immunogenicity/SOURCES.md

immunogenicity/ — sources, provenance, filtering and a complete audit

General immunogenicity corpora: is this presented peptide seen by a T cell? Neoantigen datasets moved to ../neoantigens/ — this directory no longer holds NCI, neoag_* or any tumour-specific screen.

Every citation resolved against PubMed; PMIDs and DOIs given. Audit regenerated by bench/neoag/audit_pmhc_data.py in 2026-mhcmatch-benchmark; corpora rebuilt by bench/neoag/corpus_chowell.py + bench/neoag/deposit_corpora.py.

Written 2026-08-17.

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Complete audit

Measured from the files, not copied from any prior description.

file	rows	distinct peptides	pos	neg	prevalence	HLA alleles	length
chowell_rebuilt.tsv.gz	511,301	315,191	19,866	491,435	0.0389	210	8–11
chowell_rebuilt_hla_matched.tsv.gz	115,592	99,763	19,866	95,726	0.1719	183	8–11
kesmir_rebuilt.tsv.gz	65,737	36,155	22,613	43,124	0.3440	196	8–11
kesmir_rebuilt_hla_matched.tsv.gz	59,941	35,202	22,613	37,328	0.3773	188	8–11
iedb_labeled.tsv.gz	790,868	516,092	34,362	756,506	0.0435	250	2–50
hla_pop_freqs.tsv.gz	7,171	—	—	—	—	4,234	—

No donor/patient identifier exists in any file here — these are peptide-level corpora aggregated over studies, not per-donor cohorts. (Donor counts are meaningful in ../neoantigens/ and are reported there.)

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chowell_rebuilt.tsv.gz — immunogenic vs presented self

Chowell's construction, rebuilt from the current IEDB dump rather than read off the 2015 spreadsheet, which is copyrighted supplementary material and frozen at 2015.

Chowell D, Krishna S, Becker PD, Cocita C, Shu J, Tan X, Greenberg PD, Klavinskis LS, Blattman JN, Anderson KS. TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes. Proc Natl Acad Sci USA 2015;112(14):E1754–62. PMID 25831525 · doi:10.1073/pnas.1500973112

Filtering procedure, in order

1. Source — iedb_labeled.tsv.gz (see below), itself an IEDB export.
2. Class — mhc_class == "MHCI".
3. Host — host_species ∈ {human, mouse}; kept as a column and never pooled. The two hosts have different MHC and different thymic repertoires, so a fit across them without the split is fitting a mixture.
4. Peptide — uppercased, whitespace-stripped; length 8–11; canonical 20 amino acids only.
5. Exclusions — SLLMWITQV, KLGGALQAK (NY-ESO-1 / MAGE-A3 epitopes that appear on both sides of the literature with contradictory annotation).
6. Positives — any peptide with a positive T-cell assay (dataset_origin == "iedb_tcell").
7. Negatives — eluted ligands whose source_species equals the host (self), plus the HLA Ligand Atlas thymus immunopeptidome for the human arm.
8. Label is a property of the (peptide, host), not the row. 1,075 peptides are both eluted and positively assayed. Elution is evidence of presentation; it is never evidence against a T-cell response. Assigning per row makes the same sequence positive under one allele and negative under another — it sits on both sides of the fit. Computing the positive set once per (peptide, host) and applying it everywhere raised self-sourced positives by 57%.
9. Aggregation — one row per (peptide, allele, host_species), label = max.

Not controlled, deliberately

Allele and length. Immunogenic and eluted sets genuinely differ in both: some HLAs are protective and some are not, and immunogenic peptides have a real length distribution that differs from bulk ligands. Matching those away removes effects, not confounds. allele and length ship as columns so a stratified analysis is a filter, not a rebuild — and the HLA-matched variant below exists for when allele composition is the thing to remove.

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kesmir_rebuilt.tsv.gz — immunogenic vs presented non-self

The Calis/Kesmir contrast. Same shape, same rules, one difference: negatives are eluted ligands from a source organism that is not the host.

Calis JJA, Maybeno M, Greenbaum JA, Weiskopf D, De Silva AD, Sette A, Keşmir C, Peters B. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol 2013;9(10):e1003266. PMID 24204222 · doi:10.1371/journal.pcbi.1003266

This arm requires ../ligandome/viral_foreign_iedb.tsv.gz, and is wrong without it. Every eluted-ligand negative in the IEDB table itself is self-sourced — 449,449 human-self and 41,986 mouse-self against 149 + 63 cross-species, and zero from any other organism. Filtering source_species != "human" on a mouse host therefore selects mouse self ligands and rebuilds Chowell under another name. The foreign ligandome supplies 41,294 genuine human non-self negatives.

