Application of a new data management system to the study ofthe gut microbiome of children who are small for theirgestational age
Microbiome studies aim to answer the following questions: which organisms are in the sample and what is their impact on the patient or the environment? To answer these questions, investigators have to perform comparative analyses on their classified sequences based on the collected metadata, such as treatment, condition of the patient, or the environment. The integrity of sequences, classifications, and metadata is paramount for the success of such studies. Still, the area of data management for the preliminary study results appears to be neglected. Here, we present the development of a central data management system with an accessible web interface for the study of the gut microbiome in children who are small for their gestational age. We have called this system MetagenomicsDB. The web interface allows users, regardless of their bioinformatics expertise, to operate the system. The interface contains links to external resources in order to facilitate literature search, statistical analyses, and assessments of the potential pathogenicity of taxa. Preliminary study results are automatically quality-controlled and subsequently imported into a relational database. After exploration and optional filtering by the user, data are exported in a format directly suitable for follow-up analyses. Compared to a more conventional approach of storing the data in plain files, the automated quality control and database storage offered by MetagenomicsDB provides an enhanced quality assurance of the produced study results. This is especially true in a collaborative setting. Also, the automation of the data transfer from the format of the input data to the format needed for downstream analyses makes basic statistical and bioinformatic analyses accessible. The web interface not only allows us to perform our internal analyses, but will also facilitate transparent sharing of the complete study results at publication time with reviewers and the general public. We demonstrated the viability of this approach by making a subset of our preliminary study results already publicly accessible: https://www.bioinformatics.uni-muenster.de/tools/metagenomicsDB In the context of our study, our system provided more flexibility to conduct study- specific analyses and to integrate specific external resources than existing and necessarily more generic solutions. Our system is not yet ready to be widely applicable out-of-the-box for any microbiome study. However, we expect that due to the modular concept, the tests, and the extensive description of the underlying rationale, large parts of our implementation can be adapted for future projects in a fraction of the time needed to develop a new data management system from scratch. Thus, we report our endeavors in order to motivate the application of data management systems at the scale of single studies in microbiome research.