Biology subjects

Veiner, M.

Publications and source records attributed to Veiner, M..

3 recordsLinked to original sources

bioRxiv2026

Transformer models of mutation risk at base-pair resolution identify non-coding hotspot cancer driver mutations

Recurrent somatic mutations reveal cancer drivers, but in whole genomes many non-coding hotspots are passengers generated by localized mutational processes. We developed MutFormer, a transformer/convolutional neural net model that predicts base-pair-resolution somatic mutation risk from DNA sequence alone, separately for COSMIC signatures. Trained on >90 million high-confidence mutational signature-assigned SNVs from cancer genomes, MutFormer learns extended sequence determinants beyond trinucleotide context, often spanning up to ~20 nucleotides, and recovers APOBEC, UV, POLE and SBS17 sequence preferences as well as various additional mutation risk-prone motifs. We integrated MutFormer predictions with mutation burden, signature exposures and epigenetic covariates to model neutral recurrence of individual hotspots in >18,000 tumor whole genomes. Coding-region analyses calibrated the framework against known driver genes and AlphaMissense scores, supporting conservative false-discovery estimates. In non-coding regions, most recurrent hotspots were explained by passenger mutability, whereas selected outliers were enriched near cancer genes and supported by SpliceAI, PromoterAI, AlphaGenome and expression data. Prioritized candidates include splice-region or deep-intronic hotspots in BCL6, PTEN, TCF7L2, PBRM1, PTPRT and VHL, and promoter hotspots in SHKBP1, PRSS3 and BCL2.

genomics↗

bioRxiv2026

Quantitative prediction of nonsense-mediated mRNA decay across human genes by genomic language model and large-scale mutational scanning

The molecular consequences of protein truncating variants depend strongly on whether their transcripts are eliminated by nonsense-mediated mRNA decay (NMD), yet NMD is still predicted largely from a small set of binary positional rules. How individual premature termination codons (PTCs) engage NMD across genes and transcript contexts therefore remains incompletely resolved. Here we integrated endogenous allele-specific PTC expression from large-scale genomic data, mRNA language-model prediction and high-throughput mutational scanning to revisit the rules that govern mammalian NMD. Using allele-specific expression measurements from large human cohorts, we trained NMDetective-AI on [~]14,000 somatic PTCs, and after testing it on [~]1,800 germline PTCs, found that it improves on previous models, with its accuracy approaching the reproducibility of the underlying measurements. We then generated experimental maps of NMD with deep mutational scanning, including [~]450 PTCs nearby the 50-nt penultimate exon boundary, [~]950 engineered PTCs across 9 exon lengths to resolve long-exon escape, and [~]11k PTCs across 139 genes to quantify and refine start-proximal evasion. Our results support the positional logic of mammalian NMD yet show that this logic is implemented quantitatively: the classical rules resolve into graded, gene-dependent response curves whose boundaries are shaped by transcript architecture and modulated by local sequence context. Applying this framework to population, disease and cancer datasets further identifies genes in which NMD is predicted to aggravate or ameliorate the effects of truncating variants, providing a basis for variant interpretation and for prioritizing NMD-directed therapies.

genomics↗

bioRxiv2025

Intrinsic DNA sequence determinants and tissue-specific regulation of human replication origins

The accurate duplication of the genome relies on the spatiotemporal control of DNA replication initiation, yet the determinants specifying mammalian origin locations remain elusive. By developing ORIFormer, a transformer-based neural network, we decode a complex, conserved DNA sequence grammar that accurately predicts initiation sites. This approach uncovers novel determinants, which we validate by demonstrating selection and molecular effects of motif-altering genetic variants. To characterize tissue-specific usage, we developed MuSAS, a statistical genomic method leveraging widespread mutational strand asymmetries, such as from APOBEC and mismatch repair deficiency, to map initiation zones across diverse somatic tissues. Integrating these modalities reveals that while intrinsic DNA sequence features establish a high-potential landscape of constitutive origins, tissue-specific usage is governed by local chromatin accessibility acting as a permissive switch. We suggest that human replication initiation is driven by a deterministic genetic code modulated by the epigenetic landscape, providing a unified framework for understanding genome copying.

genomics↗