Abstract
Biological data analysis is typically implemented using a pipeline that combines many data analysis tools and meta-databases. These pipelines must scale to very large datasets, and therefore often require parallel and distributed computing. There are many infrastructure systems for data-intensive computing. However, most biological data analysis pipelines do not leverage these systems. An important challenge is therefore to integrate biological data analysis frameworks with data-intensive computing infrastructure systems. In this paper, we describe how we have extended data-intensive computing systems to support unmodified biological data analysis tools. We also describe four approaches for integrating the extended systems with biological data analysis frameworks, and discuss challenges for such integration on production platforms. Our results demonstrate how biological data analysis pipelines can benefit from infrastructure systems for data-intensive computing.
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