We present a method for evaluating the suitability of different string dissimilarity measures and clustering algorithms for EST clustering, one of the main techniques used in transcriptome projects. Our method consists of first generating simulated ESTs according to user-specified parameters, and then evaluating the quality of clusterings produced when different dissimilarity measures and different clustering algorithms are used. We have implemented two tools for this purpose: (i) ESTSim (EST Simulator), which generates simulated EST sequences from mRNAs/cDNAs according to userspecified parameters, and (ii) ECLEST (Evaluator for CLusterings of ESTs), which computes and evaluates a clustering of a set of input ESTs, where the dissimilarity measure, the clustering algorithm, and the clustering validity index can be specified independently. We demonstrate the method on a sample set of 699 cDNAs taken from a public mammalian gene collection: We generated approximately 16,000 simulated ESTs from this set according to parameters that follow the guidelines laid down by NCBI, and compared the clusterings produced using five different dissimilarity measures, while fixing the clustering algorithm and clustering validity index. We then repeated the experiment with higher error parameters. We have been able to derive
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