7 articles tagged with #clinical-genomics.
An evidence-first review of genomic foundation models across clinical molecular diagnostic workflows. Compares DNABERT-2, Nucleotide Transformer, HyenaDNA, Caduceus, Evo 2, and AlphaGenome against established clinical tools across eight diagnostic tasks and post-calling variant effect prediction.
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.
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.
Short-read genome callers recover most deletions above 10 kb. Small deletions, duplications and exome data are where they fail, and read depth alone misses most 1 to 5 kb deletions. There is no reliable evidence that a machine-learning caller beats the read-depth baseline on the same data.
Two H100s on different clouds gave bit-identical Evo 2 scores for 3,893 BRCA1 variants. An L40S running the same container reshuffled two thirds of the ranking. Running the model in FP32 brought the two GPUs back into agreement, at three to six times the compute.
One pretrained genomic model can look dominant on a leaderboard and lose to a linear baseline on the next task. Two evidence ledgers explain why.
