Panel Discussion
summary
Surveyed panelists synthesize concrete gaps and wins: reproducibility and benchmarking lag despite production of massive single cell and long read datasets, deep learning now predicts epigenomic effects of single nucleotides but fails to predict differential expression between conditions, and third‑generation sequencing plus spatial omics (examples include MERFISH and Hi‑C integration) create new data‑management and annotation burdens. Practical takeaways: need standards, metadata, model‑driven experimental design, long‑term training pathways, and regulatory rigor for clinical translation (FDA‑style validation works but is slow).
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