Machine Learning versus Statistical Inference in Microbial Genomics

My talk given today at the 2025 Microbiology Society Conference in Liverpool:

Abstract

The advent of vast genomic datasets has transformed microbiology, presenting opportunities and challenges for data analysis. The distinct philosophies of machine learning (ML) and statistical inference gives them complementarity strengths and weaknesses in tackling big data problems in pathogen research. While statistical inference prioritizes understanding underlying relationships, ML focuses on optimizing predictive performance. In this talk I will contrast the approaches and offer a view on their relative utility for three problems: source attribution, bacterial genome-wide association studies, and predicting antimicrobial resistance phenotypes from whole genome sequences.

Seeking Postdoc in Statistical Genetics and Infectious Disease

I am seeking a senior postdoc in Statistical Genetics and Infectious Disease to join my research group at the Big Data Institute, University of Oxford. Our research into Infectious Disease Genomics is focused on developing and applying big data methods to identify genetic risk factors for disease, both microbial virulence factors and human susceptibility genes. We are focused on a range of bacterial and viral diseases including staphylococcal sepsis and COVID-19.

The Big Data Institute, part of Oxford Population Health, provides an excellent environment for multi-disciplinary research and teaching. Situated on the modern Old Road Campus in the heart of the medical sciences neighbourhood of Headington, we benefit from outstanding facilities and opportunities to collaborate with world-leading scientists and clinicians to help expand knowledge and improve global health.

As a Senior Postdoc the post-holder will work closely with me to jointly lead the implementation, design and application of new statistical tools for genome-wide association studies, and to lead the biological interpretation of key findings. They will develop novel methodologies for analysis and data collection, take the lead in the production of scientific reports and publications and supervise junior group members.

To be considered applicants will have a PhD and post-doctoral experience in a relevant subject, with direct experience in statistical genetics, demonstrable expertise and knowledge of the statistical genetics literature or a closely related, relevant discipline and a publication record as first author, in statistical genetics.

The position is full time (part time considered) and fixed-term for 3 years.

The closing date for application is 12.00 noon GMT on 18th March.

Click here for more information including how to apply.

Postdoc position available in Statistical Genomics

I am seeking someone with a track record in methods development for Statistical Genomics and an interest in Infectious Disease to join the group. The aim of the post is to conduct innovative research within the group's range of interests and to make use of the opportunities afforded by our outstanding collaborators. I would welcome candidates who wish to use the opportunity as a stepping stone to independent funding.

The postdoc will join a team with expertise in microbiology, genomics, evolution, population genetics and statistical inference. Responsibilities will include planning a research project and milestones with help and guidance from the group, preparing manuscripts for publication, keeping records of results and methods and tracking milestones, and disseminating results, including through academic conferences.

We will consider applicants who hold, or are close to completion of, a PhD/DPhil involving statistical methods development, and who have experience of large-scale statistical data analysis, evidence of originating and executing independent academic research ideas, excellent interpersonal skills and the ability to work closely with others in a team.

The position is advertised to 31 December 2021. The application deadline is noon on Thursday 1st October 2020. Visit the University recruitment page to apply.

Postdoc Available in Statistical Genetics

The closing date for applications for this post is noon on Wednesday 15th April 2020.

We are seeking an exceptional researcher with a track record in methods development for Statistical Genomics and an interest in Infectious Disease to join our group at the Big Data Institute. Our research focuses on Bacterial Genomics, Genome-Wide Association Studies and Population Genetics. The aim of the post is to conduct innovative research within the group's range of interests and to make use of the opportunities afforded by our outstanding collaborators. We welcome candidates who wish to use the opportunity as a stepping stone to independent funding.

The Oxford University Big Data Institute (BDI) is an interdisciplinary research centre aiming to develop, evaluate and deploy efficient methods for acquiring and analysing biomedical data at scale and for exploiting the opportunities arising from such studies. The Nuffield Department of Population Health, a partner in the BDI, contains world-renowned population health research groups and is an excellent environment for multi-disciplinary teaching and research.

The Postdoctoral Researcher in Statistical Genomics will join our team which has expertise in microbiology, genomics, evolution, population genetics and statistical inference. Responsibilities include planning a research project and milestones with help and guidance from the group, preparing manuscripts for publication, keeping records of results and methods and tracking milestones, and disseminating results.

To be considered, you need to hold, or be close to completion of, a PhD/DPhil involving statistical methods development. You also need experience of large-scale statistical data analysis, evidence of originating and executing your own academic research ideas and excellent interpersonal skills and the ability to work closely with others in a team.

For informal enquiries, please contact me.

Further details, including how to apply are here: https://my.corehr.com/pls/uoxrecruit/erq_jobspec_details_form.jobspec?p_id=145506

New paper: SCOTTI Efficient reconstruction of transmission within outbreaks with the structured coalescent

New paper published today in PLoS Computational Biology: Understanding how infectious disease spreads and where it originates is essential for devising policies to prevent and limit outbreaks. Whole genome sequencing of pathogens has proved an extremely promising tool for identifying transmission, particularly when combined with classical epidemiological data. Several statistical and computational approaches are available for exploiting genomics for epidemiological investigation. These methods have seen applications to dozens of outbreak studies. However, they have a number of serious drawbacks.

