BioInterval-CoordinateQA-20K

RegalFire — AI Data Foundry. An original synthetic biology / computational genome arithmetic computational fixture benchmark.

Problem and differentiator

Convert interval conventions and compute feature overlaps and unique query bases covered on synthetic contigs.

Why download this instead of existing data?

Synthetic boundary tasks mix zero-based half-open and one-based closed inputs, touching/overlapping/nested intervals and strand filters, with exact feature-level overlaps and union counts. Large real annotation workloads test throughput; these small answerable fixtures isolate coordinate conversion and double-counting defects.

Research context: academic.oup.com, bedtools.readthedocs.io, arxiv.org. These references support the relevance of the task area; they do not establish customer demand or that this release improves any model.

Verified release

20,000 accepted records; 20,000 unique IDs; 20,000 unique semantic task fingerprints; 20,000 source groups. Zero rejected records. Independent computational validation: PASS. Full restarted generation was byte-identical (see reproduction_report.json). No model training/evaluation run is claimed.

Split	Records
train	15964
validation	2030
test	2006

Construction family	Records
coordinate_conversion	4000
nested	4000
overlapping_blocks	4000
stranded	4000
touching_boundary	4000

Ground-truth contract

All contigs are fictitious, named synthetic_A/B, with declared bounds. zero_half_open means [start,end); one_closed means [start,end] in one-based coordinates, converted to [start-1,end). Touching half-open endpoints do not overlap. Match only the query contig; match strand if same_strand=true. Feature indices refer to original feature order. Union coverage counts each query base once, even where features overlap. No real gene or clinical annotation is supplied.

Generator: interval arithmetic and sorted union sweep. Validator: independent discrete-position sets and intersections on small bounded contigs.

Record and file structure

Each JSONL row has id, index, seed, family, input, expected, fingerprint, group_id, split and provenance. Input contains the complete task; expected contains computable labels and witnesses where relevant. schema.json specifies exact field/type requirements. There are five full worked examples in examples/sample.json. data/dataset.jsonl is the complete canonical dataset; HF default loading uses only the disjoint train/validation/test files.

For agent evaluation, remove expected before passing inputs to the agent and keep the answer files inaccessible to it. These are public fixed-label fixtures, not contamination-resistant secret evaluations or recorded agent rollouts.

Reproduce and validate

Seed: 20261001. Published source and the generation environment are recorded in SOURCE_LICENSE.json. Exact byte reproduction was verified in that environment; matching seed alone is not a guarantee across Python versions.

pip install -r requirements.txt
python generator/generate.py --out reproduced --count 20000 --seed 20261001
python validator/validate.py --dataset reproduced/dataset.jsonl --count 20000 --out reproduced_qa
python generator/generate.py --out reproduced --count 20000 --seed 20261001 --resume
python -m unittest discover -s tests -p test_oracles.py -v

Writes use committed 500-record chunks, atomic checkpoint updates and fresh final assembly. Resume loads committed shards, verifies binding to seed/count/source, and regenerates any uncommitted work. A missing final file or checkpoint does not destroy committed records. Do not mix different seeds, counts or generator versions in one output folder.

Generator and validator implement different label algorithms; shared helpers only cover serialization, schema and canonical semantic identities. This is independent computational implementation, not independent human scientific review. Nine unit tests cover label corruption, exact small cases, schema types, missing/corrupt files, semantic symmetries and crash-resume reproduction. All passed locally; see the QA and reproduction reports.

Deduplication, splits and quality limits

Family quotas are equal. Split assignment is deterministic by source-group hash (approximately 80/10/10; exact counts above). Identical source groups never cross splits. Grid rotations/reflections are canonicalized; agent-label swaps are canonicalized in coordination tasks. Interval translation equivalents are canonicalized. DNA source grouping canonicalizes reverse complements. Each paired edit or sequence pair remains within its record.

The DNA near-duplicate audit screens 7-mer bottom-four sketches and at most 25 previous candidates per bucket, then checks Jaccard >=0.85 across split groups. It found 0 flags. This is a bounded similarity screen, not exhaustive homology analysis. Grid and interval checks cover exact stated equivalences, not all possible near-isomorphic or related scenarios. Splits are by generated source, not proven biological species or real game distribution. Public fixtures can be memorized.

Provenance, privacy and licensing

Entirely generated by the original supplied procedural source. No external dataset records, genomes, annotations, patient data, PHI, human identity profiles, proprietary game images, dialogue, maps, textures or binaries were used. Cited tools and papers are research context only; their data/code were not copied. MIT applies to this original release and generator/validator code; retain the LICENSE notice. SOURCE_LICENSE.json records permissions, attribution and environment.

Scope and limitations

Arithmetic fixtures on small synthetic contigs, not whole-genome performance tests, real annotation biology, human genomic identity data, statistical enrichment inference or clinical validation. Translation-equivalent input scenarios are deduplicated; contig bounds still constrain validity.

Tests establish the specified computational cases, not universal software correctness, biological validity of a simulation, clinical suitability, benchmark novelty over every related work or market value. Generator and validator were authored with Codex and have not been reviewed by a domain expert.

Commercial extension: Private coordinate schemas, annotation pipeline regression, strandedness policies and validated tool adapters. Domain rules and privacy/access constraints must be agreed and tested before delivery.

Custom / Private Dataset Work

RegalFire builds custom AI datasets, evaluation sets and reproducible data pipelines for research and production systems.

Available adaptations include:

• private schemas
• larger volumes
• custom domains
• custom validators
• private evaluation sets
• continuous generation
• custom environments

Contact: ootiris@gmail.com Hugging Face: RegalFire