AnimalMetaOmics: a multi-omics data resources for exploring animal microbial genomes and microbiomes
Ruirui Hu1, Fulin Li1, Yifan Chen2,3
1College of Life Sciences, Shihezi University, Shihezi, Xinjiang 832003, China.
Nucleic Acids Research
|October 28, 2023
Summary
The Animal Meta-omics landscape database offers extensive microbiome data, including genomes, gene functions, and protein information for diverse animal species. This resource aids in exploring animal microbiota classification and functional diversity.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Animal microbiomes are crucial for host health and function.
- Comprehensive data integration is needed to understand animal microbial communities.
- Existing resources lack integrated multi-omics data across diverse animal species.
Purpose of the Study:
- To establish a centralized, freely accessible database for animal meta-omics data.
- To provide a user-friendly platform for exploring animal microbiome composition, function, and activity.
- To facilitate research on animal microbiota classification, diversity, and functional genes.
Main Methods:
- Integrated metagenomic, metatranscriptomic, and metaproteomic data analysis.
- Annotation of microbial genomes from a wide range of animal species.
- Analysis of gene expression and protein identification within animal microbiomes.
- Development of a user-friendly interface for data retrieval and analysis.
Main Results:
- Annotated 55,898 microbial genomes from 581 animal species (bacteria, viruses, archaea, fungi).
- Analyzed 321 metatranscriptomic and 326 metaproteomic datasets.
- Characterized pan-genomic dynamics and composition of 679 bacterial and 13 archaea species.
- Database provides access to species, genomes, activity levels, protein functions, and pan-genome composition.
Conclusions:
- The Animal Meta-omics landscape database is a valuable resource for animal microbiome research.
- It enables efficient exploration of microbial communities across diverse animal hosts.
- The database supports deeper understanding of microbial classification, functional diversity, and biological processes.
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