Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.gene expression | gene transcription | RNA processing | Gaussian process inference | RNA splicing I nduction of transcription through extracellular signaling can yield rapid changes in gene expression for many genes. Establishing the timing of events during this process is important for understanding the rate-limiting mechanisms regulating the response and vital for inferring causality of regulatory events. Several processes influence the patterns of mRNA abundance observed in the cell, including the kinetics of transcriptional initiation, elongation, splicing, and mRNA degradation. It was recently demonstrated that significant delays attributable to the kinetics of splicing can be an important factor in a focused study of genes induced by tumor necrosis factor (TNF-α) (1). Delayed transcription can play an important functional role in the cell, for example, inducing oscillations within negative feedback loops (2) or facilitating "justin-time" transcriptional programs with optimal efficiency (3). It is therefore important to identify such delays and to better understand how they are regulated. In this study, we combine RNA polymerase (pol-II) ChIP-Seq data with RNA-Seq data to study transcription kinetics of estrogen receptor (ER) signaling in breast cancer cells. Using an unbiased genome-wide modeling approach, we find evidence for large delays in mRNA production in 11% of the genes with a quantifiable signal in our data. A statistical analysis of gene...

Motivation: Recent advances in high-throughput sequencing (HTS) have made it possible to monitor genomes in great detail. New experiments not only use HTS to measure genomic features at one time point but also monitor them changing over time with the aim of identifying significant changes in their abundance. In population genetics, for example, allele frequencies are monitored over time to detect significant frequency changes that indicate selection pressures. Previous attempts at analyzing data from HTS experiments have been limited as they could not simultaneously include data at intermediate time points, replicate experiments and sources of uncertainty specific to HTS such as sequencing depth.Results: We present the beta-binomial Gaussian process model for ranking features with significant non-random variation in abundance over time. The features are assumed to represent proportions, such as proportion of an alternative allele in a population. We use the beta-binomial model to capture the uncertainty arising from finite sequencing depth and combine it with a Gaussian process model over the time series. In simulations that mimic the features of experimental evolution data, the proposed method clearly outperforms classical testing in average precision of finding selected alleles. We also present simulations exploring different experimental design choices and results on real data from Drosophila experimental evolution experiment in temperature adaptation.Availability and implementation: R software implementing the test is available at https://github.com/handetopa/BBGP.Contact: hande.topa@aalto.fi, agnes.jonas@vetmeduni.ac.at, carolin.kosiol@vetmeduni.ac.at, antti.honkela@hiit.fiSupplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Alternative splicing is an important mechanism in which the regions of pre-mRNAs are differentially joined in order to form different transcript isoforms. Alternative splicing is involved in the regulation of normal physiological functions but also linked to the development of diseases such as cancer. We analyse differential expression and splicing using RNA-sequencing time series in three different settings: overall gene expression levels, absolute transcript expression levels and relative transcript expression levels.Results: Using estrogen receptor α signaling response as a model system, our Gaussian process-based test identifies genes with differential splicing and/or differentially expressed transcripts. We discover genes with consistent changes in alternative splicing independent of changes in absolute expression and genes where some transcripts change whereas others stay constant in absolute level. The results suggest classes of genes with different modes of alternative splicing regulation during the experiment.Availability and Implementation: R and Matlab codes implementing the method are available at https://github.com/PROBIC/diffsplicing. An interactive browser for viewing all model fits is available at http://users.ics.aalto.fi/hande/splicingGP/Contact: hande.topa@helsinki.fi or antti.honkela@helsinki.fiSupplementary information: Supplementary data are available at Bioinformatics online.