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The amount of genomic data available for study is increasing at a rate similar to that of Moore's law. This deluge of data is challenging bioinformaticians to develop newer, faster and better algorithms for analysis and examination of this data. The growing availability of large scale computing grids coupled with high-performance networking is challenging computer scientists to develop better, faster methods of exploiting parallelism in these biological computations and deploying them across computing grids. In this paper, we describe two computations that are required to be run frequently and which require large amounts of computing resource to complete in a reasonable time. The data for these computations are very large and the sequential computational time can exceed thousands of hours. We show the importance and relevance of these computations, the nature of the data and parallelism and we show how we are meeting the challenge of efficiently distributing and managing these computations in the SEED project.