Datasets:
metadata
license: apache-2.0
task_categories:
- text-generation
tags:
- biology
- genomics
- dna
- benchmark
configs:
- config_name: tata
data_files:
- split: train
path: tata/data.parquet
- config_name: synonymous_codons
data_files:
- split: train
path: synonymous_codons/data.parquet
Carbon Perturbation Bench
Two sequence-level perturbation tasks for evaluating DNA foundation models:
tata(5,000 rows): the TATA-box motif inside a real promoter is disrupted with random nucleotide substitutions. Probes whether the model has internalised eukaryotic promoter architecture (it should assign higher log-likelihood to the intact promoter than the perturbed one).synonymous_codons(5,000 rows): codons in a real CDS are replaced with synonyms encoding the same amino acid. Probes whether the model has learned codon-usage bias (it should prefer native codon usage over the synonymous variant).
Both subsets share the same schema:
| column | description |
|---|---|
chr, start, end, strand |
hg38 locus |
length |
sequence length in bp |
original_sequence |
the real (unperturbed) sequence — positive |
sequence |
the perturbed sequence — negative control |
Usage
from datasets import load_dataset
tata = load_dataset("hf-carbon/carbon-perturbation-bench", "tata", split="train")
syn = load_dataset("hf-carbon/carbon-perturbation-bench", "synonymous_codons", split="train")
Eval recipe
Pairwise likelihood discrimination: score LL(original_sequence) vs
LL(sequence) under the model, and report mean(LL(real) >= LL(perturbed)).
A ready-to-run scorer for Carbon, GENERator, and Evo2 lives at
evaluation/perturbation_tasks.py
in the Carbon release repo.