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About

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.

My skillset includes; Bioinformatics, gene expression profiling, Applied statistics, R programming, sequencing, genomics, computational biology, genetics, systems biology.

Publications

Selected publications from Prof Matthew Ritchie

Zeglinski K, Schuster J, D J, Adair A, Deng J, Pymm P, Ritchie ME, Bowden R, Tham W-H, Gouil Q. Alpseq: an open-source workflow to turbocharge nanobody discovery with high-throughput sequencing. mAbs. 2026;18(1):10.1080/19420862.2026.2623326

2026;

Cao WHJ, You Y, Wang N, Chaudhry MZ, Yu H, Bell PT, Noye EC, Denman R, Lee B, Waddington A, Ye J, Schreuder J, Huang Q, Tellier J, Curio S, Santiago J, Amann-Zalcenstein D, Jacquelot N, Hickey P, Nutt SL, Seillet C, Hansbro PM, Wimmer VC, Strugnell RA, Tuong ZK, Richie ME, Belz GT. Peyer’s patch M cells organize an epithelial niche that sustains group 3 innate lymphoid cells and IL-22. Nature Immunology. 2026;:10.1038/s41590-026-02606-3

Chua NK, González-Robles TJ, Reddington CJ, Dudley-Fraser J, Birkinshaw RW, Han J, Solano A, Wong SW, Kochańczyk T, Peter JJ, Nakasone MA, Aust F, Munro J, Tong YH, Iskander J, Abeysekera W, Garnham A, Huckstep H, Ritchie ME, Wertz I, Hymowitz S, Kumar S, Conaway RC, Privé GG, Bullock AN, Babon JJ, Klevit RE, Lorenz S, Ciulli A, Fischer ES, Thomä NH, Nowak RP, Schulman BA, Rapé M, Rittinger K, Pagan JK, Bahlo M, Mackay JP, Mace PD, Lima CD, Hay RT, Komander D, Lechtenberg BC, Joazeiro CAP, Pagano M, Hofmann K, Feltham R. The E3-ome gene-centric compendium reveals the human E3 ligase landscape. Cell. 2026;189(7):10.1016/j.cell.2026.01.029

Xiao LC, Semwal A, St John B, Zeglinski K, Su S, Lancaster J, Xue S, Reversade B, Ritchie ME, Magdinier F, Blewitt ME, Gouil Q. Complete genetic and epigenetic architecture of D4Z4 macrosatellites in FSHD, BAMS, and reference cohorts with D4Z4End2End. Genome Research. 2026;36(4):10.1101/gr.280907.125

Yan F, Baldoni PL, Lancaster J, Ritchie ME, Lewsey MG, Gouil Q, Davidson NM. A comprehensive evaluation of long-read de novo transcriptome assembly. Genome Biology. 2026;27(1):10.1186/s13059-026-04001-5

Xu Y, Sargeant CJ, You Y, You Y, Su S, Wang C, Tian L, Chen Y, Ritchie ME. stPipe: a flexible and streamlined R/Bioconductor pipeline for preprocessing sequencing-based spatial transcriptomics data. NAR Genomics and Bioinformatics. 2025;7(4):10.1093/nargab/lqaf167

O’Keeffe P, Nouri Y, Saw HS, Moore Z, Baldwin TM, Olechnowicz SWZ, Jabbari JS, Squire DM, Leslie S, Wang C, You Y, Ritchie ME, Cross RS, Jenkins MR, Audiger C, Naik SH, Whittle JR, Freytag S, Best SA, Hickey PF, Amann-Zalcenstein D, Bowden R, Brown DV. TIRE-seq simplifies transcriptomics via integrated RNA capture and library preparation. Scientific Reports. 2025;15(1):10.1038/s41598-025-98282-8

Peng H, Jabbari JS, Tian L, Wang C, You Y, Chua CC, Anstee NS, Amin N, Wei AH, Davidson N, Roberts AW, Huang D, Ritchie ME, Thijssen R. Single-cell Rapid Capture Hybridization sequencing to reliably detect isoform usage and coding mutations in targeted genes. Genome Research. 2025;35(4):10.1101/gr.279322.124

Du MRM, Wang C, Law CW, Amann-Zalcenstein D, Anttila CJA, Ling L, Hickey PF, Sargeant CJ, Chen Y, Ioannidis LJ, Rajasekhar P, Yip RKH, Rogers KL, Hansen DS, Bowden R, Ritchie ME. Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset. Genome Biology. 2025;26(1):10.1186/s13059-025-03543-4

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