MOTIVATION: Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, publicly available analysis tools for further characterization are rare. Our work provides researchers with a harmonized workflow that covers different stages of in silico circRNA analyses, from prediction to first functional insights. RESULTS: Here, we present circtools, a modular, Python-based framework for computational circRNA analyses. The software includes modules for circRNA detection, internal sequence reconstruction, quality checking, statistical testing, screening for enrichment of RBP binding sites, differential exon RNase R resistance and circRNA-specific primer design. circtools supports researchers with visualization options and data export into commonly used formats. AVAILABILITY AND IMPLEMENTATION: circtools is available via https://github.com/dieterich-lab/circtools and http://circ.tools under GPLv3.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Massively parallel RNA sequencing (RNA-seq) in combination with metabolic labeling has become the de facto standard approach to study alterations in RNA transcription, processing or decay. Regardless of advances in the experimental protocols and techniques, every experimentalist needs to specify the key aspects of experimental design: For example, which protocol should be used (biochemical separation vs. nucleotide conversion) and what is the optimal labeling time? In this work, we provide approximate answers to these questions using the asymptotic theory of optimal design. Specifically, we investigate, how the variance of degradation rate estimates depends on the time and derive the optimal time for any given degradation rate. Subsequently, we show that an increase in sample numbers should be preferred over an increase in sequencing depth. Lastly, we provide some guidance on use cases when laborious biochemical separation outcompetes recent nucleotide conversion based methods (such as SLAMseq) and show, how inefficient conversion influences the precision of estimates. Code and documentation can be found at https://github.com/dieterich-lab/DesignMetabolicRNAlabeling.

MOTIVATION: Metabolic labelling of RNA is a well-established and powerful method to estimate RNA synthesis and decay rates. The pulseR R package simplifies the analysis of RNA-seq count data that emerge from corresponding pulse-chase experiments. RESULTS: The pulseR package provides a flexible interface and readily accommodates numerous different experimental designs. To our knowledge, it is the first publicly available software solution that models count data with the more appropriate negative-binomial model. Moreover, pulseR handles labelled and unlabelled spike-in sets in its workflow and accounts for potential labeling biases (e.g. number of uridine residues). AVAILABILITY AND IMPLEMENTATION: The pulseR package is freely available at https://github.com/dieterich-lab/pulseR under the GPLv3.0 licence. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.