TY - JOUR T1 - Flowr: Robust and efficient pipelines using a simple language-agnostic approach JF - bioRxiv DO - 10.1101/029710 SP - 029710 AU - Sahil Seth AU - Samir Amin AU - Xingzhi Song AU - Xizeng Mao AU - Huandong Sun AU - Andrew Futreal AU - Jianhua Zhang Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/10/22/029710.abstract N2 - Motivation Bioinformatics 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.Results Flowr 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.Availability http://docs.flowr.space ER -