A self-tuning method for one-chip SNP identification.
Identifieur interne : 005487 ( Main/Curation ); précédent : 005486; suivant : 005488A self-tuning method for one-chip SNP identification.
Auteurs : Michael Molla [États-Unis] ; Jude Shavlik ; Todd Richmond ; Steven SmithSource :
- Proceedings. IEEE Computational Systems Bioinformatics Conference [ 1551-7497 ] ; 2004.
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Abstract
Current methods for interpreting oligonucleotide-based SNP-detection microarrays, SNP chips, are based on statistics and require extensive parameter tuning as well as extremely high-resolution images of the chip being processed. We present a method, based on a simple data-classification technique called nearest-neighbors that, on haploid organisms, produces results comparable to the published results of the leading statistical methods and requires very little in the way of parameter tuning. Furthermore, it can interpret SNP chips using lower-resolution scanners of the type more typically used in current microarray experiments. Along with our algorithm, we present the results of a SNP-detection experiment where, when independently applying this algorithm to six identical SARS SNP chips, we correctly identify all 24 SNPs in a particular strain of the SARS virus, with between 6 and 13 false positives across the six experiments.
DOI: 10.1109/csb.2004.1332419
PubMed: 16448001
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pubmed:16448001Le document en format XML
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<affiliation wicri:level="1"><nlm:affiliation>University of Wisconsin-Madison, USA. molla.shavlik@cs.wisc.edu</nlm:affiliation>
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<affiliation wicri:level="1"><nlm:affiliation>University of Wisconsin-Madison, USA. molla.shavlik@cs.wisc.edu</nlm:affiliation>
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<author><name sortKey="Smith, Steven" sort="Smith, Steven" uniqKey="Smith S" first="Steven" last="Smith">Steven Smith</name>
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<series><title level="j">Proceedings. IEEE Computational Systems Bioinformatics Conference</title>
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<term>Chromosome Mapping (methods)</term>
<term>DNA Mutational Analysis (methods)</term>
<term>Oligonucleotide Array Sequence Analysis (methods)</term>
<term>Pattern Recognition, Automated (methods)</term>
<term>Polymorphism, Single Nucleotide (genetics)</term>
<term>Sequence Alignment (methods)</term>
<term>Sequence Analysis, DNA (methods)</term>
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<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term>
<term>Alignement de séquences ()</term>
<term>Analyse de mutations d'ADN ()</term>
<term>Analyse de séquence d'ADN ()</term>
<term>Cartographie chromosomique ()</term>
<term>Polymorphisme de nucléotide simple (génétique)</term>
<term>Reconnaissance automatique des formes ()</term>
<term>Séquençage par oligonucléotides en batterie ()</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Polymorphism, Single Nucleotide</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Polymorphisme de nucléotide simple</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Chromosome Mapping</term>
<term>DNA Mutational Analysis</term>
<term>Oligonucleotide Array Sequence Analysis</term>
<term>Pattern Recognition, Automated</term>
<term>Sequence Alignment</term>
<term>Sequence Analysis, DNA</term>
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<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
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<term>Analyse de mutations d'ADN</term>
<term>Analyse de séquence d'ADN</term>
<term>Cartographie chromosomique</term>
<term>Reconnaissance automatique des formes</term>
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<front><div type="abstract" xml:lang="en">Current methods for interpreting oligonucleotide-based SNP-detection microarrays, SNP chips, are based on statistics and require extensive parameter tuning as well as extremely high-resolution images of the chip being processed. We present a method, based on a simple data-classification technique called nearest-neighbors that, on haploid organisms, produces results comparable to the published results of the leading statistical methods and requires very little in the way of parameter tuning. Furthermore, it can interpret SNP chips using lower-resolution scanners of the type more typically used in current microarray experiments. Along with our algorithm, we present the results of a SNP-detection experiment where, when independently applying this algorithm to six identical SARS SNP chips, we correctly identify all 24 SNPs in a particular strain of the SARS virus, with between 6 and 13 false positives across the six experiments.</div>
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