TY  - JOUR
AU  - Huang, Zhiqin
AU  - Jones, David
AU  - Wu, Yonghe
AU  - Lichter, Peter
AU  - Zapatka, Marc
TI  - confFuse: High-Confidence Fusion Gene Detection across Tumor Entities.
JO  - Frontiers in genetics
VL  - 8
SN  - 1664-8021
CY  - Lausanne
PB  - Frontiers Media
M1  - DKFZ-2017-04647
SP  - 137
PY  - 2017
AB  - Background: Fusion genes play an important role in the tumorigenesis of many cancers. Next-generation sequencing (NGS) technologies have been successfully applied in fusion gene detection for the last several years, and a number of NGS-based tools have been developed for identifying fusion genes during this period. Most fusion gene detection tools based on RNA-seq data report a large number of candidates (mostly false positives), making it hard to prioritize candidates for experimental validation and further analysis. Selection of reliable fusion genes for downstream analysis becomes very important in cancer research. We therefore developed confFuse, a scoring algorithm to reliably select high-confidence fusion genes which are likely to be biologically relevant. Results: confFuse takes multiple parameters into account in order to assign each fusion candidate a confidence score, of which score ≥8 indicates high-confidence fusion gene predictions. These parameters were manually curated based on our experience and on certain structural motifs of fusion genes. Compared with alternative tools, based on 96 published RNA-seq samples from different tumor entities, our method can significantly reduce the number of fusion candidates (301 high-confidence from 8,083 total predicted fusion genes) and keep high detection accuracy (recovery rate 85.7
LB  - PUB:(DE-HGF)16
C6  - pmid:29033976
C2  - pmc:PMC5627533
DO  - DOI:10.3389/fgene.2017.00137
UR  - https://inrepo02.dkfz.de/record/128631
ER  -