%0 Journal Article %1 10.1371/journal.pcbi.1001138 %A McPherson, Andrew %A Hormozdiari, Fereydoun %A Zayed, Abdalnasser %A Giuliany, Ryan %A Ha, Gavin %A Sun, Mark G. F. %A Griffith, Malachi %A Heravi Moussavi, Alireza %A Senz, Janine %A Melnyk, Nataliya %A Pacheco, Marina %A Marra, Marco A. %A Hirst, Martin %A Nielsen, Torsten O. %A Sahinalp, S. Cenk %A Huntsman, David %A Shah, Sohrab P. %D 2011 %I Public Library of Science %J PLOS Computational Biology %K cancer-research fulltext glioma low-grade mustread rna rna-seq software tcga tcga-lgg %N 5 %P 1-16 %R 10.1371/journal.pcbi.1001138 %T deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data %U https://doi.org/10.1371/journal.pcbi.1001138 %V 7 %X 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.

@article{10.1371/journal.pcbi.1001138, abstract = {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.}, added-at = {2019-05-04T18:44:50.000+0200}, author = {McPherson, Andrew and Hormozdiari, Fereydoun and Zayed, Abdalnasser and Giuliany, Ryan and Ha, Gavin and Sun, Mark G. F. and Griffith, Malachi and Heravi Moussavi, Alireza and Senz, Janine and Melnyk, Nataliya and Pacheco, Marina and Marra, Marco A. and Hirst, Martin and Nielsen, Torsten O. and Sahinalp, S. Cenk and Huntsman, David and Shah, Sohrab P.}, biburl = {https://www.bibsonomy.org/bibtex/2dc78d2563420d1bac83a5d9d7c2bfdc9/marcsaric}, description = {deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data}, doi = {10.1371/journal.pcbi.1001138}, interhash = {2c70c735675fb4c0d1c61c29cc4d321b}, intrahash = {dc78d2563420d1bac83a5d9d7c2bfdc9}, journal = {PLOS Computational Biology}, keywords = {cancer-research fulltext glioma low-grade mustread rna rna-seq software tcga tcga-lgg}, month = {05}, number = 5, pages = {1-16}, publisher = {Public Library of Science}, timestamp = {2019-05-04T18:44:50.000+0200}, title = {deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data}, url = {https://doi.org/10.1371/journal.pcbi.1001138}, volume = 7, year = 2011 }