Add dataset card for ChatNT-style DNALongBench conversion

README.md

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+---
+language:
+- en
+license: other
+task_categories:
+- text-classification
+tags:
+- genomics
+- dna
+- long-context
+- benchmark
+- chat-format
+size_categories:
+- 10K<n<100K
+---
+
+# DNALongBench ChatNT-Style v1
+
+This repository contains a **derived, ChatNT-style serialization** of two long-range binary classification tasks from [DNALongBench](https://github.com/wenduocheng/DNALongBench): enhancer-target gene prediction (ETGP) and eQTL prediction (eQTLP). It is intended to make the tasks convenient for DNA + text multimodal model evaluation and supervised fine-tuning.
+
+It is **not an official DNALongBench release** and does not include the original source tables or reference genomes. Please cite and comply with the original DNALongBench data sources and licenses when using this derivative.
+
+## Contents
+
+```text
+etgp_v1/
+  train.jsonl.gz
+  valid.jsonl.gz
+  test.jsonl.gz
+  manifest.json
+eqtlp_v1/
+  train.jsonl.gz
+  valid.jsonl.gz
+  test.jsonl.gz
+  manifest.json
+```
+
+All JSONL files are gzip-compressed. Each sample has a fixed 450,000 bp DNA context.
+
+| Task | Train | Validation | Test | Positive labels in test |
+|---|---:|---:|---:|---:|
+| ETGP | 2,066 | 266 | 270 | 10 |
+| eQTLP | 20,364 | 6,554 | 4,297 | 190 |
+
+The official DNALongBench splits are preserved. Both tasks are strongly imbalanced, so AUROC should be accompanied by AUPRC and class-aware metrics.
+
+## Tasks
+
+### ETGP: enhancer-target gene prediction
+
+Given a 450 kb genomic context and metadata for an enhancer candidate, target gene, and K562 cell type, predict whether the enhancer regulates the gene (`Yes` or `No`).
+
+### eQTLP: eQTL prediction
+
+Given matched 450 kb reference and alternate allele contexts plus tissue and gene metadata, predict whether the variant is an eQTL (`Yes` or `No`).
+
+## Data format
+
+Each line is a standalone JSON object. The conversation format follows ChatNT-style multimodal references: text references a named DNA span, while DNA is stored separately in `dna_sequences`.
+
+```json
+{
+  "id": "dnalongbench_etgp_k562_test_0000022",
+  "messages": [
+    {
+      "role": "user",
+      "content": "Task: enhancer-target gene prediction. Does the tested enhancer regulate BAX in K562, based on the long genomic sequence <DNA_1>? Answer Yes or No."
+    },
+    {"role": "assistant", "content": "No"}
+  ],
+  "dna_sequences": [
+    {"name": "DNA_1", "role": "genomic_context", "sequence": "ACGT..."}
+  ],
+  "target": "No",
+  "meta": {
+    "task_name": "etgp",
+    "split": "test",
+    "label": 0,
+    "sequence_length": 450000
+  }
+}
+```
+
+For eQTLP, `dna_sequences` contains `DNA_REF` and `DNA_ALT` rather than a single `DNA_1` sequence. Metadata contain provenance fields such as chromosome, gene ID, tissue/cell type, genomic distance, alleles, and masking statistics where available. The user message never contains the target label.
+
+## Sequence construction
+
+The conversion mirrors the official DNALongBench EPI/eQTL dataset loaders:
+
+- ETGP uses a gene TSS window (+/- 3 kb) and enhancer-region window (+/- 500 bp), then takes the genomic interval spanning both.
+- eQTLP follows the same long-context construction and creates reference/alternate sequence pairs from the two alleles.
+- Intermediate blacklist regions are masked with `N`; sequences shorter than 450 kb are padded with `N`, while longer contexts are truncated to 450 kb.
+- Cross-chromosome and over-distance eQTL records are excluded consistently with the official loading logic. The resulting eQTLP conversion skipped 67 records.
+- When genomic orientation is reversed relative to gene direction, the sequence is reverse complemented.
+
+The 450 kb context is a benchmark design choice: it makes input length fixed for fair long-context comparison and allows models to use distal regulatory evidence. It is not a claim that every base is functional.
+
+## Loading example
+
+```python
+import gzip
+import json
+
+path = "etgp_v1/train.jsonl.gz"
+with gzip.open(path, "rt", encoding="utf-8") as handle:
+    sample = json.loads(next(handle))
+
+print(sample["messages"][0]["content"])
+print(sample["target"])
+print(len(sample["dna_sequences"][0]["sequence"]))
+```
+
+## Recommended evaluation
+
+Use the provided validation/test splits without resampling them. Report at least AUROC and AUPRC; additionally report MCC, accuracy, and threshold-selection protocol. Because text fields may carry useful biological priors, DNA + text results should be compared with the following controls:
+
+1. DNA-only.
+2. Text/metadata-only.
+3. DNA + text.
+4. DNA + shuffled metadata.
+
+## Provenance and citation
+
+Source benchmark: [DNALongBench repository](https://github.com/wenduocheng/DNALongBench) and its associated paper, *DNALongBench: Benchmarking long-context genomic sequence models* ([Nature Communications, 2025](https://www.nature.com/articles/s41467-025-65077-4)).
+
+Please cite the DNALongBench paper and the original underlying datasets. Cite this repository as a derived conversion, not as the source of the biological labels or reference sequence.
+
+## Limitations
+
+- This is a task-format conversion, not a newly curated biological dataset.
+- The natural-language instructions are templated from task metadata; they should not be interpreted as free-form annotations.
+- Large compressed files contain raw DNA strings. Use streaming readers rather than loading all samples into memory.
+- Public redistribution is subject to the terms of the original benchmark, reference genome, and source datasets. Verify compliance before redistribution or publication.