FusionScan: accurate prediction of fusion genes from RNA-Seq data
Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far,...
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BioMed Central
2019-07-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gi-2019-17-3-e26.pdf |
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| author | Pora Kim Ye Eun Jang Sanghyuk Lee |
| author_facet | Pora Kim Ye Eun Jang Sanghyuk Lee |
| author_sort | Pora Kim |
| collection | DOAJ |
| description | Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate. Here, we present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. We specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, we have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. We also provide several curative means to help users investigate the details of fusion candidates easily. We believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community. FusionScan is freely available at http://fusionscan.ewha.ac.kr/. |
| format | Article |
| id | doaj-art-09a8e5dbc18a41dc96df5d8fb668ea48 |
| institution | Kabale University |
| issn | 2234-0742 |
| language | English |
| publishDate | 2019-07-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-09a8e5dbc18a41dc96df5d8fb668ea482025-02-02T11:27:59ZengBioMed CentralGenomics & Informatics2234-07422019-07-0117310.5808/GI.2019.17.3.e26565FusionScan: accurate prediction of fusion genes from RNA-Seq dataPora Kim0Ye Eun Jang1Sanghyuk Lee2 Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul 03760, Korea Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul 03760, Korea Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul 03760, KoreaIdentification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate. Here, we present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. We specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, we have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. We also provide several curative means to help users investigate the details of fusion candidates easily. We believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community. FusionScan is freely available at http://fusionscan.ewha.ac.kr/.http://genominfo.org/upload/pdf/gi-2019-17-3-e26.pdfchromosomal translocationfusion transcriptgene fusionRNA-Seqtranscriptome sequencing |
| spellingShingle | Pora Kim Ye Eun Jang Sanghyuk Lee FusionScan: accurate prediction of fusion genes from RNA-Seq data Genomics & Informatics chromosomal translocation fusion transcript gene fusion RNA-Seq transcriptome sequencing |
| title | FusionScan: accurate prediction of fusion genes from RNA-Seq data |
| title_full | FusionScan: accurate prediction of fusion genes from RNA-Seq data |
| title_fullStr | FusionScan: accurate prediction of fusion genes from RNA-Seq data |
| title_full_unstemmed | FusionScan: accurate prediction of fusion genes from RNA-Seq data |
| title_short | FusionScan: accurate prediction of fusion genes from RNA-Seq data |
| title_sort | fusionscan accurate prediction of fusion genes from rna seq data |
| topic | chromosomal translocation fusion transcript gene fusion RNA-Seq transcriptome sequencing |
| url | http://genominfo.org/upload/pdf/gi-2019-17-3-e26.pdf |
| work_keys_str_mv | AT porakim fusionscanaccuratepredictionoffusiongenesfromrnaseqdata AT yeeunjang fusionscanaccuratepredictionoffusiongenesfromrnaseqdata AT sanghyuklee fusionscanaccuratepredictionoffusiongenesfromrnaseqdata |