构建大规模核酸功能预测基准，统一评估各类模型表现。
NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction
- 整合162项高通量实验数据，覆盖260万条突变序列。
- 在零样本、少样本等场景下系统评估29个基础模型性能。
- 提供可复现基准，助力基因设计与合成生物学研究。
核苷酸序列变异可显著影响功能适应度。近年来的核苷酸基础模型有望直接从序列预测此类适应度变化，但数据集异质性和预处理不一致导致跨DNA/RNA家族的方法难以公平比较。本文提出NABench，一个大规模、系统化的核酸适应度预测基准。NABench聚合了162项高通量实验，整理出260万条跨越多种DNA和RNA家族的突变序列，具备标准化划分与丰富元数据。结果表明，NABench在规模、多样性与数据质量上超越现有基准。在统一评估框架下，我们严格测试了29个代表性基础模型在零样本、少样本、迁移学习及监督学习场景下的表现。结果揭示了不同任务与核酸类型间的性能差异，明确各建模策略的优势与局限，确立了强而可复现的基线。我们已公开NABench，以推动核酸建模发展，支持RNA/DNA设计、合成生物学与生物化学下游应用。代码见https://github.com/mrzzmrzz/NABench。
原文摘要 · Abstract (English)
Nucleotide sequence variation can induce significant shifts in functional fitness. Recent nucleotide foundation models promise to predict such fitness effects directly from sequence, yet heterogeneous datasets and inconsistent preprocessing make it difficult to compare methods fairly across DNA and RNA families. Here we introduce NABench, a large-scale, systematic benchmark for nucleic acid fitness prediction. NABench aggregates 162 high-throughput assays and curates 2.6 million mutated sequences spanning diverse DNA and RNA families, with standardized splits and rich metadata. We show that NABench surpasses prior nucleotide fitness benchmarks in scale, diversity, and data quality. Under a unified evaluation suite, we rigorously assess 29 representative foundation models across zero-shot, few-shot prediction, transfer learning, and supervised settings. The results quantify performance heterogeneity across tasks and nucleic-acid types, demonstrating clear strengths and failure modes for different modeling choices and establishing strong, reproducible baselines. We release NABench to advance nucleic acid modeling, supporting downstream applications in RNA/DNA design, synthetic biology, and biochemistry. Our code is available at https://github.com/mrzzmrzz/NABench.
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