ERVmancer: A phylogeny-guided framework for decoding mechanisms of human endogenous retrovirus regulation in health and disease

Studying them is limited by short-read sequencing, which cannot uniquely assign reads to repetitive elements. 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 ERVmancer surpasses existing methods in sensitivity and specificity. Application recapitulates long-read observed HERV patterns in multiple sclerosis and reveals novel biology in breast cancer, including p53-mediated suppression of HERVH-LTR7, enabling scalable study of retroviral mechanisms.