OMEinfo: global geographic metadata for -omics experiments.

Clicks: 103
ID: 278314
2024
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Abstract
Microbiome studies increasingly associate geographical features like rurality and climate with microbiomes. It is essential to correctly integrate rich geographical metadata; and inconsistent definitions of rurality, can hinder cross-study comparisons. We address this with OMEinfo, a tool for automated retrieval of consistent geographical metadata from user-provided location data. OMEinfo leverages open data sources such as the Global Human Settlement Layer, and Open-Data Inventory for Anthropogenic Carbon dioxide. OMEinfo's web-app enables users to visualize and investigate the spatial distribution of metadata features. OMEinfo promotes reproducibility and consistency in microbiome metadata through a standardized metadata retrieval approach. To demonstrate utility, OMEinfo is used to replicate the results of a previous study linking population density to bacterial diversity. As the field explores relationships between microbiomes and geographical features, tools like OMEinfo will prove vital in developing a robust, accurate, and interconnected understanding of these interactions, whilst having applicability beyond this field to any studies utilizing location-based metadata. Finally, we release the OMEinfo annotation dataset of 5.3 million OMEinfo annotated samples from the ENA, for use in retrospective analyses of sequencing samples, and suggest several ways researchers and sequencing read repositories can improve the quality of underlying metadata submitted to these public stores.OMEinfo is freely available and released under an MIT licence. OMEinfo source code is available at https://github.com/m-crown/OMEinfo/ and https://doi.org/10.5281/zenodo.10518763.
Reference Key
crown2024omeinfobioinformatics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Crown, Matthew;Bashton, Matthew;
Journal Bioinformatics advances
Year 2024
DOI
vbae025
URL
Keywords
deformable image registration ground truth dataset lung motion

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