AVID enables sensitive and accurate viral integration detection across human cancers

Abstract

Oncovirus infection is a key etiological risk factor of human cancers, which triggers virus integration in the host genome. Viral integration can lead to structural variation, gene dysfunction, and genome instability, promoting tumorigenesis. To support the investigation of virus-associated cancer and improve the detection of virus infection, we developed an algorithm called AVID (accurate viral integration detector) for viral integration detection. AVID was built by overcoming the existing detection limitations, enhancing sensitivity and accuracy, and expanding additional functions of viral integration detection. The performance of AVID was estimated in simulated datasets and experimentally validated datasets compared with other tools. To demonstrate its wide applicability, we also tested AVID on viral integration detection in multiple oncovirus-associated human cancers, including hepatocellular carcinoma (HCC), cervical cancer, and nasopharyngeal carcinoma. Taken together, our study developed an improved and applicable tool for viral integration detection and visualization to facilitate further exploration of virus-infected diseases.