All Competitors

Fine-tuned Enformer derivative that predicts discrete, interpretable cis-regulatory element class annotations (enhancer, promoter, insulator) directly from DNA sequence across human cell types.

Contrastive promoter-protein pretraining that aligns bacterial promoters with their encoded proteins to learn regulatory genomics representations.

Generative discrete-diffusion model that designs regulatory DNA with tunable activity and learns activity-predictive representations rivaling genomic language models.

A genome language model that adds evolutionary-rate prediction as a pretraining task, improving representations and variant effect prediction over sequence-only training.

Google DeepMind model that predicts thousands of functional genomic tracks at single base-pair resolution from megabase-scale DNA sequences. Open-sourced with public API access in January 2026.

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.

Knowledge-informed cross-species foundation model pre-trained on 101M human and mouse single-cell transcriptomes to decipher universal gene regulatory mechanisms.

Explainable sequence model for transcription initiation that identifies the minimal set of sequence rules governing human promoter activity at base-pair resolution.

Genomic language model trained on metagenomic scaffolds that learns protein co-regulation and function by modeling gene context and operon structure.

Transformer model predicting context-specific epigenomic signals across cell types using DNA sequence and transcription factor activity profiles.

DNA language model trained on over 800 species spanning 500 million years of evolution to capture conserved regulatory elements and their evolution beyond sequence alignment.

Masked DNA language model trained on 800+ species spanning 500M years of evolution, using explicit species conditioning to capture conserved regulatory elements.

Multi-modal self-supervised model pre-trained on regulatory genome sequences and transcription factor binding matrices for cell-type-specific regulatory prediction.

Transformer model that predicts gene expression and regulatory activity from 200kb DNA sequences, capturing enhancer-promoter interactions up to 100kb away.

Updated Basenji architecture enabling cross-species regulatory sequence activity prediction, trained jointly on human and mouse genomes with improved generalization.

Sparse attention transformer extending BERT to sequences up to 8x longer via random, local, and global attention patterns, with demonstrated applications in genomic sequence modeling.

Deep convolutional neural network that predicts cell-type-specific epigenetic and transcriptional profiles from DNA sequence across large mammalian genomes.

Deep convolutional neural network that learns the regulatory code of DNA accessibility from DNase-seq data across 164 cell types, enabling variant effect prediction at cis-regulatory elements.