Benchmarking seq2func models on distal enhancer effects with CRISPRi screens

We benchmark four sequence-to-function genomic deep learning models — Enformer, Borzoi, NTv3, and AlphaGenome — zero-shot on two K562 CRISPRi enhancer-knockdown screens (Fulco et al., 2019 and Gasperini et al., 2019), extending the in-silico CRISPRi setup from Karollus et al., 2023. Three things stand out: * **AlphaGenome leads on both screens** (Pearson's r = 0.67 on Fulco et al., 0.45 on Gasperini et al.), with Borzoi a close second on Fulco et al. and a more distant second on Gasperini et al. * **All four models systematically underpredict knockdown magnitude**, and the gap to experimental measurements widens with enhancer-to-TSS distance — distal cis-regulatory element (CRE) effects remain difficult to predict. * **AlphaGenome's RNA-Seq head (with GENCODE-exon aggregation) beats its CAGE head** on the larger screen — which output track you choose matters. **Code**: seq2func_crispri_eval