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Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data.

Fernandez-Cuesta L, Sun R, Menon R, George J, Lorenz S, Meza-Zepeda LA, Peifer M, Plenker D, Heuckmann JM, Leenders F, Zander T, Dahmen I, Koker M, Schöttle J, Ullrich RT, Altmüller J, Becker C, Nürnberg P, Seidel H, Böhm D, Göke F, Ansén S, Russell PA, Wright GM, Wainer Z, Solomon B, Petersen I, Clement JH, Sänger J, Brustugun OT, Helland Å, Solberg S, Lund-Iversen M, Buettner R, Wolf J, Brambilla E, Vingron M, Perner S, Haas SA, Thomas RK - Genome Biol. (2015)

Bottom Line: Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties.Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets.We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondary rearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8.

View Article: PubMed Central - PubMed

ABSTRACT
Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline) (https://github.com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondary rearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8.

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Related in: MedlinePlus

Comparison between TRUP and other publically available fusion detection tools. (a) Feature comparison: TRUP adopts breakpoint assembly after a sensitive detection of potential fusion points. Note that only TRUP and TopHat-Fusion are integrated into regular RNA-seq analysis pipelines, that is, the mapping results are shared for fusion detection and regular RNA-seq analysis. Alternative tools adopt various split-read mapping strategy specifically for fusion detection, generating customized mapping results, which could not be easily re-used for other purposes. (b) Computing resources consumed by TRUP and other tools for processing the data of sample S00054: resources used in the step of mapping are isolated for each tool to indicate the cost only for fusion detection and further processing (excl.: excluding). TRUP* indicates that TRUP is run with STAR as the mapper instead of GSNAP. (c) A heatmap showing the number of non-redundant reads spanning fusion-points for candidates predicted by at least two tools in sample S00054 (referred to as ‘shared fusions’ which is used as a gold set for evaluation). TRUP* indicates that TRUP is run with STAR as the mapper instead of GSNAP. Underlined fusions are experimentally validated.
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Fig3: Comparison between TRUP and other publically available fusion detection tools. (a) Feature comparison: TRUP adopts breakpoint assembly after a sensitive detection of potential fusion points. Note that only TRUP and TopHat-Fusion are integrated into regular RNA-seq analysis pipelines, that is, the mapping results are shared for fusion detection and regular RNA-seq analysis. Alternative tools adopt various split-read mapping strategy specifically for fusion detection, generating customized mapping results, which could not be easily re-used for other purposes. (b) Computing resources consumed by TRUP and other tools for processing the data of sample S00054: resources used in the step of mapping are isolated for each tool to indicate the cost only for fusion detection and further processing (excl.: excluding). TRUP* indicates that TRUP is run with STAR as the mapper instead of GSNAP. (c) A heatmap showing the number of non-redundant reads spanning fusion-points for candidates predicted by at least two tools in sample S00054 (referred to as ‘shared fusions’ which is used as a gold set for evaluation). TRUP* indicates that TRUP is run with STAR as the mapper instead of GSNAP. Underlined fusions are experimentally validated.

Mentions: In order to evaluate the performance of TRUP we applied eight additional fusion detection tools to the data of sample S00054: chimerascan [4], FusionHunter [20], FusionMap [9], TopHat-Fusion [8], deFuse [7], SOAPfuse [21], FusionSeq [6], and BreakFusion [10]. For two tools (FusionSeq and BreakFusion) evaluation could not be carried out because of computational limitations. Details about parameter settings are provided in Additional file 7. Despite the fact that most tools use both read-pair analysis and split-read mapping for the detection of fusion transcripts, they vary widely in terms of resources required and computing time (Figure 3a and b). Although TRUP additionally capitalizes on regional assembly of potential fusion-points, its overall performance in terms of disc space, memory size, and running time is superior to the others (Figure 3b). Also, even though the sensitive split mapping via GSNAP takes more time in the case of TRUP, the re-usability of the mapped data will eventually save time when regular RNA-seq analyses are performed. Alternatively, STAR can be used, as this mapper dramatically decreases the mapping time (Figure 3b) although it is a little less sensitive than GSNAP (Figure 3c). Notably, only the mapping results generated by TRUP and TopHat-Fusion are reusable for regular RNA-seq analysis, whereas other tools perform customized split-read mapping specifically for fusion detection (Figure 3a).Figure 3


Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data.

