MOTIVATION: High throughput (HT) screens in the omics field are typically analyzed by automated pipelines that generate static visualizations and comprehensive spreadsheet data for scientists. However, exploratory and hypothesis driven data analysis are key aspects of the understanding of biological systems, both generating extensive need for customized and dynamic visualization. RESULTS: Here we describe WIlsON, an interactive workbench for analysis and visualization of multi-omics data. It is primarily intended to empower screening platforms to offer access to pre-calculated HT screen results to the non-computational scientist. Facilitated by an open file format, WIlsON supports all types of omics screens, serves results via a web-based dashboard, and enables end users to perform analyses and generate publication-ready plots. AVAILABILITY AND IMPLEMENTATION: We implemented WIlsON in R with a focus on extensibility using the modular Shiny and Plotly frameworks. A demo of the interactive workbench without limitations may be accessed at http://loosolab.mpi-bn.mpg.de. A standalone Docker container as well as the source code of WIlsON are freely available from our Docker hub https://hub.docker. com/r/loosolab/wilson, CRAN https://cran.r-project.org/web/packages/wilson/, and GitHub repository https://github.molgen.mpg.de/loosolab/wilson-apps, respectively.

BACKGROUND: MicroRNAs (miRNAs) impact various biological processes within animals and plants. They complementarily bind target mRNAs, effecting a post-transcriptional negative regulation on mRNA level. The investigation of miRNA target interactions (MTIs) by high throughput screenings is challenging, as frequently used in silico target prediction tools are prone to emit false positives. This issue is aggravated for niche model organisms, where validated miRNAs and MTIs both have to be transferred from well described model organisms. Even though DBs exist that contain experimentally validated MTIs, they are limited in their search options and they utilize different miRNA and target identifiers. RESULTS: The implemented pipeline LimiTT integrates four existing DBs containing experimentally validated MTIs. In contrast to other cumulative databases (DBs), LimiTT includes MTI data of 26 species. Additionally, the pipeline enables the identification and enrichment analysis of MTIs with and without species specificity based on dynamic quality criteria. Multiple tabular and graphical outputs are generated to permit the detailed assessment of results. CONCLUSION: Our freely available web-based pipeline LimiTT ( https://bioinformatics.mpi-bn.mpg.de/ ) is optimized to determine MTIs with and without species specification. It links miRNAs and/or putative targets with high granularity. The integrated mapping to homologous target identifiers enables the identification of MTIs not only for standard models, but for niche model organisms as well.

UNLABELLED: MicroRNAs (miRNAs) represent an important class of small non-coding RNAs regulating gene expression in eukaryotes. Present algorithms typically rely on genomic data to identify miRNAs and require extensive installation procedures. Niche model organisms lacking genomic sequences cannot be analyzed by such tools. Here we introduce the MIRPIPE application enabling rapid and simple browser-based miRNA homology detection and quantification. MIRPIPE features automatic trimming of raw RNA-Seq reads originating from various sequencing instruments, processing of isomiRs and quantification of detected miRNAs versus public- or user-uploaded reference databases. AVAILABILITY AND IMPLEMENTATION: The Web service is freely available at http://bioinformatics.mpi-bn.mpg.de. MIRPIPE was implemented in Perl and integrated into Galaxy. An offline version for local execution is also available from our Web site.