RNAnue: efficient data analysis for RNA-RNA interactomics

ChimericFragments: computation, analysis and visualization of global RNA networks

RNA-RNA interactions are a key feature of post-transcriptional gene regulation in all domains of life. While ever more experimental protocols are being developed to study RNA duplex formation on a genome-wide scale, computational methods for the analysis and interpretation of the underlying data are lagging behind. Here, we present ChimericFragments, an analysis framework for RNA-seq experiments that produce chimeric RNA molecules. ChimericFragments implements a novel statistical method based on the complementarity of the base-pairing RNAs around their ligation site and provides an interactive graph-based visualization for data exploration and interpretation. ChimericFragments detects true RNA-RNA interactions with high precision and is compatible with several widely used experimental procedures such as RIL-seq, LIGR-seq or CLASH. We further demonstrate that ChimericFragments enables the systematic detection of novel RNA regulators and RNA-target pairs with crucial roles in microbial physiology and virulence. ChimericFragments is written in Julia and available at: https://github.com/maltesie/ChimericFragments.

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites

BACKGROUND

RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate.

RESULTS

We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs.

CONCLUSIONS

CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

RNA interaction format: a general data format for RNA interactions

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.

Chemical RNA Cross-Linking: Mechanisms, Computational Analysis, and Biological Applications

In recent years, RNA has emerged as a multifaceted biomolecule that is involved in virtually every function of the cell and is critical for human health. This has led to a substantial increase in research efforts to uncover the many chemical and biological aspects of RNA and target RNA for therapeutic purposes. In particular, analysis of RNA structures and interactions in cells has been critical for understanding their diverse functions and druggability. In the last 5 years, several chemical methods have been developed to achieve this goal, using chemical cross-linking combined with high-throughput sequencing and computational analysis. Applications of these methods resulted in important new insights into RNA functions in a variety of biological contexts. Given the rapid development of new chemical technologies, a thorough perspective on the past and future of this field is provided. In particular, the various RNA cross-linkers and their mechanisms, the computational analysis and challenges, and illustrative examples from recent literature are discussed.

Bioinformatic and experimental methods to identify and validate bacterial RNA-human RNA interactions

Ample evidence supports the importance of the microbiota on human health and disease. Recent studies suggest that extracellular vesicles are an important means of bacterial-host communication, in part via the transport of small RNAs (sRNAs). Bacterial sRNAs have been shown to co-precipitate with human and mouse RNA-induced silencing complex, hinting that some may regulate gene expression as eukaryotic microRNAs do. Bioinformatic tools, including those that can incorporate an sRNA's secondary structure, can be used to predict interactions between bacterial sRNAs and human messenger RNAs (mRNAs). Validation of these potential interactions using reproducible experimental methods is essential to move the field forward. This review will cover the evidence of interspecies communication via sRNAs, bioinformatic tools currently available to identify potential bacterial sRNA-host (specifically, human) mRNA interactions, and experimental methods to identify and validate those interactions.

SCRAP: a bioinformatic pipeline for the analysis of small chimeric RNA-seq data

MicroRNAs (miRNAs) are small non-coding RNAs (sncRNAs) that function in post-transcriptional gene regulation through imperfect base pairing with mRNA targets which results in inhibition of translation and typically destabilization of bound transcripts. Sequence-based algorithms historically used to predict miRNA targets face inherent challenges in reliably reflecting in vivo interactions. Recent strategies have directly profiled miRNA-target interactions by crosslinking and ligation of sncRNAs to their targets within the RNA-induced silencing complex (RISC), followed by high throughput sequencing of the chimeric sncRNA:target RNAs. Despite the strength of these direct profiling approaches, standardized pipelines for effectively analyzing the resulting chimeric sncRNA:target RNA sequencing data are not readily available. Here we present SCRAP, a robust Small Chimeric RNA Analysis Pipeline for the bioinformatic processing of chimeric sncRNA:target RNA sequencing data. SCRAP consists of two parts, each of which are specifically optimized for the distinctive characteristics of chimeric small RNA sequencing reads: first, read processing and alignment and second, peak calling and annotation. We apply SCRAP to benchmark chimeric sncRNA:target RNA sequencing datasets generated by distinct molecular approaches, and compare SCRAP to existing chimeric RNA analysis pipelines. SCRAP has minimal hardware requirements, is cross-platform, and contains extensive annotation to broaden accessibility for processing small chimeric RNA sequencing data and enable insights about the targets of small non-coding RNAs in regulating diverse biological systems.