FuGePrior: A novel gene fusion prioritization algorithm based on accurate fusion structure analysis in cancer RNA-seq samples

Abstract

BackgroundLatest Next Generation Sequencing technologies opened the way to a novel era of genomic studies, allowing to gain novel insights into multifactorial pathologies as cancer. In particular gene fusion detection and comprehension have been deeply enhanced by these methods. However, state of the art algorithms for gene fusion identification are still challenging. Indeed, they identify huge amounts of poorly overlapping candidates and all the reported fusions should be considered for in lab validation clearly overwhelming wet lab capabilities.ResultsIn this work we propose a novel methodological approach and tool named FuGePrior for the prioritization of gene fusions from paired-end RNA-Seq data. The proposed pipeline combines state of the art tools for chimeric transcript discovery and prioritization, a series of filtering and processing steps designed by considering modern literature on gene fusions and an analysis on functional reliability of gene fusion structure.Conclusions FuGePrior performance has been assessed on two publicly available paired-end RNA-Seq datasets: The first by Edgren and colleagues includes four breast cancer cell lines and a normal breast sample, whereas the second by Ren and colleagues comprises fourteen primary prostate cancer samples and their paired normal counterparts. FuGePrior results accounted for a reduction in the number of fusions output of chimeric transcript discovery tools that ranges from 65 to 75% depending on the considered breast cancer cell line and from 37 to 65% according to the prostate cancer sample under examination. Furthermore, since both datasets come with a partial validation we were able to assess the performance of FuGePrior in correctly prioritizing real gene fusions. Specifically, 25 out of 26 validated fusions in breast cancer dataset have been correctly labelled as reliable and biologically significant. Similarly, 2 out of 5 validated fusions in prostate dataset have been recognized as priority by FuGePrior tool.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1450-6) contains supplementary material, which is available to authorized users.

Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods

“…A major challenge in benchmarking using real RNA-seq is that the truth set cannot be perfectly defined. Earlier benchmarking studies of fusion prediction accuracy using RNA-seq from cancer cell lines [15, 28, 32, 54, 55] relied on 53 experimentally validated fusion transcripts from four breast cancer cell lines: BT474, KPL4, MCF7, and SKBR3 [56–59] (Additional file 1: Table S3). However, these fusions arguably represent too small a target truth set for rigorous benchmarking, and the catalog of true fusions for these four cell lines may still be incomplete.…”

Novel and Rare Fusion Transcripts Involving Transcription Factors and Tumor Suppressor Genes in Acute Myeloid Leukemia

“…Fusion genes were detected on RNA-seq data by applying FuGePrior pipeline to the gene fusion lists provided by ChimeraScan [56] and deFuse [57] tools. According to FuGePrior workflow [58], fusions with the following features were removed: (i) not supported by split reads (i.e., reads harboring the fusion breakpoint); (ii) involving at least one unannotated partner gene; (iii) shared by healthy samples; (iv) characterized by a non-reliable structure; (v) having at least the driver score probability lower than 0.7. The DS score was a measure of the probability of the fusion being an oncogenic event, according to Pegasus [59] and Oncofuse [60].…”

Transcriptional-Readthrough RNAs Reflect the Phenomenon of “A Gene Contains Gene(s)” or “Gene(s) within a Gene” in the Human Genome, and Thus Are Not Chimeric RNAs

“…On the other hand, more publications continue emerging to report [ 95 ] or summarize [ 3 , 69 , 70 ] such trans -splicing related chimeras or other noncolinear RNAs. Moreover, many bioinformatic experts are establishing different algorithms to cull chimeras from different sets of high-throughput sequencing data [ 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 ], although all these data sets contain many spurious sequences, as we and others have pointed out [ 5 , 6 , 64 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 ]. This situation is worrisome to us.…”