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# Nucleotide Transformer

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bioRxiv Oct 16, 2026
Every base of the human genome swapped in silico: about 3.12 billion positions form a directed dependency network with seven recurring architectures
Using the DNA language model Nucleotide Transformer v3 on the JUPITER supercomputer, the team substituted each of roughly 3.12 billion positions of the telomere-to-telomere human reference genome with each of the three alternative bases and recorded how model predictions changed elsewhere, yielding a genome-wide directed map of nucleotide dependencies. These dependencies preferentially link evolutionarily conserved positions and repeatedly assemble into seven geometric structures called Relons, some outside current annotation. Combined with CADD, dependency features raised intronic-variant pathogenicity AUPRC from 0.299 to 0.356.
Using the DNA language model Nucleotide Transformer v3 on the JUPITER supercomputer, the team substituted each of roughly 3.12 billion positions of the telomere-to-telomere human reference genome with each of the three alternative bases and recorded how model predictions changed elsewhere, yielding a genome-wide directed map of nucleotide dependencies. These dependencies preferentially link evolutionarily conserved positions and repeatedly assemble into seven geometric structures called Relons, some outside current annotation. Combined with CADD, dependency features raised intronic-variant pathogenicity AUPRC from 0.299 to 0.356.
Jülich Supercomputing Centre RWTH Aachen University
Jeremias Krause（First author）Ingo Kurth（Corresponding author）
Nucleotide Dependency Maps Relons Nucleotide Transformer In Silico Perturbation Relome Browser
New Biotechnology Sep 14, 2026
Sequence optimization targeting mRNA stability enhances monoclonal antibody titers in CHO cells
This study treats mRNA stability as a tunable codon-optimization design parameter: it built a combinatorial library of synonymous coding-sequence variants of an IgG1 light chain integrated as single copies at a defined genomic locus in CHO cells, used steady-state mRNA abundance quantified by deep sequencing of gDNA and mRNA as a proxy for stability, trained a machine-learning model predicting mRNA abundance from coding sequence using embeddings from a pre-trained nucleotide transformer, and incorporated this predictor with established translational metrics into a genetic algorithm for multi-objective codon optimization; as proof-of-concept with Trastuzumab-encoding sequences, high-abundance designs raised intracellular mRNA by 41%, protein titer by 59%, and cell-specific productivity by 8
This study treats mRNA stability as a tunable codon-optimization design parameter: it built a combinatorial library of synonymous coding-sequence variants of an IgG1 light chain integrated as single copies at a defined genomic locus in CHO cells, used steady-state mRNA abundance quantified by deep sequencing of gDNA and mRNA as a proxy for stability, trained a machine-learning model predicting mRNA abundance from coding sequence using embeddings from a pre-trained nucleotide transformer, and incorporated this predictor with established translational metrics into a genetic algorithm for multi-objective codon optimization; as proof-of-concept with Trastuzumab-encoding sequences, high-abundance designs raised intracellular mRNA by 41%, protein titer by 59%, and cell-specific productivity by 8
Bayer (Germany)
Sebastian Schneckener（First author）
Codon Optimization Genetic Algorithm mRNA Stability Nucleotide Transformer Monoclonal Antibody Expression

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