Motivation: The number of microbial and metagenomic studies has increased drastically due to advancements in next-generation sequencing-based measurement techniques. Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data (sampling zeros). Accounting uncertainty in microbiome data is often challenging due to the difficulty of obtaining biological replicates. Additionally, the compositional nature of current amplicon and metagenomic data differs from many other biological data types adding another challenge to the data analysis. Results: To address these challenges in human microbiome research, we introduce a novel probabilistic approach to explicitly model overdispersion and sampling zeros by considering the temporal correlation between nearby time points using Gaussian Processes. The proposed Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA) shows superior modeling performance compared to commonly used Dirichlet-multinomial, multinomial and non-parametric regression models on real and synthetic data. We demonstrate that the nonreplicative nature of human gut microbiota studies can be partially overcome by our method with proper experimental design of dense temporal sampling. We also show that different modeling approaches have a strong impact on ecological interpretation of the data, such as stationarity, persistence and environmental noise models. Availability and implementation: A Stan implementation of the proposed method is available under MIT license at https://github.com/tare/GPMicrobiome. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: 5-methylcytosine (5mC) is a widely studied epigenetic modification of DNA. The ten-eleven translocation (TET) dioxygenases oxidize 5mC into oxidized methylcytosines (oxi-mCs): 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC). DNA methylation modifications have multiple functions. For example, 5mC is shown to be associated with diseases and oxi-mC species are reported to have a role in active DNA demethylation through 5mC oxidation and DNA repair, among others, but the detailed mechanisms are poorly understood. Bisulphite sequencing and its various derivatives can be used to gain information about all methylation modifications at single nucleotide resolution. Analysis of bisulphite based sequencing data is complicated due to the convoluted read-outs and experiment-specific variation in biochemistry. Moreover, statistical analysis is often complicated by various confounding effects. How to analyse 5mC and oxi-mC data sets with arbitrary and complex experimental designs is an open and important problem. RESULTS: We propose the first method to quantify oxi-mC species with arbitrary covariate structures from bisulphite based sequencing data. Our probabilistic modeling framework combines a previously proposed hierarchical generative model for oxi-mC-seq data and a general linear model component to account for confounding effects. We show that our method provides accurate methylation level estimates and accurate detection of differential methylation when compared with existing methods. Analysis of novel and published data gave insights into to the demethylation of the forkhead box P3 (Foxp3) locus during the induced T regulatory cell differentiation. We also demonstrate how our covariate model accurately predicts methylation levels of the Foxp3 locus. Collectively, LuxGLM method improves the analysis of DNA methylation modifications, particularly for oxi-mC species. AVAILABILITY AND IMPLEMENTATION: An implementation of the proposed method is available under MIT license at https://github.org/tare/LuxGLM/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Gene expression profiling using RNA-seq is a powerful technique for screening RNA species' landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed. RESULTS: In this study, we use RNA-seq to measure gene expression during the early human T helper 17 (Th17) cell differentiation and T-: cell activation (Th0). To quantify Th17-: specific gene expression dynamics, we present a novel statistical methodology, DyNB, for analyzing time-course RNA-seq data. We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data. To account for experiment-: specific biases in gene expression dynamics, such as differences in cell differentiation efficiencies, we propose a method to rescale the dynamics between replicated measurements. We develop an MCMC sampling method to make inference of differential expression dynamics between conditions. DyNB identifies several known and novel genes involved in Th17 differentiation. Analysis of differentiation efficiencies revealed consistent patterns in gene expression dynamics between different cultures. We use qRT-PCR to validate differential expression and differentiation efficiencies for selected genes. Comparison of the results with those obtained via traditional timepoint-: wise analysis shows that time-course analysis together with time rescaling between cultures identifies differentially expressed genes which would not otherwise be detected. AVAILABILITY: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/

MOTIVATION: Signaling networks mediate responses to different stimuli using a multitude of feed-forward, feedback and cross-talk mechanisms, and malfunctions in these mechanisms have an important role in various diseases. To understand a disease and to help discover novel therapeutic approaches, we have to reveal the molecular mechanisms underlying signal transduction and use that information to design targeted perturbations. RESULTS: We have pursued this direction by developing an efficient computational approach, Sorad, which can estimate the structure of signal transduction networks and the associated continuous signaling dynamics from phosphoprotein time-course measurements. Further, Sorad can identify experimental conditions that modulate the signaling toward a desired response. We have analyzed comprehensive phosphoprotein time-course data from a human hepatocellular liver carcinoma cell line and demonstrate here that Sorad provides more accurate predictions of phosphoprotein responses to given stimuli than previously presented methods and, importantly, that Sorad can estimate experimental conditions to achieve a desired signaling response. Because Sorad is data driven, it has a high potential to generate novel hypotheses for further research. Our analysis of the hepatocellular liver carcinoma data predict a regulatory connection where AKT activity is dependent on IKK in TGFα stimulated cells, which is supported by the original data but not included in the original model. AVAILABILITY: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed. RESULTS: To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies. CONCLUSIONS: These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.

MOTIVATION: Regulation of gene expression is fundamental to the operation of a cell. Revealing the structure and dynamics of a gene regulatory network (GRN) is of great interest and represents a considerably challenging computational problem. The GRN estimation problem is complicated by the fact that the number of gene expression measurements is typically extremely small when compared with the dimension of the biological system. Further, because the gene regulation process is intrinsically complex, commonly used parametric models can provide too simple description of the underlying phenomena and, thus, can be unreliable. In this article, we propose a novel methodology for the inference of GRNs from time-series and steady-state gene expression measurements. The presented framework is based on the use of Bayesian analysis with ordinary differential equations (ODEs) and non-parametric Gaussian process modeling for the transcriptional-level regulation. RESULTS: The performance of the proposed structure inference method is evaluated using a recently published in vivo dataset. By comparing the obtained results with those of existing ODE- and Bayesian-based inference methods we demonstrate that the proposed method provides more accurate network structure learning. The predictive capabilities of the method are examined by splitting the dataset into a training set and a test set and by predicting the test set based on the training set. AVAILABILITY: A MATLAB implementation of the method will be available from http://www.cs.tut.fi/~aijo2/gp upon publication.