Retroposition is RNA-based gene duplication leading to the creation of single exon nonfunctional copies. Nevertheless, over time, many of these duplicates acquire transcriptional capabilities. In human in most cases, these so-called retrogenes do not code for proteins but function as regulatory long noncoding RNAs (lncRNAs). The mechanisms by which they can regulate other genes include microRNA sponging, modulation of alternative splicing, epigenetic regulation and competition for stabilizing factors, among others. Here, we summarize recent findings related to lncRNAs originating from retrocopies that are involved in human diseases such as cancer and neurodegenerative, mental or cardiovascular disorders. Special attention is given to retrocopies that regulate their progenitors or host genes. Presented evidence from the literature and our bioinformatics analyses demonstrates that these retrocopies, often described as unimportant pseudogenes, are significant players in the cell’s molecular machinery.

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 MetagenomicsDB (http://github.com/IOB-Muenster/MetagenomicsDB; accessed 2024/12/18), a central data management system for the study of the gut microbiome in children who are small for their gestational age (SGA). Our system provided more flexibility to conduct study-specific analyses and to integrate specific external resources than existing and necessarily more generic solutions. It supports short or long read data produced by virtually any sequencing instrument targeting (parts of) popular marker genes, such as the 16S rRNA gene and its variable regions. Classifications of these reads from the MetaG and Kraken 2 software are supported. The main goals of the system are to store the pre-computed study data securely under concurrent load and to make downstream analyses accessible to all researchers, regardless of programming proficiency. Thus, after initial plausibility checks on the input data to reduce human error, data are stored in a relational database and can be continuously updated over the whole life time of the study. We used a modular approach for MetagenomicsDB with comprehensive tests verifying the expected behavior and extensively described the underlying rational which allows users to adapt the system to their needs. We advocate the use of MetagenomicsDB as the backend for a graphical web interface. We showcase the potential of this approach at the example of our study on SGA children (http://www.bioinformatics.uni-muenster.de/tools/metagenomicsDB; accessed 2024/12/02). Without restrictions caused by the level of programming proficiency, our team members could explore the study data and optionally filter them using the graphical interface, before exporting the data in a format directly suitable for external normalization of read counts and statistical analyses. Study results could be conveniently and transparently shared with the public, as demonstrated here. Links to external resources facilitated literature search with regard to the SGA condition and assessments of the potential pathogenicity of taxa. Since different users will have different demands regarding features, data security, and web environments, we provide our implementation of the web interface as a visual example. By providing users with the MetagenomicsDB backend which constitutes the major part of the system, we ensure that custom development can be finished in a reasonable amount of time. We report our endeavors in order to motivate the application of data management systems at the scale of single studies in microbiome research.

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.