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

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 a...

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Bibliographic Details
Published in:PloS one 2020-12, Vol.15 (12), p.e0243609
Main Authors: Chen, Yi-An, Park, Jonguk, Natsume-Kitatani, Yayoi, Kawashima, Hitoshi, Mohsen, Attayeb, Hosomi, Koji, Tanisawa, Kumpei, Ohno, Harumi, Konishi, Kana, Murakami, Haruka, Miyachi, Motohiko, Kunisawa, Jun, Mizuguchi, Kenji
Format: Article
Language:English
Subjects:
Artificial intelligence
Bioinformatics
Biology and Life Sciences
Biomedical research
Comparative analysis
Computer and Information Sciences
Computer programs
Correlation
Correlation analysis
Database administration
Databases, Factual
Dietary Fats - metabolism
Eating
Engineering and Technology
Evaluation
Exercise
Gastrointestinal Microbiome
Genetic aspects
Host-bacteria relationships
Humans
Laboratories
Lifestyles
Medicine and Health Sciences
Microbiomes
Microbiota
Microbiota (Symbiotic organisms)
Microorganisms
Nutrition
Phenotype
Phenotypes
Phylogenetics
Properties
Software
Source code
Taxonomy
User interface
Vaccines
Veganism
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0243609