General Information
| Description | Scalable analysis of large numerical datasets from biological simulations and experiments using Hadoop and Spark |
| Homepage | https://www.assembla.com/spaces/roberts-lab-public/wiki/Biospark |
Publications
Biospark: scalable analysis of large numerical datasets from biological simulations and experiments using Hadoop and Spark
Summary:
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.
Availability and Implementation:
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.edu
Supplementary information:
Supplementary data are available at Bioinformatics online.
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