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confFuse: high-confidence fusion gene detection across tumor entities
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| Veröffentlicht in: | Frontiers in genetics 8(2017) Artikel-Nummer 137, 10 Seiten |
|---|---|
| Personen und Körperschaften: | , , , |
| Titel: | confFuse: high-confidence fusion gene detection across tumor entities/ Zhiqin Huang, David T.W. Jones, Yonghe Wu, Peter Lichter and Marc Zapatka |
| Format: | E-Book-Kapitel |
| Sprache: | Englisch |
| veröffentlicht: |
29 September 2017
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| Gesamtaufnahme: |
: Frontiers in genetics, 8(2017) Artikel-Nummer 137, 10 Seiten
, volume:8 |
| Schlagwörter: | |
| Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
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| 520 | |a Background: Fusion genes play an important role in the tumorigenesis of many cancers. Next-generation sequencing (NGS) technologies have been successfully applied in fusion gene detection for the last several years, and a number of NGS-based tools have been developed for identifying fusion genes during this period. Most fusion gene detection tools based on RNA-seq data report a large number of candidates (mostly false positives), making it hard to prioritize candidates for experimental validation and further analysis. Selection of reliable fusion genes for downstream analysis becomes very important in cancer research. We therefore developed confFuse, a scoring algorithm to reliably select high-confidence fusion genes which are likely to be biologically relevant. Results: ConfFuse takes multiple parameters into account in order to assign each fusion candidate a confidence score, of which score ≥8 indicates high-confidence fusion gene predictions. These parameters were manually curated based on our experience and on certain structural motifs of fusion genes. Compared with alternative tools, based on 96 published RNA-seq samples from different tumor entities, our method can significantly reduce the number of fusion candidates (301 high-confidence from 8,083 total predicted fusion genes) and keep high detection accuracy (recovery rate 85.7%). Validation of 18 novel, high-confidence fusions detected in three breast tumor samples resulted in a 100% validation rate. Conclusions: ConfFuse is a novel downstream filtering method that allows selection of highly reliable fusion gene candidates for further downstream analysis and experimental validations. confFuse is available at https://github.com/Zhiqin-HUANG/confFuse. | ||
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| contents | Background: Fusion genes play an important role in the tumorigenesis of many cancers. Next-generation sequencing (NGS) technologies have been successfully applied in fusion gene detection for the last several years, and a number of NGS-based tools have been developed for identifying fusion genes during this period. Most fusion gene detection tools based on RNA-seq data report a large number of candidates (mostly false positives), making it hard to prioritize candidates for experimental validation and further analysis. Selection of reliable fusion genes for downstream analysis becomes very important in cancer research. We therefore developed confFuse, a scoring algorithm to reliably select high-confidence fusion genes which are likely to be biologically relevant. Results: ConfFuse takes multiple parameters into account in order to assign each fusion candidate a confidence score, of which score ≥8 indicates high-confidence fusion gene predictions. These parameters were manually curated based on our experience and on certain structural motifs of fusion genes. Compared with alternative tools, based on 96 published RNA-seq samples from different tumor entities, our method can significantly reduce the number of fusion candidates (301 high-confidence from 8,083 total predicted fusion genes) and keep high detection accuracy (recovery rate 85.7%). Validation of 18 novel, high-confidence fusions detected in three breast tumor samples resulted in a 100% validation rate. Conclusions: ConfFuse is a novel downstream filtering method that allows selection of highly reliable fusion gene candidates for further downstream analysis and experimental validations. confFuse is available at https://github.com/Zhiqin-HUANG/confFuse. |
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| spelling | Huang, Zhiqin VerfasserIn (DE-588)1100088938 (DE-627)858983303 (DE-576)35858051X aut, confFuse high-confidence fusion gene detection across tumor entities Zhiqin Huang, David T.W. Jones, Yonghe Wu, Peter Lichter and Marc Zapatka, 29 September 2017, 10, Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Gesehen am 13.09.2018, Background: Fusion genes play an important role in the tumorigenesis of many cancers. Next-generation sequencing (NGS) technologies have been successfully applied in fusion gene detection for the last several years, and a number of NGS-based tools have been developed for identifying fusion genes during this period. Most fusion gene detection tools based on RNA-seq data report a large number of candidates (mostly false positives), making it hard to prioritize candidates for experimental validation and further analysis. Selection of reliable fusion genes for downstream analysis becomes very important in cancer research. We therefore developed confFuse, a scoring algorithm to reliably select high-confidence fusion genes which are likely to be biologically relevant. Results: ConfFuse takes multiple parameters into account in order to assign each fusion candidate a confidence score, of which score ≥8 indicates high-confidence fusion gene predictions. These parameters were manually curated based on our experience and on certain structural motifs of fusion genes. Compared with alternative tools, based on 96 published RNA-seq samples from different tumor entities, our method can significantly reduce the number of fusion candidates (301 high-confidence from 8,083 total predicted fusion genes) and keep high detection accuracy (recovery rate 85.7%). Validation of 18 novel, high-confidence fusions detected in three breast tumor samples resulted in a 100% validation rate. Conclusions: ConfFuse is a novel downstream filtering method that allows selection of highly reliable fusion gene candidates for further downstream analysis and experimental validations. confFuse is available at https://github.com/Zhiqin-HUANG/confFuse., bioinformatics, biomarkers, fusion gene, Next-generation sequencing, RNA-Seq, Jones, David T. W. VerfasserIn (DE-588)1058669672 (DE-627)79739334X (DE-576)414823583 aut, Lichter, Peter 1957- VerfasserIn (DE-588)1096366282 (DE-627)856749915 (DE-576)468019049 aut, Zapatka, Marc 1974- VerfasserIn (DE-588)128629975 (DE-627)376935782 (DE-576)297249320 aut, Enthalten in Frontiers in genetics Lausanne : Frontiers Media, 2010 8(2017) Artikel-Nummer 137, 10 Seiten Online-Ressource (DE-627)65799829X (DE-600)2606823-0 (DE-576)343624826 1664-8021 nnns, volume:8 year:2017 extent:10, http://dx.doi.org/10.3389/fgene.2017.00137 Verlag Resolving-System kostenfrei Volltext, https://www.frontiersin.org/articles/10.3389/fgene.2017.00137/full Verlag kostenfrei Volltext, http://dx.doi.org/10.3389/fgene.2017.00137 LFER, LFER 2018-10-10T00:00:00Z |
| spellingShingle | Huang, Zhiqin, Jones, David T. W., Lichter, Peter, Zapatka, Marc, confFuse: high-confidence fusion gene detection across tumor entities, Background: Fusion genes play an important role in the tumorigenesis of many cancers. Next-generation sequencing (NGS) technologies have been successfully applied in fusion gene detection for the last several years, and a number of NGS-based tools have been developed for identifying fusion genes during this period. Most fusion gene detection tools based on RNA-seq data report a large number of candidates (mostly false positives), making it hard to prioritize candidates for experimental validation and further analysis. Selection of reliable fusion genes for downstream analysis becomes very important in cancer research. We therefore developed confFuse, a scoring algorithm to reliably select high-confidence fusion genes which are likely to be biologically relevant. Results: ConfFuse takes multiple parameters into account in order to assign each fusion candidate a confidence score, of which score ≥8 indicates high-confidence fusion gene predictions. These parameters were manually curated based on our experience and on certain structural motifs of fusion genes. Compared with alternative tools, based on 96 published RNA-seq samples from different tumor entities, our method can significantly reduce the number of fusion candidates (301 high-confidence from 8,083 total predicted fusion genes) and keep high detection accuracy (recovery rate 85.7%). Validation of 18 novel, high-confidence fusions detected in three breast tumor samples resulted in a 100% validation rate. Conclusions: ConfFuse is a novel downstream filtering method that allows selection of highly reliable fusion gene candidates for further downstream analysis and experimental validations. confFuse is available at https://github.com/Zhiqin-HUANG/confFuse., bioinformatics, biomarkers, fusion gene, Next-generation sequencing, RNA-Seq |
| swb_id_str | 510936083 |
| title | confFuse: high-confidence fusion gene detection across tumor entities |
| title_auth | confFuse high-confidence fusion gene detection across tumor entities |
| title_full | confFuse high-confidence fusion gene detection across tumor entities Zhiqin Huang, David T.W. Jones, Yonghe Wu, Peter Lichter and Marc Zapatka |
| title_fullStr | confFuse high-confidence fusion gene detection across tumor entities Zhiqin Huang, David T.W. Jones, Yonghe Wu, Peter Lichter and Marc Zapatka |
| title_full_unstemmed | confFuse high-confidence fusion gene detection across tumor entities Zhiqin Huang, David T.W. Jones, Yonghe Wu, Peter Lichter and Marc Zapatka |
| title_in_hierarchy | confFuse: high-confidence fusion gene detection across tumor entities / Zhiqin Huang, David T.W. Jones, Yonghe Wu, Peter Lichter and Marc Zapatka, |
| title_short | confFuse |
| title_sort | conffuse high confidence fusion gene detection across tumor entities |
| title_sub | high-confidence fusion gene detection across tumor entities |
| topic | bioinformatics, biomarkers, fusion gene, Next-generation sequencing, RNA-Seq |
| topic_facet | bioinformatics, biomarkers, fusion gene, Next-generation sequencing, RNA-Seq |
| url | http://dx.doi.org/10.3389/fgene.2017.00137, https://www.frontiersin.org/articles/10.3389/fgene.2017.00137/full |