ArticleNature communications2025
DNALONGBENCH: a benchmark suite for long-range DNA prediction tasks.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Leveraging genomic deep learning models for the prediction of noncoding variant effects.Nature genetics · 2026Review
- LAMBDA: a prophage detection benchmark for genomic language models.NAR genomics and bioinformatics · 2026Article
- [Applications and Challenges of Deep Learning in Human Genome Research].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- LAMBDA: A Prophage Detection Benchmark for Genomic Language Models.bioRxiv : the preprint server for biology · 2026Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- DNALONGBENCH: a benchmark suite for long-range DNA prediction tasks.Nature communications · 2025Article
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- MutBERT: probabilistic genome representation improves genomics foundation models.Bioinformatics (Oxford, England) · 2025Article
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
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8 authors.
Funding
Abstract
Modeling long-range DNA dependencies is crucial for understanding genome structure and function across diverse biological contexts. However, effectively capturing these dependencies, which may span millions of base pairs in tasks such as three-dimensional (3D) chromatin folding prediction, remains a major challenge. A comprehensive benchmark suite for evaluating tasks that rely on long-range dependencies is notably absent. To address this gap, we introduce DNALONGBENCH, a benchmark dataset covering five key genomics tasks with long-range dependencies up to 1 million base pairs: enhancer-target gene interaction, expression quantitative trait loci, 3D genome organization, regulatory sequence activity, and transcription initiation signals. We assess DNALONGBENCH using five methods: a task-specific expert model, a convolutional neural network (CNN)-based model, and three fine-tuned DNA foundation models - HyenaDNA, Caduceus-Ph, and Caduceus-PS. We envision DNALONGBENCH as a standardized resource to enable comprehensive comparisons and rigorous evaluations of emerging DNA sequence-based deep learning models that account for long-range dependencies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.