DNA & Gene
DNA & Gene Models
Genomic sequence modeling and gene expression analysis
400 models in this category
What DNA & gene foundation models do
DNA and gene foundation models learn the regulatory and functional grammar of genomic sequences, predicting how nucleotide changes propagate through gene regulatory networks to alter expression, splicing, and cellular phenotype. Models like Enformer predict cell-type-specific gene expression tracks from sequence alone, while Evo and the Nucleotide Transformer learn broader representations spanning prokaryotic and eukaryotic genomes. DNABERT and its successors apply BERT-style masking to DNA k-mers, enabling fine-tuning for tasks from promoter classification to variant effect prediction.
Common applications and use cases
Variant effect prediction is among the highest-value applications: models like Enformer and Sei can score non-coding variants for regulatory impact, informing GWAS interpretation and rare disease diagnosis. Regulatory element classification — identifying enhancers, promoters, and silencers — and CRISPR guide efficiency scoring are other well-established use cases. Evo's pretraining at genomic scale also supports sequence generation tasks, including the design of novel regulatory elements and protein-coding sequences.
Notable Models
Top-rated dna & gene models from our evaluations
3.1K305K632
Sparse attention transformer that extends BERT to 8x longer sequences via random, local, and global attention, with genomic sequence applications.
DNA & Gene
48Openness
2.2K—403
Noncoding variant effect prediction from DNA sequence, scoring how an allele shifts 919 chromatin features across ENCODE and Roadmap cell types.
DNA & Gene
83Openness
1.3K—15.2K
Transformer that predicts gene expression and epigenomic signals from 200kb of DNA sequence, capturing distal enhancers up to 100kb from a promoter.
DNA & Gene
84Openness
970—269
Convolutional neural network that predicts DNA accessibility from sequence across 164 DNase-seq cell types, enabling variant effect prediction.
DNA & Gene
80Openness
786—46
Cell-type-specific gene expression prediction from DNA sequence, mapping Enformer epigenomic features to pseudobulk expression for cell-resolved TWAS.
DNA & GeneSingle-cell
569—138
Tissue-specific gene expression prediction from DNA sequence, scoring a noncoding variant as the log fold change it causes in each of 218 tissues.
DNA & GeneRNA
19Openness
Frequently asked questions
What is a DNA and gene foundation model?
A DNA and gene foundation model is a neural network pretrained on large collections of genomic sequences — DNA or RNA — to learn representations of regulatory syntax, coding potential, and sequence function. These representations transfer to downstream tasks like variant effect prediction, gene expression modeling, and regulatory element classification. Examples include Enformer, Nucleotide Transformer, and Evo.
How do genomic foundation models handle the non-coding genome?
Most genomic foundation models are pretrained on whole-genome sequences that include non-coding regions, meaning they implicitly encode information about regulatory elements, transposons, and intergenic space. Models like Enformer were specifically designed to predict transcription factor binding and chromatin accessibility from non-coding sequence windows, making them well-suited to interpreting GWAS hits in regulatory regions.
What is the difference between DNABERT and Enformer?
DNABERT applies BERT-style masked language modeling to tokenized DNA k-mers, producing general-purpose genomic embeddings useful across many classification and regression tasks. Enformer is an architecture explicitly trained to predict genomic assay tracks (CAGE, ATAC-seq, ChIP-seq) from long input windows of up to 200 kb, making it specifically powerful for gene expression and regulatory prediction rather than general-purpose sequence representation.
Can DNA foundation models predict variant pathogenicity?
Yes, this is one of the most actively developed applications. Models like Enformer, Sei, and Nucleotide Transformer can score the predicted functional impact of single-nucleotide variants by comparing reference and alternate sequence outputs. However, most current models are stronger at regulatory variants in well-characterized tissues than at rare coding variants, and calibration against clinical databases remains an active area of evaluation.