Add sourced InterPro annotation datasets

README.md

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+# interpro_repeat
+
+Protein-level multi-label dataset sourced from InterPro entry metadata and
+UniProtKB protein-to-InterPro cross-references.
+
+## Intended use
+
+Protein repeat annotation prediction from sequence. This evaluates
+recovery of curated InterPro classifications, not an experimental assay result.
+
+## Source and labels
+
+- InterPro metadata: `https://ftp.ebi.ac.uk/pub/databases/interpro/current_release/entry.list`
+- UniProt REST API: `https://rest.uniprot.org/uniprotkb/stream`
+- Card generated (UTC): `2026-10-05`
+- Organism Taxonomy ID: `9606` (`all` = all organisms).
+- Review status: `reviewed` (`reviewed` = Swiss-Prot, `unreviewed` = TrEMBL, `all` = both).
+- UniProt query: `(organism_id:9606) AND (reviewed:true)`
+- UniProt TSV fields: `accession,sequence,xref_interpro,organism_id,reviewed`
+- InterPro entry type: `Repeat`
+- Consider entries observed in at least 5 proteins.
+- After splitting, retain entries with at least 20
+    positives in training, 3 in validation,
+    and 3 in test.
+- `targets` is a multi-hot vector ordered by InterPro accession. Accession,
+  display name, and type are recorded in `label_vocabulary.json`.
+- Proteins without a retained entry before splitting are dropped; after
+    support filtering, other split variants may contain all-zero targets.
+
+## Splits
+
+Whole MMseqs2 `easy-linclust` clusters are assigned to splits targeting
+`{'train': 0.8, 'validation': 0.1, 'test': 0.1}`, with minimum identity
+`0.3`, minimum coverage
+`0.8`, and
+`1` thread(s). Seed: `1957723`.
+When `create_split_subsets` is enabled, pooled random, stratified, and
+hold-cluster-out subsets are also included. Hold-cluster-out subsets require
+enough MMseqs clusters to populate all three roles.
+The optional maximum sequence length is `None` and is
+applied before vocabulary construction and MMseqs2 clustering.
+
+## Split sizes
+
+- `test`: 156 rows
+- `test_cluster`: 156 rows
+- `test_random`: 156 rows
+- `test_stratified`: 156 rows
+- `train`: 1251 rows
+- `train_cluster`: 1251 rows
+- `train_random`: 1251 rows
+- `train_stratified`: 1251 rows
+- `validation`: 156 rows
+- `validation_cluster`: 156 rows
+- `validation_random`: 156 rows
+- `validation_stratified`: 156 rows
+
+Vocabulary size: 9.
+
+## Dataset statistics
+
+[`stats.json`](stats.json) at the dataset root contains row counts by split,
+columns, and SeqKit sequence-length metrics.

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label_vocabulary.json

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+{
+  "dataset_name": "interpro_repeat",
+  "vocabulary_version": 1,
+  "labels": [
+    {
+      "accession": "IPR001611",
+      "name": "Leucine-rich repeat",
+      "entry_type": "Repeat"
+    },
+    {
+      "accession": "IPR001680",
+      "name": "WD40 repeat",
+      "entry_type": "Repeat"
+    },
+    {
+      "accession": "IPR002110",
+      "name": "Ankyrin repeat",
+      "entry_type": "Repeat"
+    },
+    {
+      "accession": "IPR002172",
+      "name": "Low-density lipoprotein (LDL) receptor class A repeat",
+      "entry_type": "Repeat"
+    },
+    {
+      "accession": "IPR003591",
+      "name": "Leucine-rich repeat, typical subtype",
+      "entry_type": "Repeat"
+    },
+    {
+      "accession": "IPR006652",
+      "name": "Kelch repeat type 1",
+      "entry_type": "Repeat"
+    },
+    {
+      "accession": "IPR008160",
+      "name": "Collagen triple helix repeat",
+      "entry_type": "Repeat"
+    },
+    {
+      "accession": "IPR019734",
+      "name": "Tetratricopeptide repeat",
+      "entry_type": "Repeat"
+    },
+    {
+      "accession": "IPR020472",
+      "name": "PAC1/LIS1-like, WD-40 repeat",
+      "entry_type": "Repeat"
+    }
+  ]
+}

stats.json

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+{
+  "rows_total": 6252,
+  "rows_by_split": {
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+    "validation": 156,
+    "test": 156,
+    "train_random": 1251,
+    "validation_random": 156,
+    "test_random": 156,
+    "train_stratified": 1251,
+    "validation_stratified": 156,
+    "test_stratified": 156,
+    "train_cluster": 1251,
+    "validation_cluster": 156,
+    "test_cluster": 156
+  },
+  "columns": [
+    "id",
+    "sequence",
+    "targets",
+    "split"
+  ],
+  "seqkit_stats": [
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+      "file": "test.fasta.gz",
+      "type": "Protein",
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+}