In this new paper Nicola De Maio, Jessie Wu and I introduce SCOTTI, a method for quickly and accurately inferring who-infected- whom from genomic and epidemiological data. SCOTTI addresses very widespread, but generally neglected problems in joint epidemiological and genomic inference, notably the presence of non-sampled and undetected intermediate cases and within-host pathogen variation caused by microevolution. Using real examples and simulations, we show that these problems cause strong misleading effects on existing popular inference methods. SCOTTI is based on BASTA, our recent breakthrough method for phylogeographic inference, and offers new standards of accuracy, calibration, and computational efficiency. SCOTTI is distributed as an open source package within BEAST2.

Prize PhD Studentships available

I am offering two PhD projects as part of the annual Nuffield Department of Medicine Prize Studentship competition:

• Real-time detection of multidrug resistant tuberculosis and transmission in England
  Joint with David Wyllie, molecular microbiologist, this project is focused on developing statistical methods for recognizing transmission clusters, integrating genomics approaches with molecular typing schemes and developing future-proof taxonomy for strain identification.
  
  
• Tracking future infection threats using genomic data and electronic health records
  Joint with David Clifton, biomedical engineer, this project aims to develop new machine learning and statistical methods to identify genomic markers of antibiotic resistance and susceptibility within various pathogens, to help track future infection threats.

These are fully-funded, four-year awards open to outstanding students of any nationality. Applicants nominate three projects, in order of preference, from the available pool. For how to apply, click here. Only applications submitted through the online system will be considered, but interested applicants are welcome to contact me informally. The deadline for applications is noon, 6th January 2017.

In addition to my projects, the Modernising Medical Microbiology project has announced the following PhD projects as part of the competition:

• Antimicrobial resistance gene/vector transmission across human, animal and environmental reservoirs
  Supervised by Nicole Stoesser, Nicola De Maio and Derrick Crook
  
  
• Healthcare big data and genomics for infectious disease threat detection
  Supervised by David Clifton, David Eyre and Tim Peto
  
  
• Prediction of Mycobacterium tuberculosis drug resistance through genome sequencing clinical samples
  Supervised by Tim Walker and Tim Peto
  
  
• Antibiotic resistance in Tuberculosis: Predicting de novo the effect of individual genetic mutations
  Supervised by Phil Fowler and Sarah Walker

Tackling and tracking TB through DNA analysis

Postdoctoral Scientist in Statistical Genomics

We are recruiting for a Postdoctoral Scientist in Statistical Genomics working on Antimicrobial Resistance (AMR) gene discovery and focused on Tuberculosis. This will be a joint position at the University of Oxford between Derrick Crook's group and mine, and part of the large international CRyPTIC consortium.

The role is for a population geneticist or statistical geneticist to develop and apply statistical methods, including genome-wide association studies, for discovering rare and common genetic variants underlying antimicrobial resistance in Mycobacterium tuberculosis.

One third of the world's population - 2.5 billion people - are thought to be infected with tuberculosis (TB). This post offers an opportunity to work with global TB experts from five continents, statistical geneticists, clinicians, medical statisticians and software engineers; integrating statistical genetics, bioinformatics and machine learning methods with the aim of uncovering all genomic variants causing at least 1% resistance to first line anti-TB drugs.

We're looking for candidates with a PhD in genomics, evolutionary biology, statistics or a related subject. The post is full-time and fixed-term for up to 3 years initially.

The deadline for applications is noon on Friday 6th May 2016.

Making the most of bacterial GWAS: new paper in Nature Microbiology

In a new paper published this week in Nature Microbiology, we report the performance of genome wide association studies (GWAS) in bacteria to identify causal mechanisms of antibiotic resistance in four major pathogens, and introduce a new method, bugwas,  to make the most of bacterial GWAS for traits under less strong selection.

As explained by Sarah Earle, joint first author with Jessie Wu and Jane Charlesworth, the problem with GWAS in bacteria is strong population structure and the consequent strong coinheritance of genetic variants throughout the genome. This phenomenon - known as genome-wide linkage disequilibrium (LD) - comes about because exchange of genes is relatively infrequent in bacteria, which reproduce clonally, compared to organisms that exchange genes every generation through sexual reproduction.

Genome-wide LD makes it difficult for GWAS to distinguish variants that causally influence a trait from other, coinherited variants that have no direct effect on the trait.

In the case of antibiotic resistance - a trait of high importance to human health - bacteria are under extraordinary selection pressures because resistance is a matter of life and death, to them as well as their human host. This helps overcome coinheritance and pinpoint causal variants because antibiotic usage selects for the independent evolution of the same resistance-causing variants in different genetic backgrounds.

Consequently, bacterial GWAS works very efficiently for antibiotic resistance: the variants most significantly associated with antibiotic resistance in 26 out of the 27 GWAS we performed were genuine resistance-conferring mutations. In the 27th we uncovered a putative novel mechanism of resistance to cefazolin in E. coli. These results for 17 antibiotics (ampicillin, cefazolin, cefuroxime, ceftriaxone, ciprofloxacin, erythromycin, ