A new version of this entry is available:
Loading...
Article
2023
The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes
The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes
Abstract (English)
Background: Metagenomic data can shed light on animal-microbiome relationships and the functional potential of these communities. Over the past years, the generation of metagenomics data has increased exponentially, and so has the availability and reusability of data present in public repositories. However, identifying which datasets and associated metadata are available is not straightforward. We created the Animal-Associated Metagenome Metadata Database (AnimalAssociatedMetagenomeDB - AAMDB) to facilitate the identification and reuse of publicly available non-human, animal-associated metagenomic data, and metadata. Further, we used the AAMDB to (i) annotate common and scientific names of the species; (ii) determine the fraction of vertebrates and invertebrates; (iii) study their biogeography; and (iv) specify whether the animals were wild, pets, livestock or used for medical research.
Results: We manually selected metagenomes associated with non-human animals from SRA and MG-RAST. Next, we standardized and curated 51 metadata attributes (e.g., host, compartment, geographic coordinates, and country). The AAMDB version 1.0 contains 10,885 metagenomes associated with 165 different species from 65 different countries. From the collected metagenomes, 51.1% were recovered from animals associated with medical research or grown for human consumption (i.e., mice, rats, cattle, pigs, and poultry). Further, we observed an over-representation of animals collected in temperate regions (89.2%) and a lower representation of samples from the polar zones, with only 11 samples in total. The most common genus among invertebrate animals was Trichocerca (rotifers).
Conclusion: Our work may guide host species selection in novel animal-associated metagenome research, especially in biodiversity and conservation studies. The data available in our database will allow scientists to perform meta-analyses and test new hypotheses (e.g., host-specificity, strain heterogeneity, and biogeography of animal-associated metagenomes), leveraging existing data. The AAMDB WebApp is a user-friendly interface that is publicly available at https://webapp.ufz.de/aamdb/ .
File is subject to an embargo until
This is a correction to:
A correction to this entry is available:
This is a new version of:
Other version
Notes
Publication license
Publication series
Published in
Animal microbiome, 5 (2023), 48.
https://doi.org/10.1186/s42523-023-00267-3.
ISSN: 2524-4671
Other version
Faculty
Institute
Examination date
Supervisor
Cite this publication
Avila Santos, A. P., Kabiru Nata’ala, M., Kasmanas, J. C., Bartholomäus, A., Keller-Costa, T., Jurburg, S. D., Tal, T., Camarinha-Silva, A., Saraiva, J. P., Ponce de Leon Ferreira de Carvalho, A. C., Stadler, P. F., Sipoli Sanches, D., & Rocha, U. (2023). The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes. Animal microbiome, 5(1). https://doi.org/10.1186/s42523-023-00267-3
Edition / version
Citation
DOI
ISSN
ISBN
Language
English
Publisher
Publisher place
Classification (DDC)
630 Agriculture
Original object
University bibliography
Standardized keywords (GND)
BibTeX
@article{Avila Santos2023,
doi = {10.1186/s42523-023-00267-3},
url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16981},
author = {Avila Santos, Anderson Paulo and Kabiru Nata’ala, Muhammad and Kasmanas, Jonas Coelho et al.},
title = {The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes},
journal = {Animal microbiome},
year = {2023},
volume = {5},
}
