miRBench / README.md
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metadata
license: cc-by-4.0
pretty_name: miRBench (curated)
size_categories:
  - 1M<n<10M
tags:
  - genomics
  - benchmark
  - rna
  - binary-classification

miRBench, curated

miRBench, as published in quality-curated genomic benchmarks - one format, fixed row order, a permanent ID on every row. 3 datasets, 6 files, 2,849,872 rows, one gzipped CSV per split, all Homo sapiens.

Getting the data

Two packages are the way in: genomic-benchmarks-data for people, genomic-benchmarks-data4agents for agents, the same functions either way. They resolve the URL, check the checksum, and carry each dataset's QC results, which this repository does not.

pip install genomic-benchmarks-data
from genomic_benchmarks_data import load_dataset

df = load_dataset("miRBench", "AGO2_CLASH_Hejret2023", "train")

A number measured on these files belongs on the leaderboard, which is what makes two of them comparable, and which prints what QC found about a dataset above every table.

Datasets

dataset task train test test_leftout
AGO2_CLASH_Hejret2023 Binary classification 8140 1018 -
AGO2_eCLIP_Klimentova2022 Binary classification - 954 -
AGO2_eCLIP_Manakov2022 Binary classification 2495535 324171 20054

Files are v1/<dataset>/<dataset>_<split>.csv.gz, where v1 is the version of the data. A dataset whose sequences change appears under a new prefix rather than being rewritten here.

Licence and provenance

Every row has an identity

Each file carries two columns the upstream release does not:

  • id - <collection>:<dataset>:<version>:<split>:<row index>, the index zero-padded to 7 digits.
  • row_index - the row's 0-based position in this file.

Row order is fixed within a major version, so an id names the same sequence for as long as that version exists - which is what makes a reported number traceable to what it was computed on. The corpus metadata carries the SHA-256 of every split file, so a copy can be checked against the audited one.

Citation

Cite the collection's own publication (https://doi.org/10.1093/bioinformatics/btaf233) for the data, and Automated quality control for genomic sequence benchmarks reveals pervasive single-feature separability and train-test leakage for the curation and the audit.