Genome Spot

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.

I get asked a lot about the best ways to store sequence data because the files are massive and researchers have various levels of  knowledge of the hardware and software. Here I'll run through some best practices for genomics research data management based on my 10 years of experience in the space. 1. Always work on servers, not remote machines or laptops On-prem machines and cloud servers are preferred because you can log into the from anywhere using ssh or other protocol. These machines are better suited to heavy loads and are less likely to breakdown because of the institutional tech support and maintenance. Institutional data transfer speeds will be far superior to your home network. Never do computational work on a laptop. Avoid storing data on your own portable hard drives or flash drives. If you don't have a server, ask for access at your institution or research cloud provider (we use Nectar in Australia). 2. Download the data to the place where you will be working on i