Nicole is a Data Scientist at the Center for Genomic Pathogen Surveillance. Her focus is on developing high-throughput computational methods for understanding adaptation in bacterial pathogens, and flagging high-risk strains. She combines a background in biochemistry and genetics with experience in statistics and machine learning to find new ways to gain insight from genome sequence data.
During my PhD I developed a bioinformatic method which weights mutations in protein coding genes based on their predicted impact on protein function. I then collaborated with a variety of groups to apply this method to the study of the genomic changes that occur during niche adaptation.
My current work is focussed on linking genotype to phenotype in bacterial pathogens using comparative genomics, genome-wide association studies and machine learning. This work is a balance of developing new methods and applying them to questions regarding the evolution of virulence in a range of bacterial species. I am particularly interested in building algorithms that identify high-risk strrains of bacteria that are resistant to antibiotics, more likely to cause severe infections, or more likely to spread globally. I’ve been applying these insights to the development of ways to recognise emerging pathogens using genome sequence data.
Tracing outbreaks with machine learning.
Nature reviews. Microbiology 2019
Genomic correlates of extraintestinal infection are linked with changes in cell morphology in Campylobacter jejuni.
Microbial genomics 2019
Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica.
PLoS genetics 2018;14;5;e1007333
Breaking the code of antibiotic resistance.
Nature reviews. Microbiology 2018;16;5;262
Genomic, Transcriptomic, and Phenotypic Analyses of Neisseria meningitidis Isolates from Disease Patients and Their Household Contacts.
A profile-based method for identifying functional divergence of orthologous genes in bacterial genomes.
Bioinformatics (Oxford, England) 2016;32;23;3566-3574
Why so narrow: Distribution of anti-sense regulated, type I toxin-antitoxin systems compared with type II and type III systems.
RNA biology 2017;14;3;275-280
Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling.
PLoS computational biology 2014;10;10;e1003907