Kesmir removes the self/non-self axis that Chowell confounds with immunogenicity, and is the harder problem: prevalence 0.344 against Chowell's 0.039, and every negative is a foreign peptide that was presented and still not attacked.

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The HLA-matched variants

*_rebuilt_hla_matched.tsv.gz, built by deposit_corpora.py --ratio 5 --seed 20260817.

There are 5–25× more negatives than positives and the two sides do not share an allele distribution, so a classifier can separate them on allele composition alone. For each allele, negatives are drawn without replacement to 5 per positive of that same allele:

• alleles with no positives contribute no negatives — keeping them would reintroduce exactly the imbalance being removed;
• alleles with too few negatives contribute all they have, and the shortfall stays visible in the per-allele counts rather than being topped up from another allele;
• the draw is seeded (20260817), so the file is reproducible rather than one sample.

Effect: Chowell 491,435 → 95,726 negatives, 210 → 183 alleles, prevalence 0.039 → 0.172. Kesmir 43,124 → 37,328, 196 → 189 alleles.

Use the matched file when the question is about peptide sequence; use the full file when allele composition is part of the signal you want. Neither is "the" corpus.

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Two residue-level artefacts, measured

Both are properties of how the data was generated. A model will fit either as if it were biology.

corpus	Cys % positives	Cys % negatives	ratio	C-term R/K % pos	C-term R/K % neg
chowell_rebuilt	10.69	1.55	6.9×	9.21	9.50
kesmir_rebuilt	10.94	14.50	0.8×	9.91	20.33
iedb_labeled	14.56	2.52	5.8×	10.90	13.98

Cysteine — an MS detection artefact, and it hits Chowell hardest

Cysteine is systematically under-detected in mass-spectrometry immunopeptidomics unless samples are alkylated. Where positives are T-cell-assayed (synthetic peptides, no MS step) and negatives are MS-eluted, the corpus carries a Cys gradient with no biological content. Chowell shows 6.9×; finer-grained, the thymus MS subset runs at 0.17% against 11.59% in the assayed positives.

Fitted freely on the Chowell corpus, Cys took the single largest coefficient in a position-role naive Bayes (+1.84 anchor / +2.05 TCR-facing). Masking it costs grouped-CV AUROC 0.712 → 0.690 and improves transfer to neoantigen cohorts. The shipped mhcmatch.posbayes tables zero it.

kesmir_rebuilt does not have this problem (0.8×) — its negatives are IEDB-curated foreign ligands rather than a single MS pipeline. That makes it the safer corpus for fitting residue-level terms, and is a reason to run both.

C-terminal Arg/Lys — genuine anchor preference, or tryptic carry-over

A basic C-terminus is the real anchor preference of HLA-A*03, A*11, A*31, A*68 and B*27 — and Arg/Lys are exactly the residues trypsin cleaves after. An elevated rate can be either, and the two are not separable from sequence alone.

kesmir_rebuilt negatives run at 20.33% against 9.91% in positives — a 2× gradient in the corpus that is otherwise the cleaner of the two. Before attributing anything to a C-terminal basic residue on this corpus, stratify by allele: if the effect survives within A*03/A*11/B*27 and within non-basic-preferring alleles separately, it is anchor preference; if it only appears pooled, it is composition or contamination.

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iedb_labeled.tsv.gz

The IEDB export both corpora are built from. This is also the ipred training set, so any ipred figure quoted on it is in-sample.

790,868 rows · 516,092 distinct peptides · 34,362 immunogenic · 250 alleles · lengths 2–50 (unfiltered — the 8–11 restriction is applied downstream).

Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, Wheeler DK, Sette A, Peters B. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res 2019;47(D1):D339–D343. PMID 30357391 · doi:10.1093/nar/gky1006

Provenance: experimental — curated T-cell assay and MHC-ligand records. Raw export kept at raw/immunogenicity/iedb_tcell_assays_29032026.tsv.gz.

Thymus negatives come from the HLA Ligand Atlas (thymus/thymus_immunopeptidome.tsv.gz); its mhc_a is null by design — the deposit is not allele-deconvolved.

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hla_pop_freqs.tsv.gz

Class-I allele frequencies (A/B/C) for EUR/ASN/AFR populations, 7,171 rows over 4,234 distinct alleles. Source: Allele Frequency Net Database (AFND), http://www.allelefrequencies.net. Provenance: experimental (population genotyping), aggregated.

