Biospark: scalable analysis of large numerical datasets from biological simulations and experiments using Hadoop and Spark.
Data-parallel programming techniques can dramatically decrease the time needed to analyze large datasets. While these methods have provided significant improvements for sequencing-based analyses, other areas of biological informatics have not yet adopted them. Here, we introduce Biospark, a new framework for performing data-parallel analysis on large numerical datasets. Biospark builds upon the open source Hadoop and Spark projects, bringing domain-specific features for biology.



Source code is licensed under the Apache 2.0 open source license and is available at the project website: https://www.assembla.com/spaces/roberts-lab-public/wiki/Biospark CONTACT: eroberts@jhu.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
Max
M
Klein
Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
Rati
R
Sharma
Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
Chris H
CH
Bohrer
Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
Cameron M
CM
Avelis
Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
Elijah
E
Roberts
D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D008853:Microscopy; D012984:Software
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