Bioinformatic workflows to analyze data, especially high-throughput sequencing data, have become uniquitous in the biological sciences. During this development, researchers in the field of bioinformatics have been making important advances to the way we run and share our analyses with peers, but no clear standard has emerged allowing for fully reproducible workflows. By combining principles of software engineering with advancements in software deployment such as containerization to bioinformatics workflow development, standardized workflows can be developed that are easily deployable and can be consistently reproduced. Here, I outline my implementation of such a standardized bioinformatics workflow that is used as part of a sequencing service at the Hong Kong University of Science and T...[ Read more ]

Bioinformatic workflows to analyze data, especially high-throughput sequencing data, have become uniquitous in the biological sciences. During this development, researchers in the field of bioinformatics have been making important advances to the way we run and share our analyses with peers, but no clear standard has emerged allowing for fully reproducible workflows. By combining principles of software engineering with advancements in software deployment such as containerization to bioinformatics workflow development, standardized workflows can be developed that are easily deployable and can be consistently reproduced. Here, I outline my implementation of such a standardized bioinformatics workflow that is used as part of a sequencing service at the Hong Kong University of Science and Technology (HKUST) Biosciences Central Research Facility (BioCRF).