Mgpipe: A Toolbox for Investigating Metagenomics Data through Personalized Microbiota Metabolic Models

Background/Purpose: Predictive, personalized medicine requires tools that can account for individual variability, such as the genome, life style, and the microbiome. Recently, the gut microbiome has received great attention for its potential to modulate human metabolism and its role in health and disease. The aim of this project is the development of an easy-to-use tool (mgPipe (1) ) that enables researchers to investigate in silico the functional implications associated with changes in gut microbial composition in healthy individuals and patients.

Methods: mgPipe combines published algorithms, which map metagenomics data onto a reference database to determine microbial identity and abundance (2-3). Subsequently, this microbial information is combined with genome-scale metabolic reconstructions of more than 800 gut microbes (4) to generate personalized microbiome metabolic models. Using established constraint-based modeling methods from the COBRA toolbox (5) and machine learning approaches, mgPipe enables the in silico interrogation of the different functional, metabolic properties of these microbiome models.

Results and conclusion: mgPipe is an efficient tool to integrate metagenomic data with constraint based modeling enabling creation, simulation, and analysis of personalized microbiome metabolic models.

References

1. Baldini F, Heinken A, et al. The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities. bioRxiv. 2018:318485.
2. Bolger AM, et al. Bioinformatics. 2014:btu170.
3. Li H, et al. Bioinformatics. 2009;25(14):1754-60.
4. Magnúsdóttir S, et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nature Biotechnology. 2016.
5. Heirendt L, Arreckx S, et al. Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3. 0. arXiv preprint arXiv:171004038. 2017.