Effects of error-correction of heterozygous next-generation sequencing data

Background Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumpti...
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Autor*in:	Fujimoto, M Stanley [verfasserIn] Bodily, Paul M Okuda, Nozomu Clement, Mark J Snell, Quinn

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Error Correction Genome Assembly Heterozygous Location Heterozygous Marker Error Correction Algorithm

Anmerkung:	© Fujimoto et al.; licensee BioMed Central Ltd. 2014

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 15(2014), Suppl 7 vom: 28. Mai
Übergeordnetes Werk:	volume:15 ; year:2014 ; number:Suppl 7 ; day:28 ; month:05

Links:	Volltext

DOI / URN:	10.1186/1471-2105-15-S7-S3

Katalog-ID:	SPR026895609

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520			\|a Background Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes. Results Quake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers. Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly. Conclusions These findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO.
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700	1		\|a Clement, Mark J \|4 aut
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allfields	10.1186/1471-2105-15-S7-S3 doi (DE-627)SPR026895609 (SPR)1471-2105-15-S7-S3-e DE-627 ger DE-627 rakwb eng Fujimoto, M Stanley verfasserin aut Effects of error-correction of heterozygous next-generation sequencing data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Fujimoto et al.; licensee BioMed Central Ltd. 2014 Background Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes. Results Quake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers. Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly. Conclusions These findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO. Error Correction (dpeaa)DE-He213 Genome Assembly (dpeaa)DE-He213 Heterozygous Location (dpeaa)DE-He213 Heterozygous Marker (dpeaa)DE-He213 Error Correction Algorithm (dpeaa)DE-He213 Bodily, Paul M aut Okuda, Nozomu aut Clement, Mark J aut Snell, Quinn aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 15(2014), Suppl 7 vom: 28. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:15 year:2014 number:Suppl 7 day:28 month:05 https://dx.doi.org/10.1186/1471-2105-15-S7-S3 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 Suppl 7 28 05
spelling	10.1186/1471-2105-15-S7-S3 doi (DE-627)SPR026895609 (SPR)1471-2105-15-S7-S3-e DE-627 ger DE-627 rakwb eng Fujimoto, M Stanley verfasserin aut Effects of error-correction of heterozygous next-generation sequencing data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Fujimoto et al.; licensee BioMed Central Ltd. 2014 Background Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes. Results Quake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers. Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly. Conclusions These findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO. Error Correction (dpeaa)DE-He213 Genome Assembly (dpeaa)DE-He213 Heterozygous Location (dpeaa)DE-He213 Heterozygous Marker (dpeaa)DE-He213 Error Correction Algorithm (dpeaa)DE-He213 Bodily, Paul M aut Okuda, Nozomu aut Clement, Mark J aut Snell, Quinn aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 15(2014), Suppl 7 vom: 28. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:15 year:2014 number:Suppl 7 day:28 month:05 https://dx.doi.org/10.1186/1471-2105-15-S7-S3 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 Suppl 7 28 05
allfields_unstemmed	10.1186/1471-2105-15-S7-S3 doi (DE-627)SPR026895609 (SPR)1471-2105-15-S7-S3-e DE-627 ger DE-627 rakwb eng Fujimoto, M Stanley verfasserin aut Effects of error-correction of heterozygous next-generation sequencing data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Fujimoto et al.; licensee BioMed Central Ltd. 2014 Background Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes. Results Quake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers. Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly. Conclusions These findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO. Error Correction (dpeaa)DE-He213 Genome Assembly (dpeaa)DE-He213 Heterozygous Location (dpeaa)DE-He213 Heterozygous Marker (dpeaa)DE-He213 Error Correction Algorithm (dpeaa)DE-He213 Bodily, Paul M aut Okuda, Nozomu aut Clement, Mark J aut Snell, Quinn aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 15(2014), Suppl 7 vom: 28. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:15 year:2014 number:Suppl 7 day:28 month:05 https://dx.doi.org/10.1186/1471-2105-15-S7-S3 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 Suppl 7 28 05
allfieldsGer	10.1186/1471-2105-15-S7-S3 doi (DE-627)SPR026895609 (SPR)1471-2105-15-S7-S3-e DE-627 ger DE-627 rakwb eng Fujimoto, M Stanley verfasserin aut Effects of error-correction of heterozygous next-generation sequencing data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Fujimoto et al.; licensee BioMed Central Ltd. 2014 Background Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes. Results Quake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers. Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly. Conclusions These findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO. Error Correction (dpeaa)DE-He213 Genome Assembly (dpeaa)DE-He213 Heterozygous Location (dpeaa)DE-He213 Heterozygous Marker (dpeaa)DE-He213 Error Correction Algorithm (dpeaa)DE-He213 Bodily, Paul M aut Okuda, Nozomu aut Clement, Mark J aut Snell, Quinn aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 15(2014), Suppl 7 vom: 28. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:15 year:2014 number:Suppl 7 day:28 month:05 https://dx.doi.org/10.1186/1471-2105-15-S7-S3 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 Suppl 7 28 05
