In this exploratory study, with annotation, reference genome and variants matched, SpliceAI and Pangolin put 63 to 66 assay-confirmed splice disruptions in their top 100 of the same 8,297 held-out MFASS variants. Masking raised average precision slightly for both tools; no top-100 difference was established.
Genomics MCP is a local MCP server that gives AI agents bounded, provenance-tracked access to real genomic data: discover public files, then read bounded regions from bigWig, BAM, CRAM and VCF. Includes a case study where Claude used only its tools to compare BCL11A enhancer signal across 26 public ENCODE files.
A recall reversal and two constant-prediction controls show why benchmark comparisons need populations, selection rules and reference scores.
The baseline, SpliceAI and Pangolin found 61, 64 and 65 disruptions in their top 100 MFASS variants. The differences do not establish a reliable winner.
RNA models share an alphabet, not an estimand: choose them by molecular object, output, information inputs, evidence class, and artifact contract.
A genomic model result is credible only when its tokenisation, strand rule, biological target, evaluation split, and artifact terms are explicit.
Protein generators propose different biological objects. A defensible design starts with the assay, records the complete computational stack, and preserves every experimental denominator.
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.
