immuneML/README.md

BenchRep-T: immuneML-ready AIRR export

This folder contains every BenchRep-T repertoire in AIRR Rearrangement format. It also has metadata files, the benchmark's fixed fold splits, and runnable specifications for immuneML.

The specimens and rows are the same as in the native files under Mal-ID/ and immunoSEQ/. Only the columns differ: they are mapped onto one shared AIRR schema, and junctions are rebuilt where they can be checked.

• Source: built from dataset revision 165c55149366eadfef1507240aa8c0c60d391fbb with utils/immuneml_export.py and utils/immuneml_tasks.py from github.com/boyd-lab/BenchRep-T.
• Tested with: immuneML 3.0.27 (with pandas 2.3.3) and the airr 2.0.0 schema validator. See Verification.

Quick start

pip install "immuneML==3.0.27" "pandas<3"

# Download one cohort with its task files and spec (here: rheumatoid arthritis)
from huggingface_hub import snapshot_download
snapshot_download(
    "lordim/BenchRep-T", repo_type="dataset", local_dir="BenchRep-T",
    allow_patterns=["immuneML/immunoSEQ/Savola_RA/*", "immuneML/tasks/ra/*",
                    "immuneML/specs/ra.yaml"],
)

From the BenchRep-T code repository, the same download is python -m utils.huggingface_data --layout immuneml --cohort ra --output-dir BenchRep-T.

cd BenchRep-T                     # specs use paths relative to this directory
immune-ml immuneML/specs/ra.yaml results/ra

Use pandas 2 with immuneML 3.0.27. immuneML declares pandas>=2.1.0, so a plain pip install immuneML currently installs pandas 3. With pandas 3, immuneML 3.0.27 fails to import AIRR files whose text columns are only partly filled, such as Mal-ID's d_call. immuneML's own lock file pins pandas 2.1.1.

Layout

immuneML/
├── README.md               # this file
├── MANIFEST.tsv            # every file: sha256, size; repertoires also: source file, source sha256, rows
├── export_report.json      # per-cohort row counts and junction statistics
├── Mal-ID/                 metadata.csv + repertoires/   550 specimens
├── immunoSEQ/
│   ├── Savola_RA/          metadata.csv + repertoires/    91
│   ├── Musvosvi_TB/        metadata.csv + repertoires/   140
│   ├── Rawat_T1D/          metadata.csv + repertoires/   614
│   ├── Mitchell_T1D/       metadata.csv + repertoires/   196
│   └── Emerson_CMV/        metadata.csv + repertoires/   761
├── tasks/<task>/           metadata.csv, fold{0,1,2}_train.csv, fold{0,1,2}_test.csv
└── specs/<task>.yaml       one immuneML specification per task

Repertoire file names are the same as in the native folders. MANIFEST.tsv maps every exported repertoire to its native source file and that file's checksum.

Repertoire files

Each repertoire file is a gzipped, tab-separated AIRR Rearrangement file. It has one row for each row of the native file, in the same order: no rows are added, removed or merged. Every file has the same 20 columns, including all 14 fields that AIRR requires:

Column	Mal-ID	immunoSEQ cohorts (Savola, Musvosvi, Rawat, Mitchell, Emerson)
sequence_id	native sequence_id	generated as <specimen_label>|<row number>, with rows numbered from 0
sequence	native read, uppercased	native rearrangement sequence (identical to Adaptive's nucleotide)
rev_comp	F (every native strand is +)	F
productive, vj_in_frame	native t/f written as T/F	T/T (native files keep only Adaptive sequenceStatus == "In")
stop_codon	native t/f written as T/F	empty: the Adaptive exports carry no stop-codon flag
locus	TRB (native TCRB)	TRB
v_call, j_call	native, with alleles (e.g. TRBV12-3*01)	native, gene level (e.g. TRBV12-3)
d_call	native	empty (BenchRep-T doesn't harmonize Adaptive D calls)
sequence_alignment, germline_alignment, v_cigar, d_cigar, j_cigar	empty (full IgBLAST fields stay in Mal-ID/repertoires)	empty
junction, junction_aa	rebuilt; see below	rebuilt; see below
cdr3_aa	native cdr3_aa, unchanged	native cdr3_aa, unchanged
duplicate_count	empty: Mal-ID files have no count column	native num_reads (= Adaptive count (templates/reads)); empty where Adaptive gives none or -1
repertoire_id	specimen label	specimen label

cdr3_aa and junction_aa

These follow IMGT definitions:

• The junction runs from the conserved cysteine (2nd-CYS, position 104) to the conserved phenylalanine or tryptophan (J-PHE/J-TRP, position 118), both included.
• The CDR3 is the junction without those two anchor residues (positions 105–117).

cdr3_aa is the BenchRep-T benchmark sequence. It is copied from the native files unchanged, and every method in the paper uses it. It is also immuneML's default analysis region (region_type: IMGT_CDR3), so the specs analyse exactly the sequences the benchmark used.

junction and junction_aa are new. The native files don't have them; Mal-ID's columns exist but are empty. They are rebuilt from nucleotide data. A candidate junction is kept only if it passes every check:

1. its length is 3 × (length of cdr3_aa + 2);
2. it contains only A, C, G and T;
3. it translates without a stop codon;
4. its translation without the first and last residue equals cdr3_aa;
5. it occurs in sequence.