Fernandez-Cuesta L, Sun R, Menon R, George J, Lorenz S, Meza-Zepeda LA, Peifer M, Plenker D, Heuckmann JM, Leenders F, Zander T, Dahmen I, Koker M, Schöttle J, Ullrich RT, Altmüller J, Becker C, Nürnberg P, Seidel H, Böhm D, Göke F, Ansén S, Russell PA, Wright GM, Wainer Z, Solomon B, Petersen I, Clement JH, Sänger J, Brustugun OT, Helland Å, Solberg S, Lund-Iversen M, Buettner R, Wolf J, Brambilla E, Vingron M, Perner S, Haas SA, Thomas RK - Genome Biol. (2015)

Comparison between TRUP and other publically available fusion detection tools. (a) Feature comparison: TRUP adopts breakpoint assembly after a sensitive detection of potential fusion points. Note that only TRUP and TopHat-Fusion are integrated into regular RNA-seq analysis pipelines, that is, the mapping results are shared for fusion detection and regular RNA-seq analysis. Alternative tools adopt various split-read mapping strategy specifically for fusion detection, generating customized mapping results, which could not be easily re-used for other purposes. (b) Computing resources consumed by TRUP and other tools for processing the data of sample S00054: resources used in the step of mapping are isolated for each tool to indicate the cost only for fusion detection and further processing (excl.: excluding). TRUP* indicates that TRUP is run with STAR as the mapper instead of GSNAP. (c) A heatmap showing the number of non-redundant reads spanning fusion-points for candidates predicted by at least two tools in sample S00054 (referred to as ‘shared fusions’ which is used as a gold set for evaluation). TRUP* indicates that TRUP is run with STAR as the mapper instead of GSNAP. Underlined fusions are experimentally validated.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4300615&req=5

Fig3: Comparison between TRUP and other publically available fusion detection tools. (a) Feature comparison: TRUP adopts breakpoint assembly after a sensitive detection of potential fusion points. Note that only TRUP and TopHat-Fusion are integrated into regular RNA-seq analysis pipelines, that is, the mapping results are shared for fusion detection and regular RNA-seq analysis. Alternative tools adopt various split-read mapping strategy specifically for fusion detection, generating customized mapping results, which could not be easily re-used for other purposes. (b) Computing resources consumed by TRUP and other tools for processing the data of sample S00054: resources used in the step of mapping are isolated for each tool to indicate the cost only for fusion detection and further processing (excl.: excluding). TRUP* indicates that TRUP is run with STAR as the mapper instead of GSNAP. (c) A heatmap showing the number of non-redundant reads spanning fusion-points for candidates predicted by at least two tools in sample S00054 (referred to as ‘shared fusions’ which is used as a gold set for evaluation). TRUP* indicates that TRUP is run with STAR as the mapper instead of GSNAP. Underlined fusions are experimentally validated.
Mentions: In order to evaluate the performance of TRUP we applied eight additional fusion detection tools to the data of sample S00054: chimerascan [4], FusionHunter [20], FusionMap [9], TopHat-Fusion [8], deFuse [7], SOAPfuse [21], FusionSeq [6], and BreakFusion [10]. For two tools (FusionSeq and BreakFusion) evaluation could not be carried out because of computational limitations. Details about parameter settings are provided in Additional file 7. Despite the fact that most tools use both read-pair analysis and split-read mapping for the detection of fusion transcripts, they vary widely in terms of resources required and computing time (Figure 3a and b). Although TRUP additionally capitalizes on regional assembly of potential fusion-points, its overall performance in terms of disc space, memory size, and running time is superior to the others (Figure 3b). Also, even though the sensitive split mapping via GSNAP takes more time in the case of TRUP, the re-usability of the mapped data will eventually save time when regular RNA-seq analyses are performed. Alternatively, STAR can be used, as this mapper dramatically decreases the mapping time (Figure 3b) although it is a little less sensitive than GSNAP (Figure 3c). Notably, only the mapping results generated by TRUP and TopHat-Fusion are reusable for regular RNA-seq analysis, whereas other tools perform customized split-read mapping specifically for fusion detection (Figure 3a).Figure 3

Bottom Line: Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties.Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets.We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondary rearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8.

View Article: PubMed Central - PubMed

ABSTRACT
Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline) (https://github.com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondary rearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8.

Show MeSH
Related in: MedlinePlus