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Regenerate

# in ~/vcs/projects/2026-mhcmatch-benchmark
python bench/neoag/corpus_chowell.py                 # plain build (Chowell arm)
python bench/neoag/corpus_chowell.py --with-viral    # + foreign ligandome (Kesmir arm)
python bench/neoag/deposit_corpora.py --ratio 5 --seed 20260817
python bench/neoag/audit_pmhc_data.py

corpus_chowell.parquet must keep its exact row counts (human 464,310 / 14,712 pos; mouse 47,203 / 5,154) — the shipped mhcmatch.posbayes tables are fitted on it, and folding extra sources into it in place breaks that provenance silently. That is why --with-viral writes a separate file.

chowell_iedb_full.tsv.gz / chowell_iedb_full_matched.tsv.gz (2026-08-18)

Rebuilt from the full IEDB export (build 2026-08-11) rather than iedb_labeled.tsv.gz. That harmonised table kept only positive T-cell assays, so its assay_type is perfectly collinear with its label and it cannot supply a measured negative at all; the full export carries 361,962 negative T-cell assay rows, for mouse as well as human.

chowell_rebuilt.tsv.gz and kesmir_rebuilt.tsv.gz are unchanged and still current — the shipped mhcmatch.complement and posbayes tables are fitted on them, they are named in the library's REFERENCE_FILES, and every AUROC in bench/results/complementarity.md belongs to them. These two files are additional, not replacements.

The rules, in the order applied

1. MHC class I.
2. Host ∈ {human, mouse}; a column, never pooled.
3. Length 8–11, canonical 20 amino acids.
4. Exclude SLLMWITQV, KLGGALQAK.
5. Immunogenic — the peptide has ≥1 positive T-cell assay in that host (Positive, Positive-Low, -High, -Intermediate).
6. Non-immunogenic — an eluted self ligand appearing in no positive T-cell assay in any host. 2,086 rows are eluted but positive elsewhere and are excluded from the negatives rather than counted as such.
7. The label is a property of (peptide, host), never of an assay row.
8. The restriction is resolved, and no row is dropped for lacking one: 1,677,439 of 3,278,355 surviving class-I rows name only HLA class I. netMHCpan-4.2 picks the best %Rank_EL among whatever the recorded string still permits — an allele group or serotype restricts to that group, an H-2 haplotype to the molecules it carries, a bare HLA class I to a 30-allele population panel built per locus. A known non-human/non-mouse MHC on a human or mouse host (2,812 rows) is a curation error, not a missing value, and is excluded rather than imputed.
9. One row per (peptide, allele group, host), label = max. The key is the allele group (HLA-A*02), which is what lets serotype-only records join.

Counts

file	rows	positive	negative	peptides	allele groups
chowell_iedb_full (human)	789,047	24,293	764,754	579,322	75
chowell_iedb_full (mouse)	65,472	7,187	58,285	49,253	43
chowell_iedb_full_matched (human)	20,360	10,180	10,180	17,181	18
chowell_iedb_full_matched (mouse)	9,132	4,566	4,566	7,969	25

Columns add allele_2f, allele_group, allele_source ∈ {reported, imputed_group, imputed_panel} and allele_rank_el, so an analysis can exclude imputed restrictions and say how much sample it lost. Human: 375,462 reported against 413,585 imputed; mouse 51,003 against 14,469.

The matched arm resamples both classes to one profile at 1:1 so the allele group carries no signal about the label — human to population frequency from hla_pop_freqs.tsv.gz, mouse to the positives' own profile, there being no H-2 frequency table. It is small: 52 of 75 human allele groups are too scarce to fill their population weight and are dropped, which is the cost of the matching, not a bug.

Regenerate, in ~/vcs/projects/2026-mhcmatch-benchmark:

python immunogenicity/fetch.py            # stage the export from ~/hf/pmhc_data/dump
bench/neoag/corpus_iedb.py --workers 12   # ~55 min, 14.7M netMHCpan predictions
bench/neoag/deposit_arms.py

Arm counts, the filter cascade and the selection tree are bench/results/corpus_arms.md.

Caveat carried forward. These negatives are still inferred — an eluted ligand nobody has tested is assumed non-immunogenic. 38,322 rows in the rebuild have a measured negative T-cell assay instead, held under evidence tcell_negative and deliberately not merged into the arm: swapping the negative definition changes what every AUROC recorded on these corpora means.

immunogenicity_legacy_arms.tsv.gz (2026-08-19)

The published deposits and the legacy Kešmir arms in one table, keyed corpus ∈ {chowell_vanilla, kesmir_S1, kesmir_S2, kesmir_S3}, so a held-out evaluation can be reproduced without the copyrighted spreadsheets. 15,991 rows.

Species is keyed on the restricting allele, not on the deposit's own Species column. The two disagree on 777 of the 2,508 Calis rows (31%), which carry Species = Mus with an HLA restriction — HLA-transgenic mice, murine host and human restricting molecule. Both readings describe the experiment; they build different corpora. Keyed on the host, the Calis human arm is 1,113 immunogenic against one non-immunogenic; keyed on the allele it is 1,619 against 272 and the mouse arm is H-2 throughout. The reported host is retained as host_reported.

