SNP detection for massively parallel whole-genome resequencing

Next-generation massively parallel sequencing technologies provide ultrahigh throughput at two orders of magnitude lower unit cost than capillary Sanger sequencing technology. One of the key applications of next-generation sequencing is studying genetic variation between individuals using whole-geno...

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Published in	Genome Research Vol. 19; no. 6; pp. 1124 - 1132
Main Authors	Li, Ruiqiang, Li, Yingrui, Fang, Xiaodong, Yang, Huanming, Wang, Jian, Kristiansen, Karsten, Wang, Jun
Format	Journal Article
Language	English
Published	United States Cold Spring Harbor Laboratory Press 01.06.2009
Subjects	Algorithms Asian Continental Ancestry Group - genetics Chromosomes, Human, X - genetics Computational Biology - methods Genetics, Population - methods Genome, Human - genetics Genotype Humans Likelihood Functions Models, Genetic Models, Statistical Polymorphism, Single Nucleotide Probability Reproducibility of Results Resource Sequence Analysis, DNA - instrumentation Sequence Analysis, DNA - methods Software
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Abstract	Next-generation massively parallel sequencing technologies provide ultrahigh throughput at two orders of magnitude lower unit cost than capillary Sanger sequencing technology. One of the key applications of next-generation sequencing is studying genetic variation between individuals using whole-genome or target region resequencing. Here, we have developed a consensus-calling and SNP-detection method for sequencing-by-synthesis Illumina Genome Analyzer technology. We designed this method by carefully considering the data quality, alignment, and experimental errors common to this technology. All of this information was integrated into a single quality score for each base under Bayesian theory to measure the accuracy of consensus calling. We tested this methodology using a large-scale human resequencing data set of 36× coverage and assembled a high-quality nonrepetitive consensus sequence for 92.25% of the diploid autosomes and 88.07% of the haploid X chromosome. Comparison of the consensus sequence with Illumina human 1M BeadChip genotyped alleles from the same DNA sample showed that 98.6% of the 37,933 genotyped alleles on the X chromosome and 98% of 999,981 genotyped alleles on autosomes were covered at 99.97% and 99.84% consistency, respectively. At a low sequencing depth, we used prior probability of dbSNP alleles and were able to improve coverage of the dbSNP sites significantly as compared to that obtained using a nonimputation model. Our analyses demonstrate that our method has a very low false call rate at any sequencing depth and excellent genome coverage at a high sequencing depth.
AbstractList	Next-generation massively parallel sequencing technologies provide ultrahigh throughput at two orders of magnitude lower unit cost than capillary Sanger sequencing technology. One of the key applications of next-generation sequencing is studying genetic variation between individuals using whole-genome or target region resequencing. Here, we have developed a consensus-calling and SNP-detection method for sequencing-by-synthesis Illumina Genome Analyzer technology. We designed this method by carefully considering the data quality, alignment, and experimental errors common to this technology. All of this information was integrated into a single quality score for each base under Bayesian theory to measure the accuracy of consensus calling. We tested this methodology using a large-scale human resequencing data set of 36x coverage and assembled a high-quality nonrepetitive consensus sequence for 92.25% of the diploid autosomes and 88.07% of the haploid X chromosome. Comparison of the consensus sequence with Illumina human 1M BeadChip genotyped alleles from the same DNA sample showed that 98.6% of the 37,933 genotyped alleles on the X chromosome and 98% of 999,981 genotyped alleles on autosomes were covered at 99.97% and 99.84% consistency, respectively. At a low sequencing depth, we used prior probability of dbSNP alleles and were able to improve coverage of the dbSNP sites significantly as compared to that obtained using a nonimputation model. Our analyses demonstrate that our method has a very low false call rate at any sequencing depth and excellent genome coverage at a high sequencing depth.Next-generation massively parallel sequencing technologies provide ultrahigh throughput at two orders of magnitude lower unit cost than capillary Sanger sequencing technology. One of the key applications of next-generation sequencing is studying genetic variation between individuals using whole-genome or target region resequencing. Here, we have developed a consensus-calling and SNP-detection method for sequencing-by-synthesis Illumina Genome Analyzer technology. We designed this method by carefully considering the data quality, alignment, and experimental errors common to this technology. All of this information was integrated into a single quality score for each base under Bayesian theory to measure the accuracy of consensus calling. We tested this methodology using a large-scale human resequencing data set of 36x coverage and assembled a high-quality nonrepetitive consensus sequence for 92.25% of the diploid autosomes and 88.07% of the haploid X chromosome. Comparison of the consensus sequence with Illumina human 1M BeadChip genotyped alleles from the same DNA sample showed that 98.6% of the 37,933 genotyped alleles on the X chromosome and 98% of 999,981 genotyped alleles on autosomes were covered at 99.97% and 99.84% consistency, respectively. At a low sequencing depth, we used prior probability of dbSNP alleles and were able to improve