Automating sequence-based detection and genotyping of SNPs from diploid samples
The detection of sequence variation, for which DNA sequencing has emerged as the most sensitive and automated approach, forms the basis of all genetic analysis. Here we describe and illustrate an algorithm that accurately detects and genotypes SNPs from fluorescence-based sequence data. Because the...
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| Veröffentlicht in: | Nature genetics 2006-03, Vol.38 (3), p.375-381 |
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| creator | Stephens, Matthew Sloan, James S Robertson, P D Scheet, Paul Nickerson, Deborah A |
| description | The detection of sequence variation, for which DNA sequencing has emerged as the most sensitive and automated approach, forms the basis of all genetic analysis. Here we describe and illustrate an algorithm that accurately detects and genotypes SNPs from fluorescence-based sequence data. Because the algorithm focuses particularly on detecting SNPs through the identification of heterozygous individuals, it is especially well suited to the detection of SNPs in diploid samples obtained after DNA amplification. It is substantially more accurate than existing approaches and, notably, provides a useful quantitative measure of its confidence in each potential SNP detected and in each genotype called. Calls assigned the highest confidence are sufficiently reliable to remove the need for manual review in several contexts. For example, for sequence data from 47–90 individuals sequenced on both the forward and reverse strands, the highest-confidence calls from our algorithm detected 93% of all SNPs and 100% of high-frequency SNPs, with no false positive SNPs identified and 99.9% genotyping accuracy. This algorithm is implemented in a software package, PolyPhred version 5.0, which is freely available for academic use. |
| doi_str_mv | 10.1038/ng1746 |
| format | Article |
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Here we describe and illustrate an algorithm that accurately detects and genotypes SNPs from fluorescence-based sequence data. Because the algorithm focuses particularly on detecting SNPs through the identification of heterozygous individuals, it is especially well suited to the detection of SNPs in diploid samples obtained after DNA amplification. It is substantially more accurate than existing approaches and, notably, provides a useful quantitative measure of its confidence in each potential SNP detected and in each genotype called. Calls assigned the highest confidence are sufficiently reliable to remove the need for manual review in several contexts. For example, for sequence data from 47–90 individuals sequenced on both the forward and reverse strands, the highest-confidence calls from our algorithm detected 93% of all SNPs and 100% of high-frequency SNPs, with no false positive SNPs identified and 99.9% genotyping accuracy. This algorithm is implemented in a software package, PolyPhred version 5.0, which is freely available for academic use.</description><identifier>ISSN: 1061-4036</identifier><identifier>EISSN: 1546-1718</identifier><identifier>DOI: 10.1038/ng1746</identifier><identifier>PMID: 16493422</identifier><identifier>CODEN: NGENEC</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>Agriculture ; Algorithms ; Animal Genetics and Genomics ; Automation - methods ; Biological and medical sciences ; Biomedical and Life Sciences ; Biomedicine ; Cancer Research ; Chromosomes ; Deoxyribonucleic acid ; Diploidy ; DNA ; DNA - genetics ; Fluorescence ; Fundamental and applied biological sciences. Psychology ; Gene amplification ; Gene Function ; Genetic Variation ; Genetics of eukaryotes. Biological and molecular evolution ; Genotype ; Genotype & phenotype ; Genotypes ; Human Genetics ; Physiological aspects ; Polymorphism, Single Nucleotide ; Reproducibility of Results ; Sensitivity and Specificity ; Single nucleotide polymorphisms ; technical-report</subject><ispartof>Nature genetics, 2006-03, Vol.38 (3), p.375-381</ispartof><rights>Springer Nature America, Inc. 2006</rights><rights>2006 INIST-CNRS</rights><rights>COPYRIGHT 2006 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Mar 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c651t-2ca87a4def392f1692400e15395187dc42a8117777a7e4a4eeb76e7b04f745ac3</citedby><cites>FETCH-LOGICAL-c651t-2ca87a4def392f1692400e15395187dc42a8117777a7e4a4eeb76e7b04f745ac3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/ng1746$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/ng1746$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,2727,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17712183$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16493422$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Stephens, Matthew</creatorcontrib><creatorcontrib>Sloan, James S</creatorcontrib><creatorcontrib>Robertson, P D</creatorcontrib><creatorcontrib>Scheet, Paul</creatorcontrib><creatorcontrib>Nickerson, Deborah A</creatorcontrib><title>Automating sequence-based detection and genotyping of SNPs from diploid samples</title><title>Nature genetics</title><addtitle>Nat Genet</addtitle><addtitle>Nat Genet</addtitle><description>The detection of sequence variation, for which DNA sequencing has emerged as the most sensitive and automated approach, forms the basis of all genetic analysis. Here we describe and illustrate an algorithm that accurately detects and genotypes SNPs from fluorescence-based sequence data. Because the algorithm focuses particularly on detecting SNPs through the identification of heterozygous individuals, it is especially well suited to the detection of SNPs in diploid samples obtained after DNA amplification. It is substantially more accurate than existing approaches and, notably, provides a useful quantitative measure of its confidence in each potential SNP detected and in each genotype called. Calls assigned the highest confidence are sufficiently reliable to remove the need for manual review in several contexts. For example, for sequence data from 47–90 individuals sequenced on both the forward and reverse strands, the highest-confidence calls from our algorithm detected 93% of all SNPs and 100% of high-frequency SNPs, with no false positive SNPs identified and 99.9% genotyping accuracy. 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Here we describe and illustrate an algorithm that accurately detects and genotypes SNPs from fluorescence-based sequence data. Because the algorithm focuses particularly on detecting SNPs through the identification of heterozygous individuals, it is especially well suited to the detection of SNPs in diploid samples obtained after DNA amplification. It is substantially more accurate than existing approaches and, notably, provides a useful quantitative measure of its confidence in each potential SNP detected and in each genotype called. Calls assigned the highest confidence are sufficiently reliable to remove the need for manual review in several contexts. For example, for sequence data from 47–90 individuals sequenced on both the forward and reverse strands, the highest-confidence calls from our algorithm detected 93% of all SNPs and 100% of high-frequency SNPs, with no false positive SNPs identified and 99.9% genotyping accuracy. This algorithm is implemented in a software package, PolyPhred version 5.0, which is freely available for academic use.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>16493422</pmid><doi>10.1038/ng1746</doi><tpages>7</tpages></addata></record> |
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| subjects | Agriculture Algorithms Animal Genetics and Genomics Automation - methods Biological and medical sciences Biomedical and Life Sciences Biomedicine Cancer Research Chromosomes Deoxyribonucleic acid Diploidy DNA DNA - genetics Fluorescence Fundamental and applied biological sciences. Psychology Gene amplification Gene Function Genetic Variation Genetics of eukaryotes. Biological and molecular evolution Genotype Genotype & phenotype Genotypes Human Genetics Physiological aspects Polymorphism, Single Nucleotide Reproducibility of Results Sensitivity and Specificity Single nucleotide polymorphisms technical-report |
| title | Automating sequence-based detection and genotyping of SNPs from diploid samples |
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