A practical guide to choosing and running genomic foundation models for diagnostic DNA and RNA testing. Covers DNABERT-2, Nucleotide Transformer, HyenaDNA, Caduceus, Evo 2, and AlphaGenome across eight clinical workflows, with a worked BRCA1 benchmark and measured cloud GPU costs, memory limits, and cross-GPU reproducibility.
In its developers' comparisons, SpliceAI2 scored above SpliceAI, Pangolin and AlphaGenome on every benchmark printed, including cryptic splicing in Genomics England blood RNA-seq. Every value is developer-reported, most test variants lie in genes the model was trained on, no uncertainty is printed, and motif scoring, the baseline for both use cases, was not compared.
The best-supported RNA metagenomic result is 93.6% sensitivity for one UCSF assay, reported by its developers. A new Oxford nanopore validation found 51%. No study compares workflows prospectively against an adjudicated reference.
Eight published somatic SNV and indel caller comparisons, read side by side: rankings change between studies, indel accuracy is lower, and no study covers FFPE, ctDNA or panels.
Fragment-feature classifiers reach AUCs near 0.9 on low-pass plasma sequencing and, in one developer study, beat ichorCNA tumour fraction when ctDNA is scarce. Rankings change between cohorts, and no fragmentomic method has been tested in an intended-use population.
GPU and DRAGEN pipelines cut a whole-genome run from 17 to 39 hours on CPUs to between about 25 minutes and two and a half hours, and the one study that compared their calls with the CPU pipeline found close agreement. No study reruns a pipeline to test reproducibility, and FP8 gave a genomic foundation model at most 19.3% more throughput.
SpliceAI and Pangolin lead the DNA-based predictors on variants tested in patient RNA, and SpliceAI beats motif scoring on the same data, but no predictor replaces the RNA test. RNA-seq outlier callers recover between none and most known events and flag outliers in nearly every negative sample.
Sequence models, variant-effect prediction, and the work to read non-coding DNA.
Single-sequence folding, protein language models, and structure without alignment.
Contrastive screening, protein–ligand interaction, and design that survives the wet lab.
What models encode, where they fail, and how evaluation and oversight hold up.
