Current bioinformatics applications require both management of huge amounts of data and heavy computation: fulfilling these requirements calls for simple ways to implement parallel computing. MapReduce is a general-purpose parallelization technology that appears to be particularly well adapted to this task. Here we report on its application, using its open source implementation Hadoop, to two relevant algorithms: BLAST and GSEA. The first is characterized by streaming computation on large data sets, while the second requires a multi-pass computational strategy on relatively small data sets. The analysis of these algorithms is relevant to a wide class of complex applications, e.g., structural genomics and genome-wide association studies, since they typically contain a mixture of these two computational flavors. Our results are very promising and indicate that the framework could have a wide range of bioinformatics applications while maintaining good computational efficiency, scalability and ease of maintenance.
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