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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. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation: The identification of active transcriptional regulatory elements is crucial to understand regulatory networks driving cellular processes such as cell development and the onset of diseases. It has recently been shown that chromatin structure information, such as DNase I hypersensitivity (DHS) or histone modifications, significantly improves cell-specific predictions of transcription factor binding sites. However, no method has so far successfully combined both DHS and histone modification data to perform active binding site prediction. Results: We propose here a method based on hidden Markov models to integrate DHS and histone modifications occupancy for the detection of open chromatin regions and active binding sites. We have created a framework that includes treatment of genomic signals, model training and genome-wide application. In a comparative analysis, our method obtained a good trade-off between sensitivity versus specificity and superior area under the curve statistics than competing methods. Moreover, our technique does not require further training or sequence information to generate binding location predictions. Therefore, the method can be easily applied on new cell types and allow flexible downstream analysis such as de novo motif finding. Availability and implementation: Our framework is available as part of the Regulatory Genomics Toolbox. The software information and all benchmarking data are available at http://costalab.org/wp/dh-hmm . Contact: ivan.costa@rwth-aachen.de or eduardo.gusmao@rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Circular RNAs (circRNAs) are a poorly characterized class of molecules that have been identified decades ago. Emerging high-throughput sequencing methods as well as first reports on confirmed functions have sparked new interest in this RNA species. However, the computational detection and quantification tools are still limited. Results: We developed the software tandem, DCC and CircTest. DCC uses output from the STAR read mapper to systematically detect back-splice junctions in next-generation sequencing data. DCC applies a series of filters and integrates data across replicate sets to arrive at a precise list of circRNA candidates. We assessed the detection performance of DCC on a newly generated mouse brain data set and publicly available sequencing data. Our software achieves a much higher precision than state-of-the-art competitors at similar sensitivity levels. Moreover, DCC estimates circRNA versus host gene expression from counting junction and non-junction reads. These read counts are finally used to test for host gene-independence of circRNA expression across different experimental conditions by our R package CircTest. We demonstrate the benefits of this approach on previously reported age-dependent circRNAs in the fruit fly. Availability and implementation: The source code of DCC and CircTest is licensed under the GNU General Public Licence (GPL) version 3 and available from https://github.com/dieterich-lab/[DCC or CircTest]. Contact: christoph.dieterich@age.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: Next generation sequencing technologies facilitate genome-wide analysis of several biological processes. We are interested in whole-genome genotyping. To our knowledge, none of the existing single nucleotide polymorphism (SNP) callers consider the quality of the reference genome, which is not necessary for high-quality assemblies of well-studied model organisms. However, most genome projects will remain in draft status with little to no genome assembly improvement due to time and financial constraints. Here, we present a simple yet elegant solution (‘ACCUSA’) that considers both the read qualities as well as the reference genome's quality using a Bayesian framework. We demonstrate that ACCUSA is as good as the current SNP calling software in detecting true SNPs. More importantly, ACCUSA does not call spurious SNPs, which originate from a poor reference sequence. Availability: ACCUSA is available free of charge to academic users and may be obtained from ftp://bbc.mdc-berlin.de/software. ACCUSA is programmed in JAVA 6 and runs on any platform with JAVA support. Contact: christoph.dieterich@mdc-berlin.de Supplementary information: Supplementary data are available at Bioinformatics online.