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.de
Highlights
Microbes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers
We describe the novel tool SEDEGPS (Socio-economic data enrichment based on global positioning system (GPS) information), which can be used to enrich data sets with data from public and publicly available corporate databases based on user-specified GPS information
The modules that query the Open Streetmap (OSM) databases, Identification of important features using EFS In order to find features in the output of SEDE-GPS that are predictive for lake microbial biodiversity, we used the R package EFS (Ensemble Feature Selection) and the eight alpha diversity metrics as target variable in separate analyses [13, 14]
Summary
Microbes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers. Some environmental features have been identified that influence microbial ecosystems. 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 available. We describe the novel tool SEDEGPS (Socio-economic data enrichment based on GPS information), which can be used to enrich data sets with data from public and publicly available corporate databases based on user-specified GPS information. SEDE-GPS has an easy-to-use graphical user interface and enables researchers to enrich their data with environmental and socio-economic information based on Sperlea et al BMC Bioinformatics 2018, 19(Suppl 15):440. This may lead to new insights into the influence of environmental and socio-economic features on a wide range of processes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
