Dr Nicole Wheeler | Data Scientist

Wheeler, Nicole

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.

Publications

  • Tracing outbreaks with machine learning.

    Wheeler NE

    Nature reviews. Microbiology 2019

  • Genomic correlates of extraintestinal infection are linked with changes in cell morphology in Campylobacter jejuni.

    Wheeler NE, Blackmore T, Reynolds AD, Midwinter AC, Marshall J et al.

    Microbial genomics 2019

  • Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica.

    Wheeler NE, Gardner PP and Barquist L

    PLoS genetics 2018;14;5;e1007333

  • Breaking the code of antibiotic resistance.

    Lo SW, Kumar N and Wheeler NE

    Nature reviews. Microbiology 2018;16;5;262

  • Genomic, Transcriptomic, and Phenotypic Analyses of Neisseria meningitidis Isolates from Disease Patients and Their Household Contacts.

    Ren X, Eccles DA, Greig GA, Clapham J, Wheeler NE et al.

    mSystems 2017;2;6

  • A profile-based method for identifying functional divergence of orthologous genes in bacterial genomes.

    Wheeler NE, Barquist L, Kingsley RA and Gardner PP

    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.

    Coray DS, Wheeler NE, Heinemann JA and Gardner PP

    RNA biology 2017;14;3;275-280

  • Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling.

    Lindgreen S, Umu SU, Lai AS, Eldai H, Liu W et al.

    PLoS computational biology 2014;10;10;e1003907

Wheeler, Nicole
Nicole's Timeline
2018

Joined the Center for Genomic Pathogen Surveillance

2017

Joined the Parkhill group

PhD in Biochemistry, University of Canterbury, New Zealand

2014

New Zealand Federation for Graduate Women Sadie Balkind Award

2013

Research Assistant, University of Otago, New Zealand