ArticleNature methods2025
gReLU: a comprehensive framework for DNA sequence modeling and design.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- DeepGeSeq: deep learning library for genomic sequence modeling and analysis.Bioinformatics (Oxford, England) · 2026Article
- Expression-linked promoter selection (ELiPS) engineers short, strong ubiquitous promoters for gene therapy applications.bioRxiv : the preprint server for biology · 2026Article
- Decoding sequence determinants of gene expression in diverse cellular and disease states.Nature methods · 2026Article
- CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species.Nature methods · 2026Article
- Pre-training genomic language model with variants for better modeling functional genomics.NPJ artificial intelligence · 2026Article
- Fine-tuning sequence to function deep learning models on large-scale proteomic data improves the accuracy of variant effect prediction.bioRxiv : the preprint server for biology · 2025Article
- Pre-training Genomic Language Model with Variants for Better Modeling Functional Genomics.bioRxiv : the preprint server for biology · 2025Article
- "Frustratingly easy" domain adaptation for cross-species transcription factor binding prediction.bioRxiv : the preprint server for biology · 2025Article
- Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human Genetics.bioRxiv : the preprint server for biology · 2025Article
- Selective State Space Models Outperform Transformers at Predicting RNA-Seq Read Coverage.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning models trained on DNA sequences can predict cell-type-specific regulatory activity, reveal cis-regulatory grammar, prioritize genetic variants and design synthetic DNA. However, building and interpreting these models correctly remains difficult, and models and software built by different groups are often not interoperable. Here we present gReLU, a comprehensive software framework that enables advanced sequence modeling pipelines, including data preprocessing, modeling, evaluation, interpretation, variant effect prediction and regulatory element design.
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Registered trials
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.