With continuing improvements and reducing costs of high-throughput technologies, microbiologists are increasingly collecting multi-omics datasets. However, the tools and techniques used to analyse these kinds of data are often highly specialised and require bioinformatics, statistics and often coding experience. Many studies also tend to report on a single aspect of the data whilst overlooking other potentially interesting phenomena. Consequently, many of these multi-omics data sets are not being used to their full potential. MORF was created as a solution to these problems by providing access to multi-omics datasets through an online interface which presents the data in a user-friendly and accessible way. No coding experience or specialist statistical knowledge is required, and users are free to explore the data using interactive graphics and simple analysis tools.

Here we demonstrate MORF using multi-omics datasets from two experiments using bacteria in industrial fermentation processes. First, engineered to produce styrene, a valuable chemical used in the manufacture of polymers, and secondly a which produces the biofuel butanol. A key outcome was the identification of targets believed to be involved in responding to membrane stress, which we identified using MORF’s differential gene and protein analysis tools. Work is underway to further characterise and engineer these targets to improve product yields. In conclusion, MORF provides a framework for omics analysis that can be applied to any organism or set of experimental conditions, and will help researchers and collaborators to make the most of their data.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.

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