deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data

By Andrew W. McPherson, Fereydoun Hormozdiari, Abdalnasser Zayed, Ryan Giuliany, G. Ha, Mark G. F. Sun, Malachi Griffith, A. Moussavi, J. Senz, Nataliya Melnyk and 7 more. PLoS Comput. Biol., 2011

562 citations recorded by the source corpus.

Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5b7b53354cb349932ea192abfbbe76b4667b134a

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Citations in the corpus

Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas (2015, unknown): “Left 133/276 (48) 37/84 (44) 69/137 (50) 27/55 (49)”
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, methodology): “REAGENT or RESOURCE SOURCE IDENTIFIER DEXSeq Anders et al., 2012 http://www.bioconductor.org/packages/release/ bioc/html/DEXSeq.html deFuse McPherson et al., 2011 http://compbio.bccrc.ca/software/defuse/ EGC.tools (v1.”
Accurate and efficient detection of gene fusions from RNA sequencing data (2021, unknown): “8.1 [McPherson et al. 2011], FusionCatcher v1.”
FusionCatcher – a tool for finding somatic fusion genes in paired-end RNA-sequencing data (2014, methodology): “In Table 1 is presented a comparison of several fusion genes finders like SOAPFuse[18], ChimeraScan[20], deFuse[27], FusionHunter[28], SnowShoes-FTD[26], and TopHat-Fusion[26] using the same dataset[18].”
New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs (2016, unknown): “1 (McPherson et al., 2011) using default parameters.”
Next-generation transcriptome assembly (2011, background): “Detection of trans-spliced genes has been shown to be critical for understanding the genetic pathways involved in some cancers 56 , such as prostate cancer 57 .”
A survey of best practices for RNA-seq data analysis (2016, methodology): “For example, removing all fusions involving an IG gene may remove real IG fusions in lymphomas and other blood disorders; filtering fusions for which both genes are from the IG locus is preferred [88].”
Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma (2017, unknown): “BCMHGSC: TCGA RNA sequencing data (RNA FastQ files) were downloaded for the 196 patients on this freeze list set from CGHub. deFuse version 0.6.1 (McPherson et al., 2011) with default settings detected a large list of candidate fusion genes.”
Genetic alterations activating kinase and cytokine receptor signaling in high-risk acute lymphoblastic leukemia (2012, methodology): “We used multiple complementary analysis pipelines, including deFuse (McPherson et al., 2011), Mosaik (Marth, 2010), CREST (Wang et al., 2011), CONSERTING (Zhang et al., 2012), and Trans-ABySS (Robertson et al., 2010) to identify rearrangements, structural variations, and sequence mutations.”
Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods (2019, methodology): “Specifically, we downloaded and installed each of (1) Arriba [17], (2) ChimeraScan [19], (3) ChimPipe [21], (4) deFuse [24], (5) EricScript [25], (6) FusionCatcher [28], (7) FusionHunter [30], (8) InFusion [31], (9) JAFFA-Assembly [32], (10) Jaffa-Direct [32], (11) JAFFA-Hybrid [32], (12) MapSplice…”
Personalized Oncology Through Integrative High-Throughput Sequencing: A Pilot Study (2011, background): “net) has produced excellent results in cancer studies, and newer tools now enable the de novo discovery of gene fusions without the need for a reference genome(30,31).”