README.md

metadata

pretty_name: IEDB Assay Export
license: cc-by-4.0
tags:
  - biology
  - immunology
  - epitopes
  - assays
  - iedb
  - parquet
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: test
        path: data/test-*.parquet

IEDB Assay Export

IEDB is a curated database of experimentally characterized immune epitopes, including B cell, T cell, and MHC-binding data across infectious, allergic, autoimmune, and transplant contexts.

Splits

Split	Rows
train	6,705,467
test	745,344
total	7,450,811

The split is deterministic and epitope-aware: sha256(epitope_id) % 10 == 0 goes to test; buckets 1 through 9 go to train. If an assay lacks an epitope ID, the script falls back to reference ID, assay ID, then XML filename.

Dataset Statistics

Metric	Value
XML files parsed	26,785
XML parse-error files	0
Assay rows	7,450,811
Unique references with assays	26,785
Unique epitopes with assays	2,320,500
Uncompressed XML bytes	26.32 GB
Compressed XML bytes	605.58 MB

Assay category counts:

Assay category	Rows
MhcLigandElution	4,635,502
BCell	1,419,468
MhcBinding	825,450
TCell	570,391

Top qualitative measurements:

Measurement	Rows
Positive	5,239,946
Negative	1,967,175
Positive-Low	118,346
Positive-High	69,648
Positive-Intermediate	55,696

Top epitope chemical types:

Chemical type	Rows
Peptide from protein	6,773,619
Peptide, no natural source	591,652
Discontinuous protein residues	51,083
Other Non-Sequence	14,279
Carbohydrate fragment	5,650

Load With datasets

from datasets import load_dataset

ds = load_dataset("LiteFold/IEDB")
print(ds)

row = ds["train"][0]
print(row)

Load one split directly:

from datasets import load_dataset

train = load_dataset("LiteFold/IEDB", split="train")
test = load_dataset("LiteFold/IEDB", split="test")

Stream rows without materializing the full table locally:

from datasets import load_dataset

streamed = load_dataset("LiteFold/IEDB", split="train", streaming=True)
first_row = next(iter(streamed))

Filter to positive T-cell assays:

from datasets import load_dataset

train = load_dataset("LiteFold/IEDB", split="train")
positive_tcell = train.filter(
    lambda row: row["assay_category"] == "TCell" and row["is_positive"]
)

For large jobs, prefer streaming or process the Parquet files with a columnar engine such as DuckDB, PyArrow, Polars, or Spark.

Main Columns

Column	Description
xml_file	Source XML file inside iedb_export.zip.
reference_id, pubmed_id, article_year, article_title, journal_title, authors	Reference/article metadata.
epitope_id, epitope_name	IEDB epitope identifiers and names.
epitope_structure_type, epitope_chemical_type	Parsed XML structure type and chemical type.
linear_sequence, linear_sequence_length	Linear peptide/protein sequence when present.
discontinuous_residues	Discontinuous residue string when present.
starting_position, ending_position	Natural-sequence coordinates when present.
source_organism_id, source_molecule_genbank_id	Source organism and molecule identifiers parsed from epitope structure.
assay_category, assay_id, assay_type_id	Assay type and identifiers.
qualitative_measurement, is_positive, quantitative_measurement	Assay outcome fields.
host_organism_id, host_sex, host_age, disease_state	Host and disease context when present.
mhc_allele_id, mhc_allele_types_present	MHC information when present.
cell_type, cell_tissue_type, cell_culture_conditions	Effector-cell context when present.
antigen_evidence_code, immunogen_evidence_code, assay_comments	Additional assay curation metadata.
split_bucket	Deterministic hash bucket; bucket 0 is test.

Citation

@article{vita2025iedb,
  title   = {The Immune Epitope Database ({IEDB}): 2024 update},
  author  = {Vita, Randi and others},
  journal = {Nucleic Acids Research},
  volume  = {53},
  number  = {D1},
  pages   = {D436--D443},
  year    = {2025},
  doi     = {10.1093/nar/gkae1092}
}