SUMMARY: Many experimental approaches have been developed to identify transcription start sites (TSS) from genomic scale data. However, experiment specific biases lead to large numbers of false-positive calls. Here, we present our integrative approach iTiSS, which is an accurate and generic TSS caller for any TSS profiling experiment in eukaryotes, and substantially reduces the number of false positives by a joint analysis of several complementary datasets. AVAILABILITY AND IMPLEMENTATION: iTiSS is platform independent and implemented in Java (v1.8) and is freely available at https://www.erhard-lab.de/software and https://github.com/erhard-lab/iTiSS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: Global quantification of total RNA is used to investigate steady state levels of gene expression. However, being able to differentiate pre-existing RNA (that has been synthesized prior to a defined point in time) and newly transcribed RNA can provide invaluable information e.g. to estimate RNA half-lives or identify fast and complex regulatory processes. Recently, new techniques based on metabolic labeling and RNA-seq have emerged that allow to quantify new and old RNA: Nucleoside analogs are incorporated into newly transcribed RNA and are made detectable as point mutations in mapped reads. However, relatively infrequent incorporation events and significant sequencing error rates make the differentiation between old and new RNA a highly challenging task. We developed a statistical approach termed GRAND-SLAM that, for the first time, allows to estimate the proportion of old and new RNA in such an experiment. Uncertainty in the estimates is quantified in a Bayesian framework. Simulation experiments show our approach to be unbiased and highly accurate. Furthermore, we analyze how uncertainty in the proportion translates into uncertainty in estimating RNA half-lives and give guidelines for planning experiments. Finally, we demonstrate that our estimates of RNA half-lives compare favorably to other experimental approaches and that biological processes affecting RNA half-lives can be investigated with greater power than offered by any other method. GRAND-SLAM is freely available for non-commercial use at http://software.erhard-lab.de; R scripts to generate all figures are available at zenodo (doi: 10.5281/zenodo.1162340).

MOTIVATION: RIP-chip is a high-throughput method to identify mRNAs that are targeted by RNA-binding proteins. The protein of interest is immunoprecipitated, and the identity and relative amount of mRNA associated with it is measured on microarrays. Even if a variety of methods is available to analyse microarray data, e.g. to detect differentially regulated genes, the additional experimental steps in RIP-chip require specialized methods. Here, we focus on two aspects of RIP-chip data: First, the efficiency of the immunoprecipitation step performed in the RIP-chip protocol varies in between different experiments introducing bias not existing in standard microarray experiments. This requires an additional normalization step to compare different samples and even technical replicates. Second, in contrast to standard differential gene expression experiments, the distribution of measurements is not normal. We exploit this fact to define a set of biologically relevant genes in a statistically meaningful way. RESULTS: Here, we propose two methods to analyse RIP-chip data: We model the measurement distribution as a gaussian mixture distribution, which allows us to compute false discovery rates (FDRs) for any cut-off. Thus, cut-offs can be chosen for any desired FDR. Furthermore, we use principal component analysis to determine the normalization factors necessary to remove immunoprecipitation bias. Both methods are evaluated on a large RIP-chip dataset measuring targets of Ago2, the major component of the microRNA guided RNA-induced silencing complex (RISC). Using published HITS-CLIP experiments performed with the same cell line as used for RIP-chip, we show that the mixture modelling approach is a necessary step to remove background, which computed FDRs are valid, and that the additional normalization is a necessary step to make experiments comparable. AVAILABILITY: An R implementation of REA is available on the project website (http://www.bio.ifi.lmu.de/REA) and as supplementary data file.

UNLABELLED: Recent improvements in experimental technologies now allow measurements of de novo transcription and/or RNA decay at whole transcriptome level and determination of precise transcript half-lives. Such transcript half-lives provide important insights into the regulation of biological processes and the relative contributions of RNA decay and de novo transcription to differential gene expression. In this article, we present HALO (Half-life Organizer), the first software for the precise determination of transcript half-lives from measurements of RNA de novo transcription or decay determined with microarrays or RNA-seq. In addition, methods for quality control, filtering and normalization are supplied. HALO provides a graphical user interface, command-line tools and a well-documented Java application programming interface (API). Thus, it can be used both by biologists to determine transcript half-lives fast and reliably with the provided user interfaces as well as software developers integrating transcript half-life analysis into other gene expression profiling pipelines. AVAILABILITY: Source code, executables and documentation are available at http://www.bio.ifi.lmu.de/software/halo.