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Johns Hopkins and Affymetrix Develop New Method to Improve Array Data-Analysis

SAN FRANCISCO, Nov. 14 – Researchers from Johns Hopkins School of Medicine and Affymetrix have developed an automated statistical method to increase the reliability of microarray hybridization data, the organizations announced Wednesday.

The method, called ABACUS, is reported in the November issue of the journal Genome Research

ABACUS, which was applied to Affymetrix’ Variation Detection Arrays, provides a quality score to individual genotypes to help investigators narrow their analysis to sites which give the most accurate information, according to the researchers.

“Until now, ways to analyze the chips were unable to distinguish highly accurate data from less reliable information,” lead author David Cutler, a research associate in the McKusick-Nathans Institute of Genetic Medicine at The Johns Hopkins University School of Medicine, said in a statement. “We took the most logical, straightforward approach we could to help us determine which of the microarray sequences to pay attention to and which ones to ignore. Our system actually evaluates and scores the reliability of each individual building block.”

The investigators tested the approach in an experiment encompassing 32 autosomal and eight X-linked genomic regions, each consisting of approximately 50 kb of unique sequence spanning a 100-kb region, in 40 humans. The results for accessing the accuracy of diploid genotypes at segregating autosomal sites showed 108 of 108 SNPS confirmed experimentally and 371 SNPs confirmed electronically, and 1515 of 1515 homozygous calls confirmed and 420 of 423 heterozygotes confirmed.

Case Western Reserve University in Cleveland, also participated in the research.

Affymetrix is headquartered in Santa Clara, Calif.

Johns Hopkins University School of Medicine is based in Baltimore, Md.   
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