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Deep sequencing to reveal new variants in pooled DNA samples

Identifieur interne : 002898 ( Main/Exploration ); précédent : 002897; suivant : 002899

Deep sequencing to reveal new variants in pooled DNA samples

Auteurs : Astrid A. Out [Pays-Bas] ; Ivonne J. H. M. Van Minderhout [Pays-Bas] ; Jelle J. Goeman [Pays-Bas] ; Yavuz Ariyurek [Pays-Bas] ; Stephan Ossowski [Allemagne] ; Korbinian Schneeberger [Allemagne] ; Detlef Weigel [Allemagne] ; Michiel Van Galen [Pays-Bas] ; Peter E. M. Taschner [Pays-Bas] ; Carli M. J. Tops [Pays-Bas] ; Martijn H. Breuning [Pays-Bas] ; Gert-Jan B. Van Ommen [Pays-Bas] ; Johan T. Den Dunnen [Pays-Bas] ; Peter Devilee [Pays-Bas] ; Frederik J. Hes [Pays-Bas]

Source :

RBID : ISTEX:9D3EAD1FDEB379117A9A73AE4D995087D5B9B795

English descriptors

Abstract

We evaluated massive parallel sequencing and long‐range PCR (LRP) for rare variant detection and allele frequency estimation in pooled DNA samples. Exons 2 to 16 of the MUTYH gene were analyzed in breast cancer patients with Illumina's (Solexa) technology. From a pool of 287 genomic DNA samples we generated a single LRP product, while the same LRP was performed on 88 individual samples and the resulting products then pooled. Concentrations of constituent samples were measured with fluorimetry for genomic DNA and high‐resolution melting curve analysis (HR‐MCA) for LRP products. Illumina sequencing results were compared to Sanger sequencing data of individual samples. Correlation between allele frequencies detected by both methods was poor in the first pool, presumably because the genomic samples amplified unequally in the LRP, due to DNA quality variability. In contrast, allele frequencies correlated well in the second pool, in which all expected alleles at a frequency of 1% and higher were reliably detected, plus the majority of singletons (0.6% allele frequency). We describe custom bioinformatics and statistics to optimize detection of rare variants and to estimate required sequencing depth. Our results provide directions for designing high‐throughput analyses of candidate genes. Hum Mutat 30:1–10, 2009. © 2009 Wiley‐Liss, Inc.

