DeepVRegulome: DNABERT-based deep-learning framework for predicting the functional impact of short genomic variants on the human regulome

תקציר מקורי באנגליתarXiv:2511.09026v2 Announce Type: replace-cross Abstract: Whole-genome sequencing (WGS) has revealed numerous non-coding short variants whose functional impacts remain poorly understood. Despite recent advances in deep-learning genomic approaches, accurately predicting and prioritizing clinically relevant mutations in gene regulatory regions remains a major challenge. We developed DeepVRegulome, a computational framework integrating 464 fine-tuned DNABERT models (458 transcription factor, 4 histone mark, and 2 splice site models) trained on ENCODE and GENCODE datasets. The framework pairs these deep learning models with a suite of analytical tools: quantitative variant scoring via log-odds ratios to assess functional impact, attention-based motif analysis to identify disrupted sequence pat