SEDE-GPS: socio-economic data enrichment based on GPS information

Abstract

BackgroundMicrobes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers. In freshwater ecosystems, the biodiversity in and the composition of microbial communities can be used as indicators for environmental quality. Recently, some environmental features have been identified that influence microbial ecosystems. However, the impact of human action on lake microbiomes is not well understood. This is, in part, due to the fact that environmental data is, albeit theoretically accessible, not easily available.ResultsIn this work, we present SEDE-GPS, a tool that gathers data that are relevant to the environment of an user-provided GPS coordinate. To this end, it accesses a list of public and corporate databases and aggregates the information in a single file, which can be used for further analysis. To showcase the use of SEDE-GPS, we enriched a lake microbial ecology sequencing dataset with around 18,000 socio-economic, climate, and geographic features. The sources of SEDE-GPS are public databases such as Eurostat, the Climate Data Center, and OpenStreetMap, as well as corporate sources such as Twitter. Using machine learning and feature selection methods, we were able to identify features in the data provided by SEDE-GPS that can be used to predict lake microbiome alpha diversity.ConclusionThe results presented in this study show that SEDE-GPS is a handy and easy-to-use tool for comprehensive data enrichment for studies of ecology and other processes that are affected by environmental features. Furthermore, we present lists of environmental, socio-economic, and climate features that are predictive for microbial biodiversity in lake ecosystems. These lists indicate that human action has a major impact on lake microbiomes. SEDE-GPS and its source code is available for download at http://SEDE-GPS.heiderlab.deElectronic supplementary materialThe online version of this article (10.1186/s12859-018-2419-4) contains supplementary material, which is available to authorized users.

A multi-omics study on quantifying antimicrobial resistance in European freshwater lakes

“…As stated above, livestock farming is one of the main fields for applying antibiotics ( Done et al, 2015 , Van Boeckel et al, 2017 ). We employed SEDE-GPS to retrieve information on agriculture in an area of 20 km around the GPS coordinates of the lakes ( Sperlea et al, 2018 ). Antibiotics or resistant bacteria from sewage with human excreta, in general, can enter freshwater in many ways.…”

“…Moreover, we analyzed the association between resistance and farmland. We used SEDE-GPS for gathering socio-economic data ( Sperlea et al, 2018 ). That is, we collected all data related to the term agriculture as defined by Eurostat ( https://ec.europa.eu/eurostat/en/web/agriculture/data ), for instance, agricultural products and organic farming, among others.…”

The relationship between land cover and microbial community composition in European lakes

“…OSM land cover data was extracted from the OSM planet file from September 2012 archived at archive.org. This file was loaded in a PostgreSQL database and queried using a routine adapted from SEDE-GPS (Sperlea et al, 2018) to retrieve the map tiles surrounding the sampling position, to fuse these, and to extract a circular area of a given radius. Map tiles were rendered using the default mapnik map style, which was adjusted to (i) merge pixels of land use sub-categories with the respective main category (such as "tertiary road" with "road") and (ii) remove signs, labels, and point of interest markers.…”

Quantifying the information content of lake microbiomes using a machine learning-based framework

“…Our main contribution is a machine learning-based framework for the quantification of the information shared between the microbiome and a total of 25 physico-chemical and positional (i.e., GPS coordinates and altitude) parameters of an ecosystem. It builds upon a wealth of studies that elucidate the role of the microbiome in ecology using machine learning [19,[31][32][33][34][35][36][37][38][39]. In our framework, a model learns a projection of the microbial prevalence space to a single dimension for each of the parameters, which makes it able to handle the extremely high dimensionality of amplicon-based microbiome datasets.…”