DR Matthew Ritchie

DR Matthew Ritchie


  • Methods development and analysis of genomic data



  • www.wehi.edu.au/people/matthew-ritchie

    Dr. Ritchie is a laboratory head in the Molecular Medicine Division at the Walter and Eliza Hall Institute of Medical Research (mritchie@wehi.edu.au).

    Research Overview

    Microarray and sequencing technologies provide a high resolution and cost-effective way of surveying complex biological problems, such as the variations in the genome in health and disease, or analysing the entire set of genes expressed in a particular cell type or tissue.

    Our research focuses on the development of analysis methods tailored to new applications of genomic technology in biomedical research.

    Our novel algorithms for analysing high-throughput data are implemented in open-source software that is freely available to researchers worldwide via the Bioconductor project.

    We also collaborate with other research groups at the institute to analyse genomic data that provides new insights into the genetic regulation of blood development.

    Research Interests

    - Statistical methods for RNA-seq data analysis.
    - Analysis methods for Illumina genotyping array data.
    - Measuring allele-specific expression using SNP arrays and sequencing.
    - Analysis of high-throughput sequencing data from shRNA screens.
    - Exploring gene regulation in blood cell production using genomic technologies.   


Selected publications


Education and training

  • PhD, University of Melbourne


Available for supervision

  • Y

Supervision Statement

  • Research interest

    Our time is divided evenly between methodological work and primary data analysis of both in-house experiments from our collaborators and public datasets to provide new insights in gene regulation in health and disease.

    We develop statistical methods that are tailored to solve problems in genomic analysis in medical research. We create open-source software tools in R / Bioconductor to handle data from a wide range of applications.

    Current projects include:
    - Statistical methods for modeling variation in RNA-sequencing data.
    - Software for analysing data from shRNA and CRISPR/Cas9 genetic screens to uncover gene function.
    - Improved genotyping algorithms for Illumina’s SNP BeadArrays used in disease studies.
    - Methods to explore epigenetic and genetic regulation in haematopoiesis and cancers.