Pathogen informatics and modelling
For a background to our work, the following may be of interest:
FitzJohn RG, Knock ES, Whittles LK, Perez-Guzman PN, Bhatia S, Guntoro F, et al.
Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate.
Wellcome Open Res. 2021;5: 288.
Knock ES, Whittles LK, Lees JA, Perez-Guzman PN, Verity R, FitzJohn RG, et al.
Key epidemiological drivers and impact of interventions in the 2020 SARS-CoV-2 epidemic in England.
Sci Transl Med. 2021.
Lees JA, Mai TT, Galardini M, Wheeler NE, Horsfield ST, Parkhill J, et al.
Improved Prediction of Bacterial Genotype-Phenotype Associations Using Interpretable Pangenome-Spanning Regressions.
MBio 2020; 11 (4), e01344-20
Lees JA, Harris SR, Tonkin-Hill G, Gladstone RA, Lo SW, Weiser JN, et al.
Fast and flexible bacterial genomic epidemiology with PopPUNK.
Genome Res. 2019;29: 304–316.
Lees JA, Ferwerda B, Kremer PHC, Wheeler NE, Serón MV, Croucher NJ, et al.
Joint sequencing of human and pathogen genomes reveals the genetics of pneumococcal meningitis.
Nat Commun. 2019;10: 2176.
Shen P, Lees JA, Bee GCW, Brown SP, Weiser JN.
Pneumococcal quorum sensing drives an asymmetric owner–intruder competitive strategy during carriage via the competence regulon.
Nature Microbiology. 2019;4: 198–208.
Lees JA, Galardini M, Bentley SD, Weiser JN, Corander J.
pyseer: a comprehensive tool for microbial pangenome-wide association studies.
Bioinformatics. 2018;34: 4310–4312.