Diagnostic interpretation of array data using public databases and internet sources
Identifieur interne : 001643 ( Istex/Corpus ); précédent : 001642; suivant : 001644Diagnostic interpretation of array data using public databases and internet sources
Auteurs : Nicole De Leeuw ; Trijnie Dijkhuizen ; Jayne Y. Hehir-Kwa ; Nigel P. Carter ; Lars Feuk ; Helen V. Firth ; Robert M. Kuhn ; David H. Ledbetter ; Christa Lese Martin ; Conny M. A. Van Ravenswaaij-Arts ; Steven W. Scherer ; Soheil Shams ; Steven Van Vooren ; Rolf Sijmons ; Morris Swertz ; Ros HastingsSource :
- Human Mutation [ 1059-7794 ] ; 2012-06.
English descriptors
- KwdEn :
Abstract
The range of commercially available array platforms and analysis software packages is expanding and their utility is improving, making reliable detection of copy‐number variants (CNVs) relatively straightforward. Reliable interpretation of CNV data, however, is often difficult and requires expertise. With our knowledge of the human genome growing rapidly, applications for array testing continuously broadening, and the resolution of CNV detection increasing, this leads to great complexity in interpreting what can be daunting data. Correct CNV interpretation and optimal use of the genotype information provided by single‐nucleotide polymorphism probes on an array depends largely on knowledge present in various resources. In addition to the availability of host laboratories' own datasets and national registries, there are several public databases and Internet resources with genotype and phenotype information that can be used for array data interpretation. With so many resources now available, it is important to know which are fit‐for‐purpose in a diagnostic setting. We summarize the characteristics of the most commonly used Internet databases and resources, and propose a general data interpretation strategy that can be used for comparative hybridization, comparative intensity, and genotype‐based array data. Hum Mutat 33:930–940, 2012. © 2012 Wiley Periodicals, Inc.
Url:
DOI: 10.1002/humu.22049
Links to Exploration step
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<front><div type="abstract" xml:lang="en">The range of commercially available array platforms and analysis software packages is expanding and their utility is improving, making reliable detection of copy‐number variants (CNVs) relatively straightforward. Reliable interpretation of CNV data, however, is often difficult and requires expertise. With our knowledge of the human genome growing rapidly, applications for array testing continuously broadening, and the resolution of CNV detection increasing, this leads to great complexity in interpreting what can be daunting data. Correct CNV interpretation and optimal use of the genotype information provided by single‐nucleotide polymorphism probes on an array depends largely on knowledge present in various resources. In addition to the availability of host laboratories' own datasets and national registries, there are several public databases and Internet resources with genotype and phenotype information that can be used for array data interpretation. With so many resources now available, it is important to know which are fit‐for‐purpose in a diagnostic setting. We summarize the characteristics of the most commonly used Internet databases and resources, and propose a general data interpretation strategy that can be used for comparative hybridization, comparative intensity, and genotype‐based array data. Hum Mutat 33:930–940, 2012. © 2012 Wiley Periodicals, Inc.</div>
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<notesStmt><note type="content">*For the Focus on CNV Detection with Diagnostic Arrays</note>
<note>DECIPHER is supported by the Wellcome Trust (grant number WT077008)</note>
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<p>The range of commercially available array platforms and analysis software packages is expanding and their utility is improving, making reliable detection of copy‐number variants (CNVs) relatively straightforward. Reliable interpretation of CNV data, however, is often difficult and requires expertise. With our knowledge of the human genome growing rapidly, applications for array testing continuously broadening, and the resolution of CNV detection increasing, this leads to great complexity in interpreting what can be daunting data. Correct CNV interpretation and optimal use of the genotype information provided by single‐nucleotide polymorphism probes on an array depends largely on knowledge present in various resources. In addition to the availability of host laboratories' own datasets and national registries, there are several public databases and Internet resources with genotype and phenotype information that can be used for array data interpretation. With so many resources now available, it is important to know which are fit‐for‐purpose in a diagnostic setting. We summarize the characteristics of the most commonly used Internet databases and resources, and propose a general data interpretation strategy that can be used for comparative hybridization, comparative intensity, and genotype‐based array data. Hum Mutat 33:930–940, 2012. © 2012 Wiley Periodicals, Inc.</p>
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<abstract lang="en">The range of commercially available array platforms and analysis software packages is expanding and their utility is improving, making reliable detection of copy‐number variants (CNVs) relatively straightforward. Reliable interpretation of CNV data, however, is often difficult and requires expertise. With our knowledge of the human genome growing rapidly, applications for array testing continuously broadening, and the resolution of CNV detection increasing, this leads to great complexity in interpreting what can be daunting data. Correct CNV interpretation and optimal use of the genotype information provided by single‐nucleotide polymorphism probes on an array depends largely on knowledge present in various resources. In addition to the availability of host laboratories' own datasets and national registries, there are several public databases and Internet resources with genotype and phenotype information that can be used for array data interpretation. With so many resources now available, it is important to know which are fit‐for‐purpose in a diagnostic setting. We summarize the characteristics of the most commonly used Internet databases and resources, and propose a general data interpretation strategy that can be used for comparative hybridization, comparative intensity, and genotype‐based array data. Hum Mutat 33:930–940, 2012. © 2012 Wiley Periodicals, Inc.</abstract>
<note type="content">*For the Focus on CNV Detection with Diagnostic Arrays</note>
<note type="funding">DECIPHER is supported by the Wellcome Trust (grant number WT077008)</note>
<subject lang="en"><genre>keywords</genre>
<topic>array</topic>
<topic>classification</topic>
<topic>CNV</topic>
<topic>database</topic>
<topic>data interpretation</topic>
<topic>diagnostic</topic>
<topic>genome wide</topic>
</subject>
<relatedItem type="host"><titleInfo><title>Human Mutation</title>
</titleInfo>
<titleInfo type="abbreviated"><title>Hum. Mutat.</title>
</titleInfo>
<name type="personal"><namePart type="given">Birgit</namePart>
<namePart type="family">Sikkema‐Raddatz</namePart>
</name>
<name type="personal"><namePart type="given">Rolf H.</namePart>
<namePart type="family">Sijmons</namePart>
</name>
<genre type="journal">journal</genre>
<note type="content"> Additional Supporting information may be found in the online version of this articleSupporting Info Item: Supporting Information - </note>
<subject><genre>article-category</genre>
<topic>Special Article</topic>
</subject>
<identifier type="ISSN">1059-7794</identifier>
<identifier type="eISSN">1098-1004</identifier>
<identifier type="DOI">10.1002/(ISSN)1098-1004</identifier>
<identifier type="PublisherID">HUMU</identifier>
<part><date>2012</date>
<detail type="title"><title>Focus on CNV Detection with Diagnostic Arrays</title>
</detail>
<detail type="volume"><caption>vol.</caption>
<number>33</number>
</detail>
<detail type="issue"><caption>no.</caption>
<number>6</number>
</detail>
<extent unit="pages"><start>930</start>
<end>940</end>
<total>11</total>
</extent>
</part>
</relatedItem>
<identifier type="istex">48ED99E15E5C0D637F4412A409E02C16FF88D9C5</identifier>
<identifier type="DOI">10.1002/humu.22049</identifier>
<identifier type="ArticleID">HUMU22049</identifier>
<accessCondition type="use and reproduction" contentType="copyright">© 2012 Wiley Periodicals, Inc.</accessCondition>
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