CGAT-core: a python framework for building scalable, reproducible computational biology workflows
Cribbs AP., Luna-Valero S., George C., Sudbery IM., Berlanga-Taylor AJ., Sansom SN., Smith T., Ilott NE., Johnson J., Scaber J., Brown K., Sims D., Heger A.
In the genomics era computational biologists regularly need to process, analyse and integrate large and complex biomedical datasets. Analysis inevitably involves multiple dependent steps, resulting in complex pipelines or workflows, often with several branches. Large data volumes mean that processing needs to be quick and efficient and scientific rigour requires that analysis be consistent and fully reproducible. We have developed CGAT-core, a python package for the rapid construction of complex computational workflows. CGAT-core seamlessly handles parallelisation across high performance computing clusters, integration of Conda environments, full parameterisation, database integration and logging. To illustrate our workflow framework, we present a pipeline for the analysis of RNAseq data using pseudo-alignment.