SUMMARY: Epigenome-Wide Association Study (EWAS) has become a powerful approach to identify epigenetic variations associated with diseases or health traits. Sex is an important variable to include in EWAS to ensure unbiased data processing and statistical analysis. We introduce the R-package EWASex, which allows for fast and highly accurate sex-estimation using DNA methylation data on a small set of CpG sites located on the X-chromosome under stable X-chromosome inactivation in females. RESULTS: We demonstrate that EWASex outperforms the current state of the art tools by using different EWAS datasets. With EWASex, we offer an efficient way to predict and to verify sex that can be easily implemented in any EWAS using blood samples or even other tissue types. It comes with pre-trained weights to work without prior sex labels and without requiring access to RAW data, which is a necessity for all currently available methods. AVAILABILITY AND IMPLEMENTATION: The EWASex R-package along with tutorials, documentation and source code are available at https://github.com/Silver-Hawk/EWASex. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Many studies have investigated the association between DNA methylation alterations and disease occurrences using two design paradigms, traditional case-control and disease-discordant twins. In the disease-discordant twin design, the affected twin serves as the case and the unaffected twin serves as the control. Theoretically the twin design takes advantage of controlling for the shared genetic make-up, but it is still highly debatable if and how much researchers may benefit from such a design over the traditional case-control design. Results: In this study, we investigate and compare the power of both designs with simulations. A liability threshold model was used assuming that identical twins share the same genetic contribution with respect to the liability of complex human diseases. Varying ranges of parameters have been used to ensure that the simulation is close to real-world scenarios. Our results reveal that the disease-discordant twin design implies greater statistical power over the traditional case-control design. For diseases with moderate and high heritability (>0.3), the disease-discordant twin design allows for large sample size reductions compared to the ordinary case-control design. Our simulation results indicate that the discordant twin design is indeed a powerful tool for epigenetic association studies. Availability and implementation: Computer scripts are available at https://github.com/zickyls/EWAS-Twin-Simulation. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Epigenome-wide association studies (EWAS) generate big epidemiological datasets. They aim for detecting differentially methylated DNA regions that are likely to influence transcriptional gene activity and, thus, the regulation of metabolic processes. The by far most widely used technology is the Illumina Methylation BeadChip, which measures the methylation levels of 450 (850) thousand cytosines, in the CpG dinucleotide context in a set of patients compared to a control group. Many bioinformatics tools exist for raw data analysis. However, most of them require some knowledge in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes. Results: Here, we present DiMmeR (Discovery of Multiple Differentially Methylated Regions), the first free standalone software that interactively guides with a user-friendly graphical user interface (GUI) scientists the whole way through EWAS data analysis. It offers parallelized statistical methods for efficiently identifying DMRs in both Illumina 450K and 850K EPIC chip data. DiMmeR computes empirical P -values through randomization tests, even for big datasets of hundreds of patients and thousands of permutations within a few minutes on a standard desktop PC. It is independent of any third-party libraries, computes regression coefficients, P -values and empirical P -values, and it corrects for multiple testing. Availability and Implementation: DiMmeR is publicly available at http://dimmer.compbio.sdu.dk . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Reverse-phase protein arrays (RPPAs) allow sensitive quantification of relative protein abundance in thousands of samples in parallel. Typical challenges involved in this technology are antibody selection, sample preparation and optimization of staining conditions. The issue of combining effective sample management and data analysis, however, has been widely neglected. RESULTS: This motivated us to develop MIRACLE, a comprehensive and user-friendly web application bridging the gap between spotting and array analysis by conveniently keeping track of sample information. Data processing includes correction of staining bias, estimation of protein concentration from response curves, normalization for total protein amount per sample and statistical evaluation. Established analysis methods have been integrated with MIRACLE, offering experimental scientists an end-to-end solution for sample management and for carrying out data analysis. In addition, experienced users have the possibility to export data to R for more complex analyses. MIRACLE thus has the potential to further spread utilization of RPPAs as an emerging technology for high-throughput protein analysis. AVAILABILITY: Project URL: http://www.nanocan.org/miracle/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The microarray-based gene expression time-course experiments represent an important research design in biomedical studies. Current methods for analyzing microarray time-course data ignore individual differences in treatment responses or time-course patterns and have difficulties in handling missing values. We introduce the latent growth curve models popular in use in longitudinal epidemiology studies to analyze microarray time-course data with incomplete observations. The models have been applied to human in vivo irritated epidermis data with missing observations to investigate time-dependent global transcriptional responses to a chemical irritant. Our strategic data analysis identified significant time-course genes and selected the best fitting model for each of the genes on the array without pre-defining any parametric form. Various time-course patterns are then recognized from the significant genes using clustering methods including hierarchical clustering and self-organising map. Very high correlation between baseline expression and time-course response has been detected for many of the significant genes. The latent linear growth curve model can be applied to microarray-based gene expression time-course data with incomplete observations to find genes exhibiting various time-course responses. This method also estimates covariance between baseline gene activity and time-course response, providing useful additional information ignored in current gene expression analysis.