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Biodoop: Bioinformatics on Hadoop

Biodoop: Bioinformatics on Hadoop,10.1109/ICPPW.2009.37,Simone Leo,Federico Santoni,Gianluigi Zanetti

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Biodoop: Bioinformatics on Hadoop   (Citations: 5)
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Bioinformatics applications currently require both processing 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 model that seems particularly well-suited to this task and for which an open source implementation (Hadoop) is available. Here we report on its application to three relevant algorithms: BLAST, GSEA and GRAMMAR. The first is characterized by relatively low-weight computation on large data sets, while the second requires heavy processing of relatively small data sets. The third one can be considered as containing a mixture of these two computational flavors. Our results are encouraging and indicate that the framework could have a wide range of bioinformatics applications while maintaining good computational efficiency, scalability and ease of maintenance.
Conference: International Conference on Parallel Processing - ICPP(Workshop) , pp. 415-422, 2009
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