Biology subjects

Bull, M. J.

Publications and source records attributed to Bull, M. J..

3 recordsLinked to original sources

bioRxiv2020

MAJORA: Continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance

Genomic epidemiology has become an increasingly common tool for epidemic response. Recent technological advances have made it possible to sequence genomes rapidly enough to inform outbreak response, and cheaply enough to justify dense sampling of even large epidemics. With increased availability of sequencing it is possible for agile networks of sequencing facilities to collaborate on the sequencing and analysis of epidemic genomic data. In response to the ongoing SARS-CoV-2 pandemic in the United Kingdom, the COVID-19 Genomics UK (COG-UK) consortium was formed with the aim of rapidly sequencing SARS-CoV-2 genomes as part of a national-scale genomic surveillance strategy. The network consists of universities, academic institutes, regional sequencing centres and the four UK Public Health Agencies. We describe the development and deployment of Majora, an encompassing digital infrastructure to address the challenge of collecting and integrating both genomic sequencing data and sample-associated metadata produced across the COG-UK network. The system was designed and implemented pragmatically to stand up capacity rapidly in a pandemic caused by a novel virus. This approach has underpinned the success of COG-UK, which has rapidly become the leading contributor of SARS-CoV-2 genomes to international databases and has generated over 60,000 sequences to date.

bioinformatics↗

bioRxiv2020

Kill and cure: genomic phylogeny and bioactivity of a diverse collection of Burkholderia gladioli bacteria capable of pathogenic and beneficial lifestyles

Burkholderia gladioli is one of few bacteria with a broad ecology spanning disease in humans, animals, and plants, and encompassing beneficial interactions with multiple eukaryotic hosts. It is a plant pathogen, a bongkrekic acid toxin producing food-poisoning agent, and a lung pathogen in people with cystic fibrosis (CF). Contrasting beneficial traits include antifungal production exploited by insects to protect their eggs, plant protective abilities and antibiotic biosynthesis. We explored the ecological diversity and specialized metabolite biosynthesis of 206 B. gladioli strains, phylogenomically defining 5 evolutionary clades. Historical disease pathovars (pv) B. gladioli pv. allicola and B. gladioli pv. cocovenenans were phylogenetically distinct, while B. gladioli pv. gladioli and B. gladioli pv. agaricicola were indistinguishable. Soft-rot disease and CF infection pathogenicity traits were conserved across all pathovars. Biosynthetic gene clusters for toxoflavin, caryoynencin and enacyloxin were dispersed across B. gladioli, but bongkrekic acid and gladiolin production were clade specific. Strikingly, 13% of CF-infection strains characterised (n=194) were bongkrekic acid toxin positive, uniquely linking this food-poisoning risk factor to chronic lung disease. Toxin production was suppressed by exposing strains to the antibiotic trimethoprim, providing a potential therapeutic strategy to minimise poisoning risk in CF.

microbiology↗

bioRxiv2019

Influenza Classification from Short Reads with VAPOR Facilitates Robust Mapping Pipelines and Zoonotic Strain Detection for Routine Surveillance Applications

BackgroundInfluenza viruses are associated with a significant global public health burden. The segmented RNA genome of influenza changes continually due to mutation, and the accumulation of these changes within the antigenic recognition sites of haemagglutinin (HA) and neuraminidase (NA) in turn leads to annual epidemics. Influenza A is also zoonotic, allowing for exchange of segments between human and non-human viruses, resulting in new strains with pandemic potential. These processes necessitate a global surveillance system for influenza monitoring. To this end, whole-genome sequencing (WGS) has begun to emerge as a useful tool. However, due to the diversity and mutability of the influenza genome, and noise in short-read data, bioinformatics processing can present challenges.\n\nResultsConventional mapping approaches can be insufficient when a sub-optimal reference strain is chosen. For short-read datasets simulated from influenza H1N1 HA sequences, read recovery after single-reference mapping was routinely as low as 90% for human-origin influenza sequences, and often lower than 10% for those from avian hosts. To this end, we developed a de Bruijn Graph (DBG)-based classifier of influenza WGS datasets: VAPOR. In real data benchmarking using 257 WGS read sets with corresponding de novo assemblies, VAPOR provided classifications for all samples with a mean of >99.8% identity to assembled contigs. This resulted in an increase in the number of mapped reads by 6.8% on average, up to a maximum of 13.3%. Additionally, using simulations, we demonstrate that classification from reads may be applied to detection of reassorted strains.\n\nConclusionsVAPOR has potential to simplify bioinformatics pipelines for surveillance, providing a novel method for detection of influenza strains of human and non-human origin directly from reads, minimization of potential data loss and bias associated with conventional mapping, and allowing visualization of alignments that would otherwise require slow de novo assembly. Whilst with expertise and time these pitfalls can largely be avoided, with pre-classification they are remedied in a single step. Furthermore, our algorithm could be adapted in future to surveillance of other RNA viruses. VAPOR is available at https://github.com/connor-lab/vapor.

bioinformatics↗