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This article is part of the supplement: Proceedings of the 11th Annual Bioinformatics Open Source Conference (BOSC) 2010

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An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics

Ronald C Taylor

Author affiliations

Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, Washington, 99352, USA

Citation and License

BMC Bioinformatics 2010, 11(Suppl 12):S1  doi:10.1186/1471-2105-11-S12-S1

Published: 21 December 2010



Bioinformatics researchers are now confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce.


An overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBase project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date.


Hadoop and the MapReduce programming paradigm already have a substantial base in the bioinformatics community, especially in the field of next-generation sequencing analysis, and such use is increasing. This is due to the cost-effectiveness of Hadoop-based analysis on commodity Linux clusters, and in the cloud via data upload to cloud vendors who have implemented Hadoop/HBase; and due to the effectiveness and ease-of-use of the MapReduce method in parallelization of many data analysis algorithms.