We propose an extension to quantile normalization that removes unwanted technical variation using control probes. We adapt our algorithm, functional normalization, to the Illumina 450k methylation array and address the open problem of normalizing methylation data with global epigenetic changes, such as human cancers. Using data sets from The Cancer Genome Atlas and a large case–control study, we show that our algorithm outperforms all existing normalization methods with respect to replication of results between experiments, and yields robust results even in the presence of batch effects. Functional normalization can be applied to any microarray platform, provided suitable control probes are available.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0503-2) contains supplementary material, which is available to authorized users.

SummaryThe minfi package is widely used for analyzing Illumina DNA methylation array data. Here we describe modifications to the minfi package required to support the HumanMethylationEPIC (‘EPIC’) array from Illumina. We discuss methods for the joint analysis and normalization of data from the HumanMethylation450 (‘450k’) and EPIC platforms. We introduce the single-sample Noob (ssNoob) method, a normalization procedure suitable for incremental preprocessing of individual methylation arrays and conclude that this method should be used when integrating data from multiple generations of Infinium methylation arrays. We show how to use reference 450k datasets to estimate cell type composition of samples on EPIC arrays. The cumulative effect of these updates is to ensure that minfi provides the tools to best integrate existing and forthcoming Illumina methylation array data.Availability and ImplementationThe minfi package version 1.19.12 or higher is available for all platforms from the Bioconductor project.Supplementary information Supplementary data are available at Bioinformatics online.

Analysis of Hi-C data has shown that the genome can be divided into two compartments called A/B compartments. These compartments are cell-type specific and are associated with open and closed chromatin. We show that A/B compartments can reliably be estimated using epigenetic data from several different platforms: the Illumina 450 k DNA methylation microarray, DNase hypersensitivity sequencing, single-cell ATAC sequencing and single-cell whole-genome bisulfite sequencing. We do this by exploiting that the structure of long-range correlations differs between open and closed compartments. This work makes A/B compartment assignment readily available in a wide variety of cell types, including many human cancers.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0741-y) contains supplementary material, which is available to authorized users.

We present shinyMethyl, a Bioconductor package for interactive quality control of DNA methylation data from Illumina 450k arrays. The package summarizes 450k experiments into small exportable R objects from which an interactive interface is launched. Reactive plots allow fast and intuitive quality control assessment of the samples. In addition, exploration of the phenotypic associations is possible through coloring and principal component analysis. Altogether, the package makes it easy to perform quality assessment of large-scale methylation datasets, such as epigenome-wide association studies or the datasets available through The Cancer Genome Atlas portal. The shinyMethyl package is implemented in R and available via Bioconductor. Its development repository is at https://github.com/jfortin1/shinyMethyl.

Recent data demonstrated that activation of the muscarinic M receptor by a subtype-selective positive allosteric modulator (PAM) contributes to the gastrointestinal (GI) and cardiovascular (CV) cholinergic adverse events (AEs) previously attributed to M and M activation. These studies were conducted using PAMs that also exhibited allosteric agonist activity, leaving open the possibility that direct activation by allosteric agonism, rather than allosteric modulation, could be responsible for the adverse effects. This article describes the design and synthesis of lactam-derived M PAMs that address this hypothesis. The lead molecule from this series, compound 1 (PF-06827443), is a potent, low-clearance, orally bioavailable, and CNS-penetrant M-selective PAM with minimal agonist activity. Compound 1 was tested in dose escalation studies in rats and dogs and was found to induce cholinergic AEs and convulsion at therapeutic indices similar to previous compounds with more agonist activity. These findings provide preliminary evidence that positive allosteric modulation of M is sufficient to elicit cholinergic AEs.

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.

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