bioRxivBioinformaticsPreprint
Flowr: Robust and efficient pipelines using a simple language-agnostic approach
Sahil SethiD, Samir AminiD, Xingzhi SongiD, Xizeng Mao, Huandong Sun, Andrew Futreal, Jianhua Zhang
Abstract
MotivationBioinformatics analyses have become increasingly intensive computing processes, with lowering costs and increasing numbers of samples. Each laboratory spends time creating and maintaining a set of pipelines, which may not be robust, scalable, or efficient. Further, the existence of different computing environments across institutions hinders both collabo-ration and the portability of analysis pipelines.\n\nResultsFlowr is a robust and scalable framework for designing and deploying computing pipelines in an easy-to-use fashion. It implements a scatter-gather approach using computing clusters, simplifying the concept to the use of five simple terms (in submission and dependency types). Most importantly, it is flexible, such that customizing existing pipelines is easy, and since it works across several computing environments (LSF, SGE, Torque, and SLURM), it is portable.\n\nAvailabilityhttp://docs.flowr.space
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