If no candidate passes, both columns are left empty and the row is still exported. Anchor residues are not required to be C and F/W; the table below reports how often they are.

How candidates are found:

• Mal-ID: take the last codon of IgBLAST's fwr3, then cdr3, then the first codon of fwr4, after removing alignment gaps (-) and uppercasing.
• Savola, Musvosvi, Mitchell: take Adaptive's annotated window, nucleotide[vIndex : vIndex + cdr3Length], which Adaptive defines to span the junction. If the window is unusable, fall back to the search below.
• Rawat, Emerson (no position columns): translate sequence in all three reading frames, find cdr3_aa, and take one codon on each side. If more than one valid candidate is found, keep the one with C…F/W anchors. If that still leaves more than one, the junction is left empty (ambiguous).

Cohort	Rows	Junction rebuilt	Anchors C…F/W¹	Not rebuilt (reason)
Mal-ID	71,302,390	99.778%	99.60%	146,897 nucleotide CDR3 doesn't translate to cdr3_aa; 11,577 length; 134 stop codon
Savola_RA	1,079,493	99.9997%	99.28%	3 length
Musvosvi_TB	13,963,315	99.9998%	99.34%	21 length
Rawat_T1D	55,538,654	99.9994%	99.50%	341 ambiguous
Mitchell_T1D	26,298,037	99.9998%	99.56%	42 length
Emerson_CMV	119,423,488	99.998%	99.45%	2,534 ambiguous; 15 stop codon

¹ Share of rebuilt junctions that start with C and end with F or W.

A "length" rejection means the annotated window's length doesn't match cdr3_aa, and the frame search found no valid alternative. Per-method and per-reason counts are in export_report.json.

Using junctions in immuneML:

• With region_type: IMGT_JUNCTION, immuneML analyses junction_aa and drops the rows without a junction (0.22% of Mal-ID rows, a handful elsewhere). Every other row is kept.
• immuneML 3.0.27 stores empty junction/junction_aa cells internally as the text NAN. Under IMGT_JUNCTION, only its default illegal-character check removes those rows. Don't combine region_type: IMGT_JUNCTION with import_illegal_characters: true.

There is no nucleotide cdr3 column, on purpose.

• It would only repeat junction[3:-3].
• immuneML 3.0.27 turns empty cells of a partly filled cdr3 column into NAN, then silently drops those rows as containing illegal characters, even under the default IMGT_CDR3 analysis.
• Without the column, immuneML derives cdr3 from junction itself, and keeps every row.

Other conversion notes

• Counts. BenchRep-T methods weight every row equally and never read counts; the example specs do the same (reads: unique). duplicate_count is provided for other analyses.
  • immuneML reads an empty duplicate_count as -1.
  • It is empty for all Mal-ID rows, and for 8,313,647 Emerson rows (7.0%) in 77 of the 761 Emerson files, where Adaptive reported no template count.
  • Mal-ID rows are kept as published, so several rows can share a clone or CDR3.
• V/J gene names. Mal-ID calls keep alleles; the immunoSEQ calls are gene level, with V genes that BenchRep-T treats as indistinguishable already collapsed. Before comparing genes across cohorts, strip alleles (TRBV7-8*01 → TRBV7-8) and collapse IMGT singleton names as utils/gene_harmonization.py::canonicalize_gene does. The example specs use cdr3_aa only.
• Rows that immuneML would filter.
  • The native files are already filtered to productive rearrangements with standard-amino-acid cdr3_aa.
  • With the default import settings (import_productive: true, import_illegal_characters: false), immuneML 3.0.27 keeps every row; this was checked on all cohorts (see Verification).

Metadata files

<cohort>/metadata.csv is comma-separated (immuneML doesn't read tab-separated metadata) and has one row per specimen:

Column	Content
filename	repertoire file name, resolved against <cohort>/repertoires/
subject_id	specimen label. immuneML's manual splits match on subject_id and require it to be unique, so it identifies the specimen, not the person
identifier	<cohort key>__<specimen label>, unique across all cohorts
participant_label	the person: specimens of one participant share it
disease	the native label, unchanged (e.g. Healthy/Background, Healthy, Controller)
fold	BenchRep-T's fixed outer fold, 0–2
age, sex, ancestry	where the native metadata has them: Mal-ID and Emerson have all three; Rawat and Mitchell have age and sex

immuneML treats every column except filename, subject_id and identifier as a label.

These files keep only the fields above. The full native metadata, including HLA, autoantibody and sequencing statistics, stays in <cohort>/metadata.tsv outside this folder. Two fields are deliberately left out here:

• Rawat's exact specimen dates;
• Mitchell's ancestry, a cohort-level constant set during conversion (see DATASHEET.md).

