CGAT-core: a python framework for building scalable, reproducible computational biology workflows

• DOI: 10.12688/f1000research.18674.2
• Publication Date: 2019-07-16T10:25:08Z
• Authors: Adam P. Cribbs; Sebastian Luna-Va...; Charlotte George; Ian M. Sudbery; Antonio J. Berlan...; Stephen N. Sansom; Tom Smith; Nicholas E. Ilott; Jethro Johnson; Jakub Scaber; Katherine Brown; David Sims; Andreas Heger
• Keywords: Python

<ns4:p>In the genomics era computational biologists regularly need to process, analyse and integrate large complex biomedical datasets. Analysis inevitably involves multiple dependent steps, resulting in pipelines or workflows, often with several branches. Large data volumes mean that processing needs be quick efficient scientific rigour requires analysis consistent fully reproducible. We have developed CGAT-core, a python package for rapid construction of workflows. CGAT-core seamlessly handles parallelisation across high performance computing clusters, integration Conda environments, full parameterisation, database logging. To illustrate our workflow framework, we present pipeline RNAseq using pseudo-alignment.</ns4:p>