title: A self-tuning method for one-chip SNP identification.
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.
authors.affiliation: University of Wisconsin-Madison, USA. molla.shavlik@cs.wisc.edu
authors.forename: Michael
authors.initials: M
authors.lastname: Molla
authors.forename: Jude
authors.initials: J
authors.lastname: Shavlik
authors.forename: Todd
authors.initials: T
authors.lastname: Richmond
authors.forename: Steven
authors.initials: S
authors.lastname: Smith
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D004252:DNA Mutational Analysis / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*