Automated image analysis of noisy microarrays

Abstract

A recent extension of DNA microarray technology has been its use in DNA fingerprinting. Our research involved developing an algorithm that automatically analyzes microarray images by extracting useful information while ignoring the large amounts of noise. Our data set consisted of slides generated from DNA strands of 24 different cultures of anthrax from isolated locations (all the same strain that differ only in origin-specific neutral mutations). The data set was provided by Argonne National Laboratories in Illinois. Here we present a fully automated method that classifies these isolates at least as well as the published AMIA (Automated Microarray Image Analysis) Toolbox for MATLAB with virtually no required user interaction or external information, greatly increasing efficiency of the image analysis.