Associate Professor

Jessica Mar

Why bulk samples (still) matter for gene expression analysis.

For beginner bioinformaticians, you may be inclined to believe that all RNA-sequencing now revolves around single cells. While advances in single-cell next-generation sequencing technologies have created new opportunities for computational biology, it is useful to recognize that the equivalent data sets generated from bulk samples still have their place for bioinformatics and for understanding biology. This talk outlines some of the reasons why bioinformaticians still need to pay attention to bulk RNA-sequencing data sets and in particular, why beginners should learn how to analyze gene expression data at both bulk and single-cell resolution.

Workshop: Gene-expression analysis with RNA sequence data using R

Presented with Dr Atefeh Taherian Fard and Huiwen Zheng, Australian Institute for Bioengineering and Nanotechnology, The University Of Queensland

In this workshop, you will learn how to analyse and explore RNA-seq count data. This hands-on workshop will cover basic steps in gene expression data analysis, including quality assessment, normalisation, differential gene expression testing, pathway over-representation analysis and visualisation. By the end of this workshop, you will be able to utilise the analysis workflow for your own RNA-seq data

Keywords: R, RStudio, RNA-seq, differential expression; data visualisation, DESeq2 and pathway analysis

Requirements: Participants must bring their own laptop and make sure that it has the latest version of R and RStudio installed. Experience in using R and RStudio is desired but not required

Relevance: This workshop is relevant to anyone who is interested in learning how to present RNA-seq data in an informative and engaging way, or applying different statistical methods, to understand the data and interpret the result using R.