Fuscan: A robust DNA fusion caller for targeted sequencing data in cancer diagnostics

Clicks: 1
ID: 315258
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #41 of 104 articles by views in Bioinformatics advances

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Motivation Gene fusions resulting from genomic structural variation in somatic cells have been increasingly identified as central events driving oncogenesis. Ultra-deep targeted sequencing of driver fusions informs therapeutic selection in precision oncology. However, most SV callers were primarily architected for WGS, failing to resolve the artifacts and alignment errors that drive false-positive calls in high-depth targeted data. Results Here, we describe Fuscan, a robust DNA fusion caller specifically optimized for targeted sequencing data to identify oncogenic drivers. Fuscan improves sensitivity by focusing alignment on targeted driver sequences while simultaneously filtering homologous genomic regions to prevent false-positive partner-gene breakpoints. We performed targeted sequencing on 85 NSCLC clinical specimens (comprising tissue and body fluids), four structural variant (SV) reference standards at 0.5% allele frequency, and 282 healthy-control leukocyte samples. We benchmarked Fuscan against established SV callers, achieving an AUC of 0.992 and demonstrating its robustness in challenging clinical scenarios, including low-tumor-content tissues and liquid biopsies. Availability Fuscan is available on our GitHub repository: https://github.com/YJmedLab/Fuscan.
Reference Key
openalex_W7162800020 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Liu Z, Siyu Wang, Si Chen, Haiyan Feng, Xiao Hu, Ping Zhou, Shi Dong
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag152
URL
https://doi.org/10.1093/bioadv/vbag152
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.