Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data

Abstract

Background: Fusion transcripts are formed by either fusion genes (DNA level) or trans-splicing events (RNA level). They have been recognized as a promising tool for diagnosing, subtyping and treating cancers. RNA-seq has become a precise and efficient standard for genome-wide screening of such aberration events. Many fusion transcript detection algorithms have been developed for paired-end RNA-seq data but their performance has not been comprehensively evaluated to guide practitioners. In this paper, we evaluated 15 popular algorithms by their precision and recall trade-off, accuracy of supporting reads and computational cost. We further combine top-performing methods for improved ensemble detection.Results: Fifteen fusion transcript detection tools were compared using three synthetic data sets under different coverage, read length, insert size and background noise, and three real data sets with selected experimental validations. No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA. We further demonstrated the potential of a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation.Conclusion: Our result provides insightful recommendations when applying individual tool or combining top performers to identify fusion transcript candidates.

InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data

“…We ran InFusion along with five widely used tools for fusion detection—TopHat-Fusion, deFuse, ChimeraScan, SOAPfuse and fusionCatcher—on the generated RNA-seq datasets. We selected the first three tools for the assessment since they were reported to have high sensitivity and specificity in comparison to other tools [30] while the last two are quite novel and demonstrated the highest accuracy in a recent comparison study [45]. …”

Global analysis of somatic structural genomic alterations and their impact on gene expression in diverse human cancers

“…Recent studies have relied on RNA-seq data to detect fusion genes in comprehensive tumor materials (37,38). However, similar to SV analysis, RNA-seq-based fusion detection is prone to both false positives and negatives (39), for example due to read-mapping issues or misinterpreted germ-line events (40). Therefore, we explored whether fusion gene detection can be improved by combining RNA and DNA data.…”

Fusion Genes and their Detection through Next Generation Sequencing in Malignant Hematological Disease and Solid Tumors

“…Several computational tools have also been developed for the detection of fusion transcripts using RNA-Seq data, such as MapSplice [51], ShortFuse [52], FusionHunter [44], FusionMap [53], SnowShoes-FTD [54],defuse [55], chimerascan [56], FusionCatcher [57], TopHatFusion [44], BreakFusion [58], EricScript [59], SOAPfuse [60], FusionQ [61] , PRADA [62] and JAFFA [63]. Liu et al [64] performed a large-scale comparative study by applying these above 15 fusion transcript detection pipelines to 3 synthetic data sets and 3 real pairedend RNA-seq studies and developed a meta-caller algorithm to combine three top-performing methods (FusionCatcher, SOAPfusea and JAFFA). If possible, it is recommended to apply all three above pipelines and combine the results in applications.…”