coverage of the dbSNP sites significantly as compared to that obtained using a nonimputation model. Our analyses demonstrate that our method has a very low false call rate at any sequencing depth and excellent genome coverage at a high sequencing depth. Next-generation massively parallel sequencing technologies provide ultrahigh throughput at two orders of magnitude lower unit cost than capillary Sanger sequencing technology. One of the key applications of next-generation sequencing is studying genetic variation between individuals using whole-genome or target region resequencing. Here, we have developed a consensus-calling and SNP-detection method for sequencing-by-synthesis Illumina Genome Analyzer technology. We designed this method by carefully considering the data quality, alignment, and experimental errors common to this technology. All of this information was integrated into a single quality score for each base under Bayesian theory to measure the accuracy of consensus calling. We tested this methodology using a large-scale human resequencing data set of 36× coverage and assembled a high-quality nonrepetitive consensus sequence for 92.25% of the diploid autosomes and 88.07% of the haploid X chromosome. Comparison of the consensus sequence with Illumina human 1M BeadChip genotyped alleles from the same DNA sample showed that 98.6% of the 37,933 genotyped alleles on the X chromosome and 98% of 999,981 genotyped alleles on autosomes were covered at 99.97% and 99.84% consistency, respectively. At a low sequencing depth, we used prior probability of dbSNP alleles and were able to improve coverage of the dbSNP sites significantly as compared to that obtained using a nonimputation model. Our analyses demonstrate that our method has a very low false call rate at any sequencing depth and excellent genome coverage at a high sequencing depth. Next-generation massively parallel sequencing technologies provide ultrahigh throughput at two orders of magnitude lower unit cost than capillary Sanger sequencing technology. One of the key applications of next-generation sequencing is studying genetic variation between individuals using whole-genome or target region resequencing. Here, we have developed a consensus-calling and SNP-detection method for sequencing-by-synthesis Illumina Genome Analyzer technology. We designed this method by carefully considering the data quality, alignment, and experimental errors common to this technology. All of this information was integrated into a single quality score for each base under Bayesian theory to measure the accuracy of consensus calling. We tested this methodology using a large-scale human resequencing data set of 36x coverage and assembled a high-quality nonrepetitive consensus sequence for 92.25% of the diploid autosomes and 88.07% of the haploid X chromosome. Comparison of the consensus sequence with Illumina human 1M BeadChip genotyped alleles from the same DNA sample showed that 98.6% of the 37,933 genotyped alleles on the X chromosome and 98% of 999,981 genotyped alleles on autosomes were covered at 99.97% and 99.84% consistency, respectively. At a low sequencing depth, we used prior probability of dbSNP alleles and were able to improve coverage of the dbSNP sites significantly as compared to that obtained using a nonimputation model. Our analyses demonstrate that our method has a very low false call rate at any sequencing depth and excellent genome coverage at a high sequencing depth.
Author	Wang, Jian Fang, Xiaodong Kristiansen, Karsten Li, Ruiqiang Wang, Jun Li, Yingrui Yang, Huanming
AuthorAffiliation	1 Beijing Genomics Institute at Shenzhen, Shenzhen 518000, China 2 Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M DK-5230, Denmark
AuthorAffiliation_xml	– name: 2 Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M DK-5230, Denmark – name: 1 Beijing Genomics Institute at Shenzhen, Shenzhen 518000, China
Author_xml	– sequence: 1 givenname: Ruiqiang surname: Li fullname: Li, Ruiqiang – sequence: 2 givenname: Yingrui surname: Li fullname: Li, Yingrui – sequence: 3 givenname: Xiaodong surname: Fang fullname: Fang, Xiaodong – sequence: 4 givenname: Huanming surname: Yang fullname: Yang, Huanming – sequence: 5 givenname: Jian surname: Wang fullname: Wang, Jian – sequence: 6 givenname: Karsten surname: Kristiansen fullname: Kristiansen, Karsten – sequence: 7 givenname: Jun surname: Wang fullname: Wang, Jun
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/19420381$$D View this record in MEDLINE/PubMed
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PublicationDateYYYYMMDD	2009-06-01
PublicationDate_xml	– month: 06 year: 2009 text: 2009-06-01 day: 01
PublicationDecade	2000
PublicationPlace	United States
PublicationPlace_xml	– name: United States
PublicationTitle	Genome Research
PublicationTitleAlternate	Genome Res
PublicationYear	2009
Publisher	Cold Spring Harbor Laboratory Press
Publisher_xml	– name: Cold Spring Harbor Laboratory Press
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Snippet	Next-generation massively parallel sequencing technologies provide ultrahigh throughput at two orders of magnitude lower unit cost than capillary Sanger...
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SubjectTerms	Algorithms Asian Continental Ancestry Group - genetics Chromosomes, Human, X - genetics Computational Biology - methods Genetics, Population - methods Genome, Human - genetics Genotype Humans Likelihood Functions Models, Genetic Models, Statistical Polymorphism, Single Nucleotide Probability Reproducibility of Results Resource Sequence Analysis, DNA - instrumentation Sequence Analysis, DNA - methods Software
Title	SNP detection for massively parallel whole-genome resequencing
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