PT  - JOURNAL ARTICLE
AU  - Sahil Seth
AU  - Samir Amin
AU  - Xingzhi Song
AU  - Xizeng Mao
AU  - Huandong Sun
AU  - Andrew Futreal
AU  - Jianhua Zhang
TI  - Flowr: Robust and efficient pipelines using a simple language-agnostic approach
AID  - 10.1101/029710
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 029710
4099  - http://biorxiv.org/content/early/2015/10/22/029710.short
4100  - http://biorxiv.org/content/early/2015/10/22/029710.full
AB  - 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