allfieldsSound	10.1186/1471-2105-15-S7-S3 doi (DE-627)SPR026895609 (SPR)1471-2105-15-S7-S3-e DE-627 ger DE-627 rakwb eng Fujimoto, M Stanley verfasserin aut Effects of error-correction of heterozygous next-generation sequencing data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Fujimoto et al.; licensee BioMed Central Ltd. 2014 Background Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes. Results Quake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers. Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly. Conclusions These findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO. Error Correction (dpeaa)DE-He213 Genome Assembly (dpeaa)DE-He213 Heterozygous Location (dpeaa)DE-He213 Heterozygous Marker (dpeaa)DE-He213 Error Correction Algorithm (dpeaa)DE-He213 Bodily, Paul M aut Okuda, Nozomu aut Clement, Mark J aut Snell, Quinn aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 15(2014), Suppl 7 vom: 28. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:15 year:2014 number:Suppl 7 day:28 month:05 https://dx.doi.org/10.1186/1471-2105-15-S7-S3 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2014 Suppl 7 28 05
language	English
source	Enthalten in BMC bioinformatics 15(2014), Suppl 7 vom: 28. Mai volume:15 year:2014 number:Suppl 7 day:28 month:05
sourceStr	Enthalten in BMC bioinformatics 15(2014), Suppl 7 vom: 28. Mai volume:15 year:2014 number:Suppl 7 day:28 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Error Correction Genome Assembly Heterozygous Location Heterozygous Marker Error Correction Algorithm
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Fujimoto, M Stanley @@aut@@ Bodily, Paul M @@aut@@ Okuda, Nozomu @@aut@@ Clement, Mark J @@aut@@ Snell, Quinn @@aut@@
publishDateDaySort_date	2014-05-28T00:00:00Z
hierarchy_top_id	326644814
id	SPR026895609
language_de	englisch
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author	Fujimoto, M Stanley
spellingShingle	Fujimoto, M Stanley misc Error Correction misc Genome Assembly misc Heterozygous Location misc Heterozygous Marker misc Error Correction Algorithm Effects of error-correction of heterozygous next-generation sequencing data
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topic_title	Effects of error-correction of heterozygous next-generation sequencing data Error Correction (dpeaa)DE-He213 Genome Assembly (dpeaa)DE-He213 Heterozygous Location (dpeaa)DE-He213 Heterozygous Marker (dpeaa)DE-He213 Error Correction Algorithm (dpeaa)DE-He213
topic	misc Error Correction misc Genome Assembly misc Heterozygous Location misc Heterozygous Marker misc Error Correction Algorithm
topic_unstemmed	misc Error Correction misc Genome Assembly misc Heterozygous Location misc Heterozygous Marker misc Error Correction Algorithm
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title	Effects of error-correction of heterozygous next-generation sequencing data
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title_full	Effects of error-correction of heterozygous next-generation sequencing data
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author_browse	Fujimoto, M Stanley Bodily, Paul M Okuda, Nozomu Clement, Mark J Snell, Quinn
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title_sort	effects of error-correction of heterozygous next-generation sequencing data
title_auth	Effects of error-correction of heterozygous next-generation sequencing data
abstract	Background Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes. Results Quake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers. Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly. Conclusions These findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO. © Fujimoto et al.; licensee BioMed Central Ltd. 2014
abstractGer	Background Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes. Results Quake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers. Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly. Conclusions These findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO. © Fujimoto et al.; licensee BioMed Central Ltd. 2014
abstract_unstemmed	Background Error correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes. Results Quake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers. Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly. Conclusions These findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO. © Fujimoto et al.; licensee BioMed Central Ltd. 2014
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title_short	Effects of error-correction of heterozygous next-generation sequencing data
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Effects of error-correction of heterozygous next-generation sequencing data

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