TY - JOUR T1 - deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data A1 - McPherson, Andrew A1 - Hormozdiari, Fereydoun A1 - Zayed, Abdalnasser A1 - Giuliany, Ryan A1 - Ha, Gavin A1 - Sun, Mark G. F. A1 - Griffith, Malachi A1 - Heravi Moussavi, Alireza A1 - Senz, Janine A1 - Melnyk, Nataliya A1 - Pacheco, Marina A1 - Marra, Marco A. A1 - Hirst, Martin A1 - Nielsen, Torsten O. A1 - Sahinalp, S. Cenk A1 - Huntsman, David A1 - Shah, Sohrab P. Y1 - 2011/05/19 N2 - Author Summary Genome rearrangements and associated gene fusions are known to be important oncogenic events in some cancers. We have developed a novel computational method called deFuse for detecting gene fusions in RNA-Seq data and have applied it to the discovery of novel gene fusions in sarcoma and ovarian tumors. We assessed the accuracy of our method and found that deFuse produces substantially better sensitivity and specificity than two other published methods. We have also developed a set of 60 positive and 61 negative examples that will be useful for accurate identification of gene fusions in future RNA-Seq datasets. We have trained a classifier on 11 novel features of the 121 examples, and show that the classifier is able to accurately identify real gene fusions. The 45 gene fusions reported in this study represent the first ovarian cancer fusions reported, as well as novel sarcoma fusions. By examining the expression patterns of the affected genes, we find that many fusions are predicted to have functional consequences and thus merit experimental followup to determine their clinical relevance. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 7 IS - 5 UR - https://doi.org/10.1371/journal.pcbi.1001138 SP - e1001138 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1001138 ER -