
| redistributing, and cite the primary publications recorded there rather than this repository. |
|
|
