ERVmancer: a phylogeny-guided framework for decoding human endogenous retrovirus regulatory mechanisms in health and disease

Abstract

Human endogenous retroviruses (HERVs), remnants of ancient infections, comprise approximately 8% of the human genome and influence development, immunity, and cancer. Their repetitive sequences complicate short-read analysis because reads often cannot be assigned to individual loci. We present ERVmancer, a phylogeny-informed method that resolves mapping ambiguity and quantifies HERV expression from individual loci to entire clades. Benchmarking with matched long- and short-read data shows that ERVmancer surpasses existing methods in sensitivity and specificity. Applications of ERVmancer in multiple sclerosis and breast cancer reproduce long-read expression patterns and identify p53-mediated suppression of HERVH-LTR7, enabling scalable study of retroviral regulation.