Genome Spot

In 2020, Dr Antony Kaspi and I published a method called "mitch" [1] which is like GSEA, but was specifically designed for multi-contrast analysis, and based on rank-MANOVA statistics inspired by a 2012 paper by Cox and Mann. Mitch worked well for various types of omics data downstream of commonly used differential abundance tools like DESeq2, edgeR, DiffBind, etc, but we didn't consider at the time how mitch could be applied to microarray data.  You might think that microarrays are outdated, but they are still used extensively for epigenome-wide association studies (EWASs), which are frequently used to understand disease processes and to identify biomarkers of disease. To demonstrate, there are 1536 publicly available methylation array studies on NCBI GEO, and probably many more thousands that are restricted access. The tools available for pathway analysis of methylation array data are a bit limited. There's an over-representation method that take into consideration ...

Performing a differential methylation analysis of infinium array data requires an impressively large number of R packages, such as `minfi`, `missmethyl`, `limma`, `genomicRanges`, `DMRcate`, `bunpHunter` and many others. Each of these in turn are considered heavy packages as they each require many dependancies. This means it can take up to an hour to go from a vanilla R installation to one with all the needed packages installed. If you are using multiple computers you might find that these have slightly different versions of R, bioconductor and this large stack of dependancies, which could lead to different results. You may also find that it is difficult to install this large set of dependancies on shared systems, as some dependenncies might require installation of system libraries that need admin permissions to install. The way I've tried to alleviate this problem is to install all my needed packages into a Docker image which can then be downloaded and run in a few minutes on a ne...

Presented 18th February 2021 Abstract Gene expression is governed by numerous chromatin modifications. Understanding these dynamics is critical to understanding human health and disease, but there are few software options for researchers looking to integrate multi-omics data at the level of pathways. To address this, we developed mitch, an R package for multi-contrast gene set enrichment analysis. It uses a rank-MANOVA statistical approach to identify sets of genes that exhibit joint enrichment across multiple contrasts. In this talk I will demonstrate using mitch and showcase its advanced visualisation features to explore the regulation of signaling and biochemical pathways at the chromatin level.

Today I gave a presentation on experiment design and use of ChIP-seq and MBD-seq to understand gene regulation. The target audience consisted of biomedical scientists with little background in genomics but were curious to incorporate deep sequencing into their studies. Link to the slides HERE . As always I love getting feedback - so leave your questions and comments below!

Bismark is currently the de facto standard for primary analysis of high throughput bisulfite sequencing data. Bismark can align the reads to the genome and perform methylation calling. In this post, I'll go through Illumina whole genome bisulfite sequence (WGBS) alignment and methylation calling using Bismark. First I want to mention that this post is just a summary, not meant to be a user manual or thorough troubleshooting guide. Fortunately, Bismark has some of the best documentation for any bioinformatics suite and is mandatory reading. The Bismark crew are very proactive with responding to user queries on various forums as well. First step in getting Bismark to work is to index the genome, in this case with Bowtie2: bismark_genome_preparation --bowtie2 /pathto/refgenome/ Conventionally, multiplexed libraries will be sequenced over a number of lanes. Resist concatenating or merging the smaller fastq files for each patient/sample until after the alignment, as the c...

Gene regulation is a really complicated thing. We have covalent marks to DNA, histones and transcription factors. Chromatin remodeling and long range enhancer interactions. Enhancer elements located in introns of genes hundreds of kilobases away from the gene they're controlling. Transcriptional control from microRNA networks and now there is an emerging model for the function of some of the thousands of long non-coding RNAs which are just now being uncovered with high resolution (directional) transcriptome analysis. Many of you which studied molecular biology at Uni would (should) remember the model for how X chromosome  inactivation  is achieved. The mechanism centers around XIST , one of the first non-coding RNA genes identified. Expression of XIST from the inactive X chromosome essentially wraps it up at the same time that repressive epigenetic marks are established through its interaction with the Polycomb Repressive Complex 2 (PRC2). Sounds simple en...

There have been a number of high profile profile articles in recent times discussing the function of TET proteins, mostly in the conversion of methylated cytosine (5mC) into hydroxymethylated cytosine (5hmC), the 5th base. Hydroxymethylcytosine is much rarer than methylcytosine and is thought to be an intermediate towards demethylation of cytosine, a mechanism which remains incompletely resolved. A paper last year showed that TET proteins also convert 5hmc to 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC), termed the 6th and 7th bases. OGT on the other hand is a fairly unique protein because it is the only known known O-GlcNAc transferase in mammals. What is GlcNAc you say? It stands for N-acetylglucosamine, a hexosamine. There has been a series of papers ( here , here , here ) discussing OGT as a nutrient sensor, transferring GlcNAc during period of surplus nutrient supply. GlcNAc can be transferred to the same amino acids as phosphorylation, so there is a suggested cross...

Its no secret that repeat DNA makes up the majority of the genome in a majority of "higher" eukaryotes. For a long time this repeat DNA has been considered "selfish" or "parasitic" DNA, its only feature was its prolific self-propagation as it hitchhiked a ride throughout evolutionary history at the energetic expense of the host. Nina Fedoroff argues in a recent review in Science that these transposable elements are not "junk" at all, and in fact they play "a profoundly generative role in genome evolution" where the transposons provide novel mechanisms to generate genetic diversity. Nina explores the relationship between genome size and epigenetic complexity in the comparison of eukaryotes and prokaryotes, postulating that the innovation of epigenetic elaborate control mechanisms in bacteria paved the way for increases in genome size and complexity we see in eukaryotes. So rather than epigenetic mechanisms evolving to keep "parasi...