Software engineering for scientific big data analysis.

GrĂ¼ning BA, Lampa S, Vaudel M, Blankenberg D

Gigascience 8 (5) - [2019-05-01; online 2019-05-24]

The increasing complexity of data and analysis methods has created an environment where scientists, who may not have formal training, are finding themselves playing the impromptu role of software engineer. While several resources are available for introducing scientists to the basics of programming, researchers have been left with little guidance on approaches needed to advance to the next level for the development of robust, large-scale data analysis tools that are amenable to integration into workflow management systems, tools, and frameworks. The integration into such workflow systems necessitates additional requirements on computational tools, such as adherence to standard conventions for robustness, data input, output, logging, and flow control. Here we provide a set of 10 guidelines to steer the creation of command-line computational tools that are usable, reliable, extensible, and in line with standards of modern coding practices.

Bioinformatics Support and Infrastructure [Collaborative]

Bioinformatics Support, Infrastructure and Training [Collaborative]

PubMed 31121028

DOI 10.1093/gigascience/giz054

Crossref 10.1093/gigascience/giz054

pii: 5497810
pmc: PMC6532757