Tasks and folds

Each task folder lists only the task's two classes. Filenames are relative to immuneML/, so one dataset can span cohorts.

For each fold k:

• fold{k}_test.csv holds that fold's specimens;
• fold{k}_train.csv holds the other two folds.

This is exactly how the BenchRep-T evaluators split, and their per-fold predictions are pooled for the reported metrics. immuneML's k_fold and stratified_k_fold splits don't reproduce these folds: they ignore participants and draw new folds.

Task	Cohort(s)	Positive class	Negative class	Specimens	Test fold 0 / 1 / 2
malid-hiv	Mal-ID	HIV	Healthy/Background	295	99 / 99 / 97
malid-t1d	Mal-ID	T1D	Healthy/Background	293	98 / 100 / 95
malid-lupus	Mal-ID	Lupus	Healthy/Background	261	88 / 87 / 86
malid-covid19	Mal-ID	Covid19	Healthy/Background	255	85 / 86 / 84
malid-influenza	Mal-ID	Influenza	Healthy/Background	234	78 / 78 / 78
mitchell-t1d	Mitchell_T1D	T1D	Healthy/Background	196	65 / 66 / 65
t1d-pooled	Mal-ID + Mitchell_T1D	T1D	Healthy/Background	489	163 / 166 / 160
rawat-t1d	Rawat_T1D	T1D	Healthy/Background	614	205 / 205 / 204
tb-progression	Musvosvi_TB	Progressor	Controller	140	47 / 48 / 45
ra	Savola_RA	Rheumatoid Arthritis	Healthy	91	30 / 33 / 28
cmv	Emerson_CMV	CMV	Healthy/Background	761	321 / 320 / 120

Notes on the folds:

• Shared controls. The five Mal-ID tasks share the same 197 healthy controls.
• Pooled T1D. t1d-pooled adds a source_cohort column. It keeps only the demographic columns both cohorts have (age, sex).
• Emerson. Fold 2 is the independent validation cohort (cohort 2, 120 specimens); folds 0 and 1 split the discovery cohort. The paper's CMV results are fold 2.
• Rawat. The paper's Rawat results are fold 2 (204 specimens).
• RA. All 91 specimens are included. The paper's 90-specimen RA comparison is a bootstrap subset that leaves out RA6-SF; that is not an exclusion from the dataset. Participant RA6 has three specimens, all in fold 1.
• TB folds are not fully participant-grouped. Three controllers have specimens in more than one fold: 07-0177 (folds 0, 1, 2), 07-0202 (0, 2) and 04-1104 (0, 2). The export keeps the folds exactly as the benchmark used them.

Specifications

specs/<task>.yaml imports the task as an AIRR repertoire dataset with region_type: IMGT_CDR3. It encodes repertoires as 3-mer frequencies of cdr3_aa (KmerFrequency, reads: unique) and trains a class-balanced LogisticRegression. It contains one TrainMLModel instruction per fold, each using a manual split on that fold's train and test CSVs.

The specs are runnable examples of the data contract, not the benchmark's methods. Change the encodings and models as you like, but keep the dataset and manual_config blocks to stay on the benchmark folds.

Run the specs from the directory that contains immuneML/. Mal-ID (71 M rows) and Emerson (119 M rows) are the largest cohorts, so start with a smaller one.

Verification

Before publication, the export was checked as follows:

• Against the native files: every row of every file was re-read and compared with its native source. Checks: row counts; cdr3_aa, v_call, j_call and counts unchanged; sequence changed only by uppercasing; sequence_id unique per file; every rebuilt junction re-checked against the rules above.
• AIRR schema: all 287,605,377 rows in all 2,352 files pass the airr 2.0.0 Rearrangement validator.
• immuneML import (3.0.27, pandas 2.3.3), on 10 specimens × 2,000 rows from every cohort and on the full RA cohort:
  • the default import keeps every row;
  • cdr3_aa, junction_aa, V/J calls, sequence_id and duplicate_count survive the round trip;
  • immuneML's derived cdr3 equals junction[3:-3];
  • IMGT_JUNCTION drops exactly the rows without a junction.
• TrainMLModel runs:
  • all three folds of ra.yaml on the full RA cohort;
  • reduced copies of mitchell-t1d (numeric specimen IDs) and t1d-pooled (two cohorts, / in class names);
  • in every run, each fold's train/test sizes match the CSVs.

Provenance and terms

• Regenerating: from the code repository, run python -m utils.immuneml_export --snapshot <native download> --output-root <dir>, then python -m utils.immuneml_tasks --output-root <dir>. The gzip output has no timestamps or file names in it, so rerunning with the same software reproduces the checksums in MANIFEST.tsv.
• Terms: this folder is a reformatting of the native BenchRep-T files and carries the same terms and source attributions. See the dataset card and DATASHEET.md.