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Expressed sequence tags, ESTs, are DNA molecules experimentally derived from expressed portions of genes. Clustering of ESTs is essential for gene recognition and understanding important genetic variations such as those resulting in diseases. In this paper, we present the design and development of a parallel software system for EST clustering. To our knowledge, this is the first such effort to address the problem of EST clustering in parallel. The novel features of our approach include 1) design of space efficient algorithms to keep the space requirement linear in the size of the input data set, 2) a combination of algorithmic techniques to reduce the total work without sacrificing the quality of EST clustering, and 3) use of parallel processing to reduce the run-time and facilitate the clustering of large datasets. Using a combination of these techniques, we report the clustering of 81,414 Arabidopsis ESTs in under 2.5 minutes on a 64-processor IBM SP, a problem that is estimated to take 9 hours of run-time with a state-of-the-art software, provided the memory required to run the software can be made available.