Exploring variation in gene and transcript expression across tissues

• Masters by Coursework
• PhD

Long-read sequencing provides researchers with a powerful tool for studying transcriptome architecture. This project will explore the complex splicing patterns that occur in tissue samples by combining single-cell and bulk RNA-seq with short- and long-read sequencing technology. The project will involve the analysis of cutting-edge data generated for protocol benchmarking and experiments profiling healthy and diseased tissue. The work will lead to the development of free, open-source software distributed to researchers worldwide via the Bioconductor project. We are looking for a student with a strong statistical or computational background (e.g. an undergraduate degree in statistics and mathematics, computer science or masters in bioinformatics), programming skills, an interest in biology and a desire to develop their skills in genomic data science.

The Ritchie laboratory develops analysis methods and open-source software (freely available as part of the Bioconductor project) that are tailored to new applications of genomic technology in biomedical research. Our time is divided evenly between methodological work and primary data analysis of in-house experiments from our collaborators and public datasets to provide new insights into gene regulation in health and disease.

Our major interests include:
– statistical methods for modelling variation in RNA-sequencing data
– software for interactive visualisation of gene expression data
– software for the analysis of single-cell and long-read gene expression and methylation data
– applying our data analysis skills to study epigenetic and genetic regulation in development and cancer together with our collaborators.