Automated detection of differentially expressed fragments in mRNA differential display

We present a novel method for the automated detection of fragments showing dissimilar expression in mRNA differential display. The analysis is based o n aligning the numerical electrophoretic lane data in respect of a given di stance function defined on a set of fragments, or signal peaks in general. We presume that significant dissimilarities between peaks result in extreme score values computed for aligned peak pairs. Whereas in sequence comparis on, an overall sequence similarity score is conventionally used, the curren t method defines a special dissimilarity score for searching the peak pairs showing the largest relative differences between the lanes. The output of the analysis is a highly reduced list of peak pairs, along with a set of as sociated features extracted from the lanes. Only the peaks of this list nee d to be visually confirmed instead of the vast amount of peaks in the origi nal electrophoretic results. The results obtained by the algorithm correlat e well with results of visual evaluation of the same electropherograms. The current algorithm may be applied to the study of complex expression patter ns in multiple lanes and, in general, to automated recognition of variously defined patterns of quantitative electrophoretic data.