OMEinfo: global geographic metadata for -omics experiments

Abstract

Summary: 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.

Availability and implementation: 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.

Figure 1.

The OMEinfo workflow. Users upload a metadata file with sample, latitude, and longitude to the Dockerised web app (or CLI). OMEinfo processes locations and retrieves relevant sub-tiles from the Cloud Optimized GeoTIFF necessary for annotating the location. Features are annotated for each location and upon completion, interactive maps and plots are displayed to the user. Metadata and citations can then be downloaded and integrated into downstream analysis. This workflow is further described in Sections 2.1 and 2.2. A walkthrough of using the Dash app is provided in Supplementary Information Section 2.

Figure 2.

Sample metadata distribution in the ENA Env. dataset. (A) The breakdown of samples according to the rurality index shows most samples are from urban centres, very low-density locations or water. (B) Breakdown of samples according to Köppen-Geiger Climate classification. Most samples are taken from ‘Temperate, no dry season, warm summer’ climates. NDS, no dry season; WS, warm summer; HS, hot summer.