MOTIVATION: Mistakes in linking a patient's biological samples with their phenotype data can confound RNA-Seq studies. The current method for avoiding such sample mixups is to test for inconsistencies between biological data and known phenotype data such as sex. However, in DNA studies a common QC step is to check for unexpected relatedness between samples. Here, we extend this method to RNA-Seq, which allows the detection of duplicated samples without relying on identifying inconsistencies with phenotype data. SUMMARY: We present RNASeq_similarity_matrix: an automated tool to generate a sequence similarity matrix from RNA-Seq data, which can be used to visually identify sample mix-ups. This is particularly useful when a study contains multiple samples from the same individual, but can also detect contamination in studies with only one sample per individual. AVAILABILITY: RNASeq_similarity_matrix has been made available as a documented GPL licensed Docker image on www.github.com/nicokist/RNASeq_similarity_matrix. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The datasets generated by DNA methylation analyses are getting bigger. With the release of the HumanMethylationEPIC micro-array and datasets containing thousands of samples, analyses of these large datasets using R are becoming impractical due to large memory requirements. As a result there is an increasing need for computationally efficient methodologies to perform meaningful analysis on high dimensional data. RESULTS: Here we introduce the bigmelon R package, which provides a memory efficient workflow that enables users to perform the complex, large scale analyses required in epigenome wide association studies (EWAS) without the need for large RAM. Building on top of the CoreArray Genomic Data Structure file format and libraries packaged in the gdsfmt package, we provide a practical workflow that facilitates the reading-in, preprocessing, quality control and statistical analysis of DNA methylation data.We demonstrate the capabilities of the bigmelon package using a large dataset consisting of 1193 human blood samples from the Understanding Society: UK Household Longitudinal Study, assayed on the EPIC micro-array platform. AVAILABILITY AND IMPLEMENTATION: The bigmelon package is available on Bioconductor (http://bioconductor.org/packages/bigmelon/). The Understanding Society dataset is available at https://www.understandingsociety.ac.uk/about/health/data upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: We present software to characterize and rank potential therapeutic (drug) targets with data from public databases and present it in a user-friendly format. By understanding potential obstacles to drug development through the gathering and understanding of this information, combined with robust approaches to target validation to generate therapeutic hypotheses, this approach may provide high quality targets, leading the process of drug development to become more efficient and cost-effective. AVAILABILITY AND IMPLEMENTATION: The information we gather on potential targets concerns small-molecule druggability (ligandability), suitability for large-molecule approaches (e.g. antibodies) or new modalities (e.g. antisense oligonucleotides, siRNA or PROTAC), feasibility (availability of resources such as assays and biological knowledge) and potential safety risks (adverse tissue-wise expression, deleterious phenotypes). This information can be termed 'tractability'. We provide visualization tools to understand its components. TractaViewer is available from https://github.com/NeilPearson-Lilly/TractaViewer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.