VIRUSBreakend: Viral Integration Recognition Using Single Breakends
preprint
OA: closed
CC-BY-ND-4.0
Abstract
Integration of viruses into infected host cell DNA can causes DNA damage and can disrupt genes. Recent cost reductions and growth of whole genome sequencing has produced a wealth of data in which viral presence and integration detection is possible. While key research and clinically relevant insights can be uncovered, existing software has not achieved widespread adoption, limited in part due to high computational costs, the inability to detect a wide range of viruses, as well as precision and sensitivity. Here, we describe VIRUSBreakend, a high-speed tool that identifies viral DNA presence and genomic integration recognition tool using single breakend variant calling. Single breakends are breakpoints in which only one side has been unambiguously placed. We show that by using a novel virus-centric single breakend variant calling and assembly approach, viral integrations can be identified with high sensitivity and a near-zero false discovery rate, even when integrated in regions of the host genome with low mappability, such as centromeres and telomeres that cannot be reliably called by existing tools. Applying VIRUSBreakend to a large metastatic cancer cohort, we demonstrate that it can reliably detect clinically relevant viral presence and integration including HPV, HBV, MCPyV, EBV, and HHV-8.
My notes (saved in your browser only)
Citation neighborhood (no data yet)
We don't have any in-corpus citations linked to this paper yet. The paper's references may be in our DB but unresolved to ``paper_id`` (resolution happens at ingest when the cited DOI matches a row we already have). Run the cross-source citation reconcile pass to retry.
References (28)
- doi:10.1016/j.bbadis.2007.12.005 via crossref
- doi:10.1128/jvi.00340-13 via crossref
- doi:10.1038/s41588-019-0558-9 via crossref
- doi:10.1038/s41586-020-2651-8 via crossref
- doi:10.1038/s41586-019-1689-y via crossref
- doi:10.1186/s12859-017-1470-x via crossref
- doi:10.1186/s12920-018-0461-8 via crossref
- doi:10.18632/oncotarget.4187 via crossref
- doi:10.1371/journal.pone.0064465 via crossref
- doi:10.1186/s13073-015-0126-6 via crossref
- doi:10.1093/bioinformatics/bts665 via crossref
- doi:10.1093/bib/bby070 via crossref
- doi:10.1093/bioinformatics/btt011 via crossref
- doi:10.1093/nar/gky180 via crossref
- doi:10.1093/bioinformatics/btr330 via crossref
- doi:10.1186/s13059-019-1891-0 via crossref
- doi:10.1186/s13059-019-1720-5 via crossref
- doi:10.1038/s41467-019-11146-4 via crossref
- doi:10.1038/ng.2295 via crossref
- doi:10.1080/15384101.2016.1191257 via crossref
- doi:10.3390/cancers11060759 via crossref
- doi:10.1084/jem.20101402 via crossref
- doi:10.1073/pnas.2011872117 via crossref
- doi:10.1093/bioinformatics/btp698 via crossref
- doi:10.1101/gr.222109.117 via crossref
- doi:10.1093/nar/gku1207 via crossref
- doi:10.1038/s41592-018-0046-7 via crossref
- doi:10.1093/bioinformatics/btr708 via crossref
Source provenance
- crossref
- last seen: 2026-05-22T01:00:06.031889+00:00
- europepmc
- last seen: 2026-05-19T01:45:01.086888+00:00
- unpaywall
- last seen: 2026-05-21T05:10:58.409756+00:00
License: CC-BY-ND-4.0