SUMMARY: BRIDGE is a Shiny-based application that provides an accessible, modular platform for individual and integrative multi-omics analysis. Using an independent SQLite database backend, it offers a local, private, and user-friendly environment that requires no prior computational expertise. The application supports proteomics, phospho-proteomics, and RNA-seq analyses through a comprehensive suite of visualization and analytical modules, together with an integrated multi-omics analysis pipeline. Built-in caching and asynchronous processing improve responsiveness, enabling efficient exploration, analysis, and visualization of multi-omics datasets on moderate hardware. AVAILABILITY AND IMPLEMENTATION: BRIDGE is implemented in R using Shiny and is freely available as a Docker container at https://ghcr.io/paulilab/bridge. A public demonstration server with example datasets is available at https://bridge.imp.ac.at. Code and datasets are also available at https://github.com/paulilab/BRIDGE and under DOI: https://doi.org/10.5281/zenodo.20215824.

SUMMARY: RNA molecules play crucial roles in various biological processes. They mediate their function mainly by interacting with other RNAs or proteins. At present, information about these interactions is distributed over different resources, often providing the data in simple tab-delimited formats that differ between the databases. There is no standardized data format that can capture the nature of all these different interactions in detail. AVAILABILITY AND IMPLEMENTATION: Here, we propose the RNA interaction format (RIF) for the detailed representation of RNA-RNA and RNA-Protein interactions and provide reference implementations in C/C++, Python, and JavaScript. RIF is released under licence GNU General Public License version 3 (GNU GPLv3) and is available on https://github.com/RNABioInfo/rna-interaction-format.

MOTIVATION: MicroRNAs form an important class of RNA regulators that has been studied extensively. The miRBase and Rfam database provide rich, frequently updated information on both pre-miRNAs and their mature forms. These data sources, however, rely on individual data submission and thus are neither complete nor consistent in their coverage across different miRNA families. Quantitative studies of miRNA evolution therefore are difficult or impossible on this basis. RESULTS: We present here a workflow and a corresponding implementation, MIRfix, that automatically curates miRNA datasets by improving alignments of their precursors, the consistency of the annotation of mature miR and miR* sequence, and the phylogenetic coverage. MIRfix produces alignments that are comparable across families and sets the stage for improved homology search as well as quantitative analyses. AVAILABILITY AND IMPLEMENTATION: MIRfix can be downloaded from https://github.com/Bierinformatik/MIRfix. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Many repetitive DNA elements are transcribed at appreciable expression levels. Mapping the corresponding RNA sequencing reads back to a reference genome is notoriously difficult and error-prone task, however. This is in particular true if chemical modifications introduce systematic mismatches, while at the same time the genomic loci are only approximately identical, as in the case of tRNAs. Results: We therefore developed a dedicated mapping strategy to handle RNA-seq reads that map to tRNAs relying on a modified target genome in which known tRNA loci are masked and instead intronless tRNA precursor sequences are appended as artificial 'chromosomes'. In a first pass, reads that overlap the boundaries of mature tRNAs are extracted. In the second pass, the remaining reads are mapped to a tRNA-masked target that is augmented by representative mature tRNA sequences. Using both simulated and real life data we show that our best-practice workflow removes most of the mapping artefacts introduced by simpler mapping schemes and makes it possible to reliably identify many of chemical tRNA modifications in generic small RNA-seq data. Using simulated data the FDR is only 2%. We find compelling evidence for tissue specific differences of tRNA modification patterns. Availability and implementation: The workflow is available both as a bash script and as a Galaxy workflow from https://github.com/AnneHoffmann/tRNA-read-mapping. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Bioinformatics (2017) https://doi.org/10.1093/bioinformatics/btx756 The authors of the above paper wishes to inform readers that Dr. Elisa Vilardo was erroneously omitted as an author during the original publication of this manuscript. The paper has now been corrected online.