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Motivation A synoptic view of the human genome benefits chiefly from the application of nucleic acid sequencing and microarray technologies. These platforms allow interrogation of patterns such as gene expression and DNA methylation at the vast majority of canonical loci, allowing granular insights and opportunities for validation of original findings. However, problems arise when validating against a “gold standard” measurement, since this immediately biases all subsequent measurements towards that particular technology or protocol. Since all genomic measurements are estimates, in the absence of a ”gold standard” we instead empirically assess the measurement precision and sensitivity of a large suite of genomic technologies via a consensus modelling method called the row-linear model. This method is an application of the American Society for Testing and Materials Standard E691 for assessing interlaboratory precision and sources of variability across multiple testing sites. Both cross-platform and cross-locus comparisons can be made across all common loci, allowing identification of technology- and locus-specific tendencies. Results We assess technologies including the Infinium MethylationEPIC BeadChip, whole genome bisulfite sequencing (WGBS), two different RNA-Seq protocols (PolyA+ and Ribo-Zero) and five different gene expression array platforms. Each technology thus is characterised herein, relative to the consensus. We showcase a number of applications of the row-linear model, including correlation with known interfering traits. We demonstrate a clear effect of cross-hybridisation on the sensitivity of Infinium methylation arrays. Additionally, we perform a true interlaboratory test on a set of samples interrogated on the same platform across twenty-one separate testing laboratories. Availability and implementation A full implementation of the row-linear model, plus extra functions for visualisation, are found in the R package consensus at https://github.com/timpeters82/consensus. Supplementary information Supplementary data are available at Bioinformatics online.

Cancer is characterised by DNA hypermethylation and gene silencing of CpG island-associated promoters, including tumour-suppressor genes. The methyl-CpG-binding domain (MBD) family of proteins bind to methylated DNA and can aid in the mediation of gene silencing through interaction with histone deacetylases and histone methyltransferases. However, the mechanisms responsible for eliciting CpG island hypermethylation in cancer, and the potential role that MBD proteins play in modulation of the methylome remain unclear. Our previous work demonstrated that MBD2 preferentially binds to the hypermethylated GSTP1 promoter CpG island in prostate cancer cells. Here, we use functional genetic approaches to investigate if MBD2 plays an active role in reshaping the DNA methylation landscape at this locus and genome-wide. First, we show that loss of MBD2 results in inhibition of both maintenance and spread of de novo methylation of a transfected construct containing the GSTP1 promoter CpG island in prostate cancer cells and Mbd2−/− mouse fibroblasts. De novo methylation was rescued by transient expression of Mbd2 in Mbd2−/− cells. Second, we show that MBD2 depletion triggers significant hypomethylation genome-wide in prostate cancer cells with concomitant loss of MBD2 binding at promoter and enhancer regulatory regions. Finally, CpG islands and shores that become hypomethylated after MBD2 depletion in LNCaP cancer cells show significant hypermethylation in clinical prostate cancer samples, highlighting a potential active role of MBD2 in promoting cancer-specific hypermethylation. Importantly, co-immunoprecipiation of MBD2 shows that MBD2 associates with DNA methyltransferase enzymes 1 and 3A. Together our results demonstrate that MBD2 has a critical role in ‘rewriting’ the cancer methylome at specific regulatory regions.

Our knowledge of regulatory mechanisms of gene expression and other chromosomal processes related to DNA methylation and chromatin state is continuing to grow at a rapid pace. Understanding how these epigenomic phenomena vary between individuals will have an impact on understanding their broader role in determining variation in gene expression and biochemical, physiological, and behavioural phenotypes. In this review we survey recent progress in this area, focusing on data available from humans. We highlight the role of obligatory (sequence-dependent) epigenomic variation as an important mechanism for generating interindividual variation that could impact our understanding of the mechanistic basis of complex trait architecture.

Summary: The initial steps in the analysis of next-generation sequencing data can be automated by way of software ‘pipelines’. However, individual components depreciate rapidly because of the evolving technology and analysis methods, often rendering entire versions of production informatics pipelines obsolete. Constructing pipelines from Linux bash commands enables the use of hot swappable modular components as opposed to the more rigid program call wrapping by higher level languages, as implemented in comparable published pipelining systems. Here we present Next Generation Sequencing ANalysis for Enterprises (NGSANE), a Linux-based, high-performance-computing-enabled framework that minimizes overhead for set up and processing of new projects, yet maintains full flexibility of custom scripting when processing raw sequence data. Availability and implementation: Ngsane is implemented in bash and publicly available under BSD (3-Clause) licence via GitHub at https://github.com/BauerLab/ngsane. Contact: Denis.Bauer@csiro.au Supplementary information: Supplementary data are available at Bioinformatics online.