Url:
DOI: 10.1002/humu.21122


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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<term>Allele frequency</term>
<term>Allele frequency estimates</term>
<term>Amplification bias</term>
<term>Attractive method</term>
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<term>Breast cancer</term>
<term>Breast cancer patients</term>
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<term>Eland</term>
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<term>Error proportion</term>
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<term>Error rate</term>
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<term>Illumina data</term>
<term>Illumina sequencing</term>
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<term>Minor allele frequency</term>
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<term>Mutyh</term>
<term>Mutyh gene</term>
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<term>Quality threshold</term>
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<term>Sanger data</term>
<term>Sanger sequencing</term>
<term>Sanger sequencing data</term>
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<term>Sequence depth</term>
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<term>Sequence variants</term>
<term>Sequencing</term>
<term>Sequencing errors</term>
<term>Shore pipeline</term>
<term>Single nucleotide polymorphisms</term>
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<term>Singleton variant</term>
<term>Singleton variants</term>
<term>Statistical power</term>
<term>Supp</term>
<term>True positives</term>
<term>Unique variants</term>
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<div type="abstract" xml:lang="en">We evaluated massive parallel sequencing and long‐range PCR (LRP) for rare variant detection and allele frequency estimation in pooled DNA samples. Exons 2 to 16 of the MUTYH gene were analyzed in breast cancer patients with Illumina's (Solexa) technology. From a pool of 287 genomic DNA samples we generated a single LRP product, while the same LRP was performed on 88 individual samples and the resulting products then pooled. Concentrations of constituent samples were measured with fluorimetry for genomic DNA and high‐resolution melting curve analysis (HR‐MCA) for LRP products. Illumina sequencing results were compared to Sanger sequencing data of individual samples. Correlation between allele frequencies detected by both methods was poor in the first pool, presumably because the genomic samples amplified unequally in the LRP, due to DNA quality variability. In contrast, allele frequencies correlated well in the second pool, in which all expected alleles at a frequency of 1% and higher were reliably detected, plus the majority of singletons (0.6% allele frequency). We describe custom bioinformatics and statistics to optimize detection of rare variants and to estimate required sequencing depth. Our results provide directions for designing high‐throughput analyses of candidate genes. Hum Mutat 30:1–10, 2009. © 2009 Wiley‐Liss, Inc.</div>
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<name sortKey="Den Dunnen, Johan T" sort="Den Dunnen, Johan T" uniqKey="Den Dunnen J" first="Johan T." last="Den Dunnen">Johan T. Den Dunnen</name>
<name sortKey="Den Dunnen, Johan T" sort="Den Dunnen, Johan T" uniqKey="Den Dunnen J" first="Johan T." last="Den Dunnen">Johan T. Den Dunnen</name>
<name sortKey="Den Dunnen, Johan T" sort="Den Dunnen, Johan T" uniqKey="Den Dunnen J" first="Johan T." last="Den Dunnen">Johan T. Den Dunnen</name>
<name sortKey="Devilee, Peter" sort="Devilee, Peter" uniqKey="Devilee P" first="Peter" last="Devilee">Peter Devilee</name>
<name sortKey="Devilee, Peter" sort="Devilee, Peter" uniqKey="Devilee P" first="Peter" last="Devilee">Peter Devilee</name>
<name sortKey="Goeman, Jelle J" sort="Goeman, Jelle J" uniqKey="Goeman J" first="Jelle J." last="Goeman">Jelle J. Goeman</name>
<name sortKey="Hes, Frederik J" sort="Hes, Frederik J" uniqKey="Hes F" first="Frederik J." last="Hes">Frederik J. Hes</name>
<name sortKey="Out, Astrid A" sort="Out, Astrid A" uniqKey="Out A" first="Astrid A." last="Out">Astrid A. Out</name>
<name sortKey="Out, Astrid A" sort="Out, Astrid A" uniqKey="Out A" first="Astrid A." last="Out">Astrid A. Out</name>
<name sortKey="Taschner, Peter E M" sort="Taschner, Peter E M" uniqKey="Taschner P" first="Peter E. M." last="Taschner">Peter E. M. Taschner</name>
<name sortKey="Taschner, Peter E M" sort="Taschner, Peter E M" uniqKey="Taschner P" first="Peter E. M." last="Taschner">Peter E. M. Taschner</name>
<name sortKey="Tops, Carli M J" sort="Tops, Carli M J" uniqKey="Tops C" first="Carli M. J." last="Tops">Carli M. J. Tops</name>
<name sortKey="Van Galen, Michiel" sort="Van Galen, Michiel" uniqKey="Van Galen M" first="Michiel" last="Van Galen">Michiel Van Galen</name>
<name sortKey="Van Galen, Michiel" sort="Van Galen, Michiel" uniqKey="Van Galen M" first="Michiel" last="Van Galen">Michiel Van Galen</name>
<name sortKey="Van Minderhout, Ivonne J H M" sort="Van Minderhout, Ivonne J H M" uniqKey="Van Minderhout I" first="Ivonne J. H. M." last="Van Minderhout">Ivonne J. H. M. Van Minderhout</name>
<name sortKey="Van Ommen, Gert An B" sort="Van Ommen, Gert An B" uniqKey="Van Ommen G" first="Gert-Jan B." last="Van Ommen">Gert-Jan B. Van Ommen</name>
<name sortKey="Van Ommen, Gert An B" sort="Van Ommen, Gert An B" uniqKey="Van Ommen G" first="Gert-Jan B." last="Van Ommen">Gert-Jan B. Van Ommen</name>
</country>
<country name="Allemagne">
<region name="Bade-Wurtemberg">
<name sortKey="Ossowski, Stephan" sort="Ossowski, Stephan" uniqKey="Ossowski S" first="Stephan" last="Ossowski">Stephan Ossowski</name>
</region>
<name sortKey="Schneeberger, Korbinian" sort="Schneeberger, Korbinian" uniqKey="Schneeberger K" first="Korbinian" last="Schneeberger">Korbinian Schneeberger</name>
<name sortKey="Weigel, Detlef" sort="Weigel, Detlef" uniqKey="Weigel D" first="Detlef" last="Weigel">Detlef Weigel</name>
</country>
</tree>
</affiliations>
</record>

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