Author

Matthew R. Goddard

University of AucklandORCID 0000-0002-7482-4438Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Research Focus:gene regulatory network inference· 1 Organism:mouse· 1 Organism:plant (other)· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Alanna CallendrelloiD· 1 shared Alex GreyiD· 1 shared Andrew D. ArmitageiD· 1 shared Bradford P. TayloriD· 1 shared Hannah M. ReadiD· 1 shared Helen M. CockertoniD· 1 shared Hemanoel Passarelli‐AraujoiD· 1 shared Madikay SenghoreiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperGenomics

Alanna CallendrelloORCID

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2 papers

Recent·Most cited

Dynamic gene gain and loss during the divergence of obligate biotrophic powdery mildew pathogens

Thomas Heaven, Helen M. Cockerton, Xiangming Xu, Matthew R. Goddard, Andrew D. Armitage

This study sequenced genomes of powdery mildew fungi to compare effector complements and gene loss across species, revealing divergent patterns in gene expansion and contraction.

bioRxiv · Jun 6, 2026 · PreprintPlant Biology

Beyond consensus sequence: a quantitative scheme for inferring transmission using deep sequencing in a bacterial transmission model

Madikay Senghore, Hannah M. Read, Priyali Oza, Sarah Johnson, Hemanoel Passarelli‐Araujo, Bradford P. Taylor, Stephen Ashley, Alex Grey, Alanna Callendrello, Robyn S. Lee, Matthew R. Goddard, Thomas Lumley, William Paul Hanage, Siouxsie Wiles

The paper develops a model to infer bacterial transmission using deep sequencing data from a mouse model, showing that within-host variant abundance differences can accurately identify transmission pairs.

bioRxiv · Oct 17, 2022 · PreprintGenomics