MANTA, an integrative database and analysis platform that relates microbiome and phenotypic data

Yi-An Chen; Jonguk Park; Yayoi Natsume-Kitatani; Hitoshi Kawashima; Attayeb Mohsen; Koji Hosomi; Kumpei Tanisawa; Harumi Ohno; Kana Konishi; Haruka Murakami; Motohiko Miyachi; Jun Kunisawa; Kenji Mizuguchi

doi:10.1371/journal.pone.0243609

MANTA, an integrative database and analysis platform that relates microbiome and phenotypic data

PLoS One. 2020 Dec 4;15(12):e0243609. doi: 10.1371/journal.pone.0243609. eCollection 2020.

Affiliations

¹ Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, Japan.
² Laboratory of Vaccine Materials, Center for Vaccine and Adjuvant Research and Laboratory of Gut Environmental System, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, Japan.
³ Department of Physical Activity Research, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku, Tokyo, Japan.

Abstract

With an ever-increasing interest in understanding the relationships between the microbiota and the host, more tools to map, analyze and interpret these relationships have been developed. Most of these tools, however, focus on taxonomic profiling and comparative analysis among groups, with very few analytical tools designed to correlate microbiota and the host phenotypic data. We have developed a software program for creating a web-based integrative database and analysis platform called MANTA (Microbiota And pheNoType correlation Analysis platform). In addition to storing the data, MANTA is equipped with an intuitive user interface that can be used to correlate the microbial composition with phenotypic parameters. Using a case study, we demonstrated that MANTA was able to quickly identify the significant correlations between microbial abundances and phenotypes that are supported by previous studies. Moreover, MANTA enabled the users to quick access locally stored data that can help interpret microbiota-phenotype relations. MANTA is available at https://mizuguchilab.org/manta/ for download and the source code can be found at https://github.com/chenyian-nibio/manta.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Databases, Factual*
Dietary Fats / metabolism
Eating
Exercise
Gastrointestinal Microbiome
Humans
Microbiota*
Phenotype
Software

Substances

Dietary Fats

Grants and funding

This work was supported by the Japan Society for the Promotion of Science under grant numbers 17K07268 (K.M.), 18H02150 (J.K.), 18H02674 (J.K.), 17K09604 (J.K.), and 18K17997 (K.H.); the Japan Agency for Medical Research and Development (AMED) under grant numbers 17fk0108223h0002 (J.K.), 17ek0410032s0102 (J.K.), 17fk0108207h0002 (J.K.), 17ek0210078h0002 (J.K.), 17ak0101068h0001 (J.K.), 17gm1010006s0101 (J.K.), and 18ck0106243h0003 (J.K.); the Ministry of Health, Labour and Welfare of Japan under grant number 15654110 (M.M.); the ONO Medical Research Foundation (J.K.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.