Category: Data Analysis and Informatics

1396-B - A Data Analysis Solution for High Throughput Screening Data Built on Open-Source Software R and Shiny

Monday, February 5, 2018
5:00 PM - 6:00 PM

The company Chr. Hansen A/S produce starter cultures for dairy, and possess a large and constantly growing collection of lactic acid bacteria (LAB). When new strains are added to the collection they undergo a comprehensive physiological and biochemical characterization, in an automated laboratory setup. It is required that the data from these analyses are analysed in a fast and consistent manner, and that they are stored in a structured and persistent way. Here we describe the development of an inexpensive web-based software solution for analysis and storage of the characterization data. The solution is built using the open-source statistical software R in combination with the R-based web application framework Shiny, backed by a Microsoft SQL Server database. This combination allows for the use of the superior computational and statistical capabilities of R, as well as the fast and easy development of a browser-based GUI with Shiny, based on a reactive programming paradigm that does not require in-depth knowledge of HTML, CSS or JavaScript. The solution is comprised of a number of modules, each providing analysis and storage of a specific type of characterization assay data, thereby ensuring encapsulation and well-defined interoperability between the different functionalities of the solution. Using this solution the time spent on data analysis and the number of errors in the analysis have been reduced significantly. By using open-source software it is easy to update and expand the functionality of the solution, and at the same time ensuring access to state-of-the-art algorithms supported by the R community.

Steen Krogsgaard

Senior Scientist
Chr. Hansen A/S
Hoersholm, Denmark

Steen Krogsgaard, PhD, Senior Scientist, Chr. Hansen A/S
working as senior scientist with high-troughput screening, assay developement, automation and data management in biotechnology