DNA & Gene Models
Foundation models trained on genomic sequences can decode regulatory elements, predict gene expression, and interpret the functional consequences of genetic variants across the non-coding genome. These models are transforming our ability to understand how DNA sequence encodes biological function, enabling applications from variant effect prediction to CRISPR guide design. By learning the grammar of the genome at scale, they help researchers connect sequence changes to phenotypic outcomes in disease and development.
41 models in this category
Notable Models
Top-rated dna & gene models from our evaluations
AlphaGenome
Google DeepMind
Google DeepMind model that predicts thousands of functional genomic tracks at single base-pair resolution from megabase-scale DNA sequences.
BioMed Multi-Omic
IBM Research
Open-source framework for building RNA and DNA foundation models, featuring WCED pretraining for transcriptomics and SNP-aware encoding for genomics.
Borzoi
Calico Life Sciences
Deep learning model predicting cell-type-specific RNA-seq coverage at 32 bp resolution from 524 kb of DNA sequence, jointly modeling transcription, splicing, and polyadenylation.
Evo 2
Arc Institute
Genomic foundation model trained on 9.3 trillion DNA base pairs spanning all domains of life, with 40B parameters and a 1-million-token context window.
VariantFormer
Chan Zuckerberg Initiative / Chan Zuckerberg Biohub
A 1.2-billion-parameter hierarchical transformer that predicts personalized gene expression from diploid genomes, integrating individual genetic variants for ancestry-robust eQTL analysis.
AIDO.DNA
genbio.ai
A 7-billion-parameter encoder-only DNA foundation model trained on 10.6 billion nucleotides from 796 species for functional genomics and synthetic biology.