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Summary: DNA methylation patterns in a cell are associated with gene expression and the phenotype of a cell, including disease states. Bisulphite PCR sequencing is commonly used to assess the methylation profile of genomic regions between different cells. Here we have developed MethPanel, a computational pipeline with an interactive graphical interface to rapidly analyse multiplex bisulphite PCR sequencing data. MethPanel comprises a complete analysis workflow from genomic alignment to DNA methylation calling and supports an unlimited number of PCR amplicons and input samples. MethPanel offers important and unique features, such as calculation of an epipolymorphism score and bisulphite PCR bias correction capabilities, and is designed so that the methylation data from all samples can be processed in parallel. The outputs are automatically forwarded to a shinyApp for convenient display, visualization and remotely sharing data with collaborators and clinicians., Availabilityand Implementation: MethPanel is freely available at https://github.com/thinhong/MethPanel., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

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., (© The Author(s) 2018. Published by Oxford University Press.)

Summary: Epigenetics, the study of heritable somatic phenotypic changes not related to DNA sequence, has emerged as a critical component of the landscape of gene regulation. The epigenetic layers, such as DNA methylation, histone modifications and nuclear architecture are now being extensively studied in many cell types and disease settings. Few software tools exist to summarize and interpret these datasets. We have created a toolbox of procedures to interrogate and visualize epigenomic data (both array- and sequencing-based) and make available a software package for the cross-platform R language., Availability: The package is freely available under LGPL from the R-Forge web site (http://repitools.r-forge.r-project.org/), Contact: mrobinson@wehi.edu.au.

Methylation analysis of individual cytosines in genomic DNA can be determined quantitatively by bisulphite treatment and PCR amplification of the target DNA sequence, followed by restriction enzyme digestion or sequencing. Methylated and unmethylated molecules, however, have different sequences after bisulphite conversion. For some sequences this can result in bias during the PCR amplification leading to an inaccurate estimate of methylation. PCR bias is sequence dependent and often strand-specific. This study presents a simple method for detection and measurement of PCR bias for any set of primers, and investigates parameters for overcoming PCR bias.