Regenerate with bench/neoag/corpus_legacy.py then deposit_arms.py.

What the recognition model is fitted on

chowell_iedb_full is the training arm for mhcmatch.recognition (0.15.0). Two choices were made by measurement and are recorded in bench/results/recognition_model.md and feature_importance.md:

• The unmatched arm ships. Resampling negatives to match population HLA usage costs 0.019 (human) and 0.058 (mouse) AUROC on held-out published deposits.
• ESM2 and the physicochemical features are both kept. ESM2 adds +0.022/+0.042 over physchem alone; physchem adds +0.009/+0.019 over ESM2 alone. Permutation importance on held-out data ranks the two ESM face-pools first (0.19–0.27), then kf_tcr; the whole-peptide Kidera aggregate is worth 0.0165/0.0012 and is not shipped, being exactly the sum of the two role columns.

staged/ — the IEDB full export, as staged (2026-08-19)

The root of the provenance chain. Every rebuilt corpus in this directory (chowell_iedb_full*, immunogenicity_legacy_arms) is derived from these two tables, and until now they lived only in a gitignored working directory — one bad checkout from being unreproducible. Deposited so the chain closes without re-downloading 450 MB from IEDB.

file	provenance	rows
staged/iedb_tcell.parquet	experimental — IEDB tcell_full_v3_tsv.zip, build 2026-08-11, downloaded 2026-08-18	570,434 linear-peptide rows, 233,636 distinct peptides
staged/iedb_ligand.parquet	experimental — IEDB mhc_ligand_full_tsv.zip, same build	5,773,495 linear-peptide rows, 1,704,996 distinct peptides
staged/fetch.py	the script that produced both	—
staged/FETCH.md	its record: zip mtimes, export row counts, alignment failures, class and qualitative-measurement breakdowns	—

Ten columns are kept from each export; nothing is filtered beyond Linear peptide and a non-empty name, because length, alphabet, class and host filters are corpus-construction rules and belong where they can be stated as rules. 0 rows failed to align in either table — IEDB ships two header lines and embedded newlines in comment fields, so fetch.py reads with csv.reader rather than splitting on tabs, and reports the misalignment count rather than assuming it is zero.

The T-cell export is what makes a measured negative possible: 361,962 Negative rows across all hosts, where the older harmonised iedb_labeled.tsv.gz keeps only positive T-cell assays and its assay_type is therefore perfectly collinear with its label.

Regenerate (needs ~/hf/pmhc_data/dump, which is gitignored — the raw zips are not deposited):

python immunogenicity/staged/fetch.py

corpus_iedb_mhc2.parquet — the MHC class-II arm (2026-08-19)

Derived/computed, from staged/iedb_tcell.parquet and staged/iedb_ligand.parquet — the same IEDB build 2026-08-11 export the class-I corpus is built from, by the same rules, so the two arms are comparable by construction rather than by assertion. This is what the shipped class-II complementarity model (mhcmatch.complement, cls="mhc2", v0.16.0) is fitted on.

field	value
rows (peptide, allele group, host)	1,096,034
distinct peptides	643,744
immunogenic peptides	77,943
human rows	1,036,041
human positives	65,486
human distinct peptides	603,781
human allele groups	121
mouse rows	59,993
mouse positives	12,457
mouse distinct peptides	50,258
mouse allele groups	40
peptide length	11–25, median 15

Three rules differ from the class-I arm and nothing else does:

• Length is 11–25 rather than 8–11.
• Restriction is parsed, never imputed. Class-II restriction is written a dozen ways across IEDB (HLA-DRB1*15:01, HLA-DPA1*01:03/DPB1*04:01, HLA-DR15, serotypes, H2-IAb), so the corpus carries a parser and keeps whatever it cannot resolve as an explicit unresolved group rather than guessing an allele. It parses 1,003,554 / 1,974,413 rows (50.8 %); the remainder are retained, not dropped, because the complementarity fit reads only peptide and label. There is therefore one class-II arm where class I has two — no HLA-matched variant is built, since matching needs a resolved allele on both sides.
• Rule 8 (pathogen epitopes) is applied by parsing the source organism rather than by set-match.

evidence records why each row is what it is: eluted_but_positive_elsewhere=5,305, eluted_foreign=103,351, eluted_self=1,156,574, eluted_unknown=440,458, tcell_negative=110,312, tcell_positive=158,413.

corpus_iedb_mhc2.log is the generator's own run record, deposited beside it; the counts in the table above are read from it. The build is deterministic — regenerated 2026-08-19 from a clean checkout, every count reproduced exactly.

Regenerate (needs this mirror at $MHCMATCH_PMHC_DIR, and the benchmark repo):

python bench/neoag/corpus_iedb.py --cls mhc2