SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines

Background Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:Creating an automated, standardized pipeline for SV prediction.Identifying the best tool(s) for SV prediction through benchmarking.Providing a statistically sound method for merging SV calls. Results The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Leung, Wai Yi [verfasserIn] Marschall, Tobias Paudel, Yogesh Falquet, Laurent Mei, Hailiang Schönhuth, Alexander Maoz (Moss), Tiffanie Yael

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Structural Variation SV tool Meta-tool Non-human genome Standardized pipeline SV prediction Benchmarking Next-Generation Sequencing Analysis SV tool development

Anmerkung:	© Leung et al. 2015

Übergeordnetes Werk:	Enthalten in: BMC genomics - London : BioMed Central, 2000, 16(2015), 1 vom: 25. März
Übergeordnetes Werk:	volume:16 ; year:2015 ; number:1 ; day:25 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s12864-015-1376-9

Katalog-ID:	SPR027104443

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520			\|a Background Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:Creating an automated, standardized pipeline for SV prediction.Identifying the best tool(s) for SV prediction through benchmarking.Providing a statistically sound method for merging SV calls. Results The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers.
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allfields	10.1186/s12864-015-1376-9 doi (DE-627)SPR027104443 (SPR)s12864-015-1376-9-e DE-627 ger DE-627 rakwb eng Leung, Wai Yi verfasserin aut SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Leung et al. 2015 Background Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:Creating an automated, standardized pipeline for SV prediction.Identifying the best tool(s) for SV prediction through benchmarking.Providing a statistically sound method for merging SV calls. Results The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers. Structural Variation (dpeaa)DE-He213 SV tool (dpeaa)DE-He213 Meta-tool (dpeaa)DE-He213 Non-human genome (dpeaa)DE-He213 Standardized pipeline (dpeaa)DE-He213 SV prediction (dpeaa)DE-He213 Benchmarking (dpeaa)DE-He213 Next-Generation Sequencing Analysis (dpeaa)DE-He213 SV tool development (dpeaa)DE-He213 Marschall, Tobias aut Paudel, Yogesh aut Falquet, Laurent aut Mei, Hailiang aut Schönhuth, Alexander aut Maoz (Moss), Tiffanie Yael aut Enthalten in BMC genomics London : BioMed Central, 2000 16(2015), 1 vom: 25. März (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:16 year:2015 number:1 day:25 month:03 https://dx.doi.org/10.1186/s12864-015-1376-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 25 03
spelling	10.1186/s12864-015-1376-9 doi (DE-627)SPR027104443 (SPR)s12864-015-1376-9-e DE-627 ger DE-627 rakwb eng Leung, Wai Yi verfasserin aut SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Leung et al. 2015 Background Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:Creating an automated, standardized pipeline for SV prediction.Identifying the best tool(s) for SV prediction through benchmarking.Providing a statistically sound method for merging SV calls. Results The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers. Structural Variation (dpeaa)DE-He213 SV tool (dpeaa)DE-He213 Meta-tool (dpeaa)DE-He213 Non-human genome (dpeaa)DE-He213 Standardized pipeline (dpeaa)DE-He213 SV prediction (dpeaa)DE-He213 Benchmarking (dpeaa)DE-He213 Next-Generation Sequencing Analysis (dpeaa)DE-He213 SV tool development (dpeaa)DE-He213 Marschall, Tobias aut Paudel, Yogesh aut Falquet, Laurent aut Mei, Hailiang aut Schönhuth, Alexander aut Maoz (Moss), Tiffanie Yael aut Enthalten in BMC genomics London : BioMed Central, 2000 16(2015), 1 vom: 25. März (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:16 year:2015 number:1 day:25 month:03 https://dx.doi.org/10.1186/s12864-015-1376-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 25 03
allfields_unstemmed	10.1186/s12864-015-1376-9 doi (DE-627)SPR027104443 (SPR)s12864-015-1376-9-e DE-627 ger DE-627 rakwb eng Leung, Wai Yi verfasserin aut SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Leung et al. 2015 Background Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:Creating an automated, standardized pipeline for SV prediction.Identifying the best tool(s) for SV prediction through benchmarking.Providing a statistically sound method for merging SV calls. Results The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers. Structural Variation (dpeaa)DE-He213 SV tool (dpeaa)DE-He213 Meta-tool (dpeaa)DE-He213 Non-human genome (dpeaa)DE-He213 Standardized pipeline (dpeaa)DE-He213 SV prediction (dpeaa)DE-He213 Benchmarking (dpeaa)DE-He213 Next-Generation Sequencing Analysis (dpeaa)DE-He213 SV tool development (dpeaa)DE-He213 Marschall, Tobias aut Paudel, Yogesh aut Falquet, Laurent aut Mei, Hailiang aut Schönhuth, Alexander aut Maoz (Moss), Tiffanie Yael aut Enthalten in BMC genomics London : BioMed Central, 2000 16(2015), 1 vom: 25. März (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:16 year:2015 number:1 day:25 month:03 https://dx.doi.org/10.1186/s12864-015-1376-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 25 03
allfieldsGer	10.1186/s12864-015-1376-9 doi (DE-627)SPR027104443 (SPR)s12864-015-1376-9-e DE-627 ger DE-627 rakwb eng Leung, Wai Yi verfasserin aut SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Leung et al. 2015 Background Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:Creating an automated, standardized pipeline for SV prediction.Identifying the best tool(s) for SV prediction through benchmarking.Providing a statistically sound method for merging SV calls. Results The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers. Structural Variation (dpeaa)DE-He213 SV tool (dpeaa)DE-He213 Meta-tool (dpeaa)DE-He213 Non-human genome (dpeaa)DE-He213 Standardized pipeline (dpeaa)DE-He213 SV prediction (dpeaa)DE-He213 Benchmarking (dpeaa)DE-He213 Next-Generation Sequencing Analysis (dpeaa)DE-He213 SV tool development (dpeaa)DE-He213 Marschall, Tobias aut Paudel, Yogesh aut Falquet, Laurent aut Mei, Hailiang aut Schönhuth, Alexander aut Maoz (Moss), Tiffanie Yael aut Enthalten in BMC genomics London : BioMed Central, 2000 16(2015), 1 vom: 25. März (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:16 year:2015 number:1 day:25 month:03 https://dx.doi.org/10.1186/s12864-015-1376-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 25 03
allfieldsSound	10.1186/s12864-015-1376-9 doi (DE-627)SPR027104443 (SPR)s12864-015-1376-9-e DE-627 ger DE-627 rakwb eng Leung, Wai Yi verfasserin aut SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Leung et al. 2015 Background Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:Creating an automated, standardized pipeline for SV prediction.Identifying the best tool(s) for SV prediction through benchmarking.Providing a statistically sound method for merging SV calls. Results The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers. Structural Variation (dpeaa)DE-He213 SV tool (dpeaa)DE-He213 Meta-tool (dpeaa)DE-He213 Non-human genome (dpeaa)DE-He213 Standardized pipeline (dpeaa)DE-He213 SV prediction (dpeaa)DE-He213 Benchmarking (dpeaa)DE-He213 Next-Generation Sequencing Analysis (dpeaa)DE-He213 SV tool development (dpeaa)DE-He213 Marschall, Tobias aut Paudel, Yogesh aut Falquet, Laurent aut Mei, Hailiang aut Schönhuth, Alexander aut Maoz (Moss), Tiffanie Yael aut Enthalten in BMC genomics London : BioMed Central, 2000 16(2015), 1 vom: 25. März (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:16 year:2015 number:1 day:25 month:03 https://dx.doi.org/10.1186/s12864-015-1376-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2015 1 25 03
language	English
source	Enthalten in BMC genomics 16(2015), 1 vom: 25. März volume:16 year:2015 number:1 day:25 month:03
sourceStr	Enthalten in BMC genomics 16(2015), 1 vom: 25. März volume:16 year:2015 number:1 day:25 month:03
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Structural Variation SV tool Meta-tool Non-human genome Standardized pipeline SV prediction Benchmarking Next-Generation Sequencing Analysis SV tool development
isfreeaccess_bool	true
container_title	BMC genomics
authorswithroles_txt_mv	Leung, Wai Yi @@aut@@ Marschall, Tobias @@aut@@ Paudel, Yogesh @@aut@@ Falquet, Laurent @@aut@@ Mei, Hailiang @@aut@@ Schönhuth, Alexander @@aut@@ Maoz (Moss), Tiffanie Yael @@aut@@
publishDateDaySort_date	2015-03-25T00:00:00Z
hierarchy_top_id	326644954
id	SPR027104443
language_de	englisch
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author	Leung, Wai Yi
spellingShingle	Leung, Wai Yi misc Structural Variation misc SV tool misc Meta-tool misc Non-human genome misc Standardized pipeline misc SV prediction misc Benchmarking misc Next-Generation Sequencing Analysis misc SV tool development SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines
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topic_title	SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines Structural Variation (dpeaa)DE-He213 SV tool (dpeaa)DE-He213 Meta-tool (dpeaa)DE-He213 Non-human genome (dpeaa)DE-He213 Standardized pipeline (dpeaa)DE-He213 SV prediction (dpeaa)DE-He213 Benchmarking (dpeaa)DE-He213 Next-Generation Sequencing Analysis (dpeaa)DE-He213 SV tool development (dpeaa)DE-He213
topic	misc Structural Variation misc SV tool misc Meta-tool misc Non-human genome misc Standardized pipeline misc SV prediction misc Benchmarking misc Next-Generation Sequencing Analysis misc SV tool development
topic_unstemmed	misc Structural Variation misc SV tool misc Meta-tool misc Non-human genome misc Standardized pipeline misc SV prediction misc Benchmarking misc Next-Generation Sequencing Analysis misc SV tool development
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title	SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines
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title_full	SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines
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author_browse	Leung, Wai Yi Marschall, Tobias Paudel, Yogesh Falquet, Laurent Mei, Hailiang Schönhuth, Alexander Maoz (Moss), Tiffanie Yael
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title_sort	sv-autopilot: optimized, automated construction of structural variation discovery and benchmarking pipelines
title_auth	SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines
abstract	Background Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:Creating an automated, standardized pipeline for SV prediction.Identifying the best tool(s) for SV prediction through benchmarking.Providing a statistically sound method for merging SV calls. Results The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers. © Leung et al. 2015
abstractGer	Background Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:Creating an automated, standardized pipeline for SV prediction.Identifying the best tool(s) for SV prediction through benchmarking.Providing a statistically sound method for merging SV calls. Results The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers. © Leung et al. 2015
abstract_unstemmed	Background Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:Creating an automated, standardized pipeline for SV prediction.Identifying the best tool(s) for SV prediction through benchmarking.Providing a statistically sound method for merging SV calls. Results The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers. © Leung et al. 2015
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title_short	SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines
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author2	Marschall, Tobias Paudel, Yogesh Falquet, Laurent Mei, Hailiang Schönhuth, Alexander Maoz (Moss), Tiffanie Yael
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SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. Conclusion SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. 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