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An automated workflow for multi-omics screening of microbial model organisms

Donati, S.; Mattanovich, M.; Hjort, P.; Jacobsen, S. A. B.; Blomquist, S. D.; Mangaard, D.; Gurdo, N.; Pastor, F. P.; Maury, J.; Hanke, R.; Herrgard, M.; Wulff, T.; Jakociunas, T.; Nielsen, L. K.; McCloskey, D.

2022-07-18 systems biology
10.1101/2022.07.18.500181 bioRxiv
Show abstract

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation is needed to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

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The top 6 journals account for 50% of the predicted probability mass.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.