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Fast and flexible analysis of linked microbiome data with mako

Nature Methods volume 19, pages 51–54 (2022)Cite this article

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A Publisher Correction to this article was published on 23 March 2022

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Abstract

Mako is a software tool that converts microbiome data and networks into a graph database and visualizes query results, thus allowing users without programming knowledge to carry out network-based queries. Mako is accompanied by a database compiled from 60 microbiome studies that is easily extended with the user’s own data. We illustrate mako’s strengths by enumerating association partners linked to propionate production and comparing frequencies of different network motifs across habitat types.

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Fig. 1: Features of mako.
Fig. 2: Motif identification with Neo4j.

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Data availability

The complete Neo4j database used in this paper has been used to generate a Neo4j dump file. An archived version of this file has been submitted to Zenodo17. Instructions for reconstructing this database are available in mako’s documentation. All BIOM files were downloaded from QIITA6 and a full overview of these studies can be found in Supplementary Table 1.

Code availability

The latest version of the mako software can be downloaded via https://github.com/ramellose/mako/. An archived version, including the script used to quantify motifs in the Neo4j database, has been submitted to Zenodo17. All mako source code and the included script are available under the Apache License 2.0. An extensive manual for running mako and Neo4j (via Docker) is available via https://ramellose.github.io/mako_docs/. Additionally, an accompanying compute capsule allows a demo of mako and a Neo4j database to be run without requiring any installations21.

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Acknowledgements

This project was supported by the KU Leuven under grant no. STG/16/006. K.F. has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program under grant agreement no. 801747.

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Authors and Affiliations

  1. KU Leuven Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, Leuven, Belgium

    Lisa Röttjers & Karoline Faust

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  1. Lisa Röttjers
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  2. Karoline Faust
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Contributions

L.R. developed the software, carried out the case studies and drafted the paper. K.F. contributed to the design of the software and the case studies, and provided substantial revisions of the paper. Both authors read and approved the final paper.

Corresponding author

Correspondence to Karoline Faust.

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The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Jianguo Xia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Lin Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

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Supplementary Table 1

Excel file containing a summary of all downloaded BIOM files and derived network size (in terms of node number and edge number) used for the study ‘Fast and flexible analysis of linked microbiome data with mako’.

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Röttjers, L., Faust, K. Fast and flexible analysis of linked microbiome data with mako. Nat Methods 19, 51–54 (2022). https://doi.org/10.1038/s41592-021-01335-9

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