Fastbaps is a fast solution to the genetic clustering problem. It rapidly identifies an approximate fit to a Dirichlet process mixture model (DPM) for clustering multilocus genotype data. Our efficient model-based clustering approach is able to cluster datasets 10–100 times larger including over 110,000 sequences of HIV-1 pol genes.
Gubbins (Genealogies Unbiased By recomBinations In Nucleotide Sequences) identifies recombination regions in the bacterial genome using an algorithm that iteratively identifies loci containing elevated densities of base substitutions while concurrently constructing a phylogeny based on the putative point mutations outside of these regions.
Pairsnp is used to quickly obtain pairwise SNP distance matrices from multiple sequence alignments. For larger alignments, pairsnp is an order of magnitude faster than approaches based on pairwise comparison of every site.
Panaroo is a graph based pangenome clustering tool that is able to account for many of the sources of error introduced during the annotation of prokaryotic genome assemblies.
PopPUNK (Population Partitioning Using Nucleotide K-mers) uses scalable and expandable annotation- and alignment-free methods for population analysis and clustering.
SEER (sequence element enrichment analysis) is a statistical method that identifies sequence elements that are significantly enriched in a phenotype of interest. PYSEER is a further implementation of seer written in python with additional features. Both pyseer and its predecessor, seer, allow for genome-wide association studies (GWAS) to be performed in clonal organisms such as bacteria and viruses.
I'm interested in genomics applications for efforts to reduce the burden of bacterial pathogens causing pneumonia, meningitis and neonatal sepsis in infants in low-income settings.
I study global spatiotemporal dynamics of Streptococcus pneumoniae to understand the impact migration has on disease prevalence and prevention.
I'm interested in studying various modes of evolution in pathogenic bacteria and how they shape different population structure.
I characterise S. pneumoniae genome collections at the country/GPSC level to infer biologic insights, enrich the project database with genomes from public databases and support external project partners.
I study the composition, diversity, and functionality of the respiratory microbiome at different sampling sites to understand its assembly dynamics and impact on health and disease.
I develop pipeline, perform analysis and build web-based visualisation for the Global Pneumococcal Sequencing Project. Additionally, I develop and maintain team websites.
I coordinate the JUNO project and study genomics and evolution of Group B Streptococcus.
I am interested in investigating potential vaccine targets for Streptococcus agalactiae using genomic and pan-genomic approaches.
I help to develop new methods and software for the GPS project, as well as analysing GPS project data to learn more about the population structure and dynamics of S. pneumoniae.
I am interested in understanding how the respiratory microbiome develops in early life and how it is affected by environmental factors such as carbon monoxide exposure, incidences of disease, and antibiotic exposure.
I oversee the Global Pneumococcal Sequencing Project by setting overall scientific strategy, communicating with collaborators, and delivering outputs through publications.
I develop bioinformatics pipelines and research software for the Global Pneumococcal Sequencing Project.
I am involved with analysis of GPS project data and will also be supporting capacity building and training activities.
I am interested in understanding the pneumococcal genomic epidemiology in Kenya, focusing on serotype replacement, and applying mathematical models to predict the effectiveness of pneumococcal conjugate vaccines in sub-Saharan Africa.
My research focuses on using large population genomic and epidemiological datasets of microbial pathogens (bacteria and viruses) to understand pathogenesis, molecular evolution, the genetic basis of phenotypes, antimicrobial resistance, transmission, and epidemiology to inform public health.
Jukka has an Associate Faculty position at the Sanger Institute and has been a long term collaborator with the Bentley team making a special impact on development of extremely efficient analytical software. And snowboarding.
My interest is in analysing microbial populations (with a focus on Streptococcus pneumoniae) to understand the epidemiology and spread of antimicrobial resistance.
I study recombination and its effects on evolution in Neisseria meningitidis and how this affects various populations.
I am interested in how the ecologies of bacterial pathogens influence the evolution of antibiotic resistance, and how we can learn about this through analysis of genome sequences.
I study within-host evolution and transmission in Streptococcus pneumoniae as well as developing statistical and bioinformatics methods for microbial genomics.
This podcast tells stories of the science at the Wellcome Genome Campus and the people behind it. From PhD students to group leaders, software developers to diversity champions -- we speak with these individuals and discover the paths they have taken throughout their careers, what it’s really like to do some of these jobs, and learn about some of the science along the way. This podcast is co-hosted by Kathryn Murie and Sophie Belman and is funded by Wellcome Genome Campus Public Engagement. It is available to listen on Spotify, Apple Podcasts, or anywhere else you get your podcasts.
The podcast is a series that aims to provide access to conversations around mentoring and other aspects of research and career development. This is delivered as part of a collaborative project between the Wellcome Sanger Institute, Wellcome Connecting Science, and the Social Entrepreneurship to Spur Health.