부산대학교 도서관

High-throughput bioinformatics research is complex and requires the combination of multiple experimental approaches each producing large amounts of diverse data. The analysis and evaluation of these data are equally complex requiring specific integrations of various software components into complex workflows. The challenge is to provide less technically involved bioinformaticians with simple interfaces to specify the workflow of commands they need while at the same time scale up to hundreds of jobs to get the terabytes of genetic data processed by recent methods. Here, we present a computational framework for bioinformatics which enables data and workflow management in a distributed computational environment. Firstly, we propose a new data model to specify workflow execution logic on available network resources and components. Our model extends existing generic workflow and bioinformatics models to describe workflows compactly and unambiguously. Secondly, we present the implementation of our computational framework, which is constructed as a computational cloud for bioinformatics using open source off-the-shelf components. Finally, we demonstrate applications of the framework on complex real-world bioinformatics tasks.