Automated Detection of Diierentially Expressed Fragments in Mrna Diierential Display

We present a novel method for the automated detection of fragments showing dissimilar expression in mRNA di erential display. The analysis is based on aligning the numerical electrophoretic lane data in respect of a given distance function de ned on a set of fragments, or signal peaks in general. We presume that signi cant dissimilarities between peaks result in extreme score values computed for aligned peak pairs. Whereas in sequence comparison, an overall sequence similarity score is conventionally used, the current method de nes a special dissimilarity score for searching the peak pairs showing the largest relative di erences between the lanes. The output of the analysis is a highly reduced list of peak pairs, along with a set of associated features extracted from the lanes. The idea is that only the peaks of this list need to be visually con rmed instead of the vast amount of peaks in the original electropherograms. The reduced list correlates well with the results of visual evaluation of the same electropherograms. We also demonstrate the relevance of the method in real mRNA di erential display analyses when more complex expression patterns in multiple lanes are searched for. The method has potential to become a key component of a truly automated recognition tool by which one can exibly search for interesting patterns in high troughput mRNA di erential display.