# NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction

arXiv: https://arxiv.org/abs/2511.02888 · HTML: https://mlsanity.com/paper/2511.02888/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 97 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution](https://mlsanity.com/paper/2306.15794/) (2023)
- [DPLM-2: A Multimodal Diffusion Protein Language Model](https://mlsanity.com/paper/2410.13782/) (2024)
- [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://mlsanity.com/paper/2403.00043/) (2024)
- [GENERator: A Long-Context Generative Genomic Foundation Model](https://mlsanity.com/paper/2502.07272/) (2025)
- [Genomic Language Models: Opportunities and Challenges](https://mlsanity.com/paper/2407.11435/) (2024)
- [Multi-Scale Representation Learning for Protein Fitness Prediction](https://mlsanity.com/paper/2412.01108/) (2024)
- [GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models](https://mlsanity.com/paper/2406.01627/) (2024)
- [DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA](https://mlsanity.com/paper/2412.05430/) (2024)
- [SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model](https://mlsanity.com/paper/2506.01833/) (2025)

## Built on by (most cited first, 1 of 1)

- [ViroBench: Benchmarking Nucleotide Foundation Models on Viral Genomics Tasks](https://mlsanity.com/paper/2605.25388/) (2026)

## Abstract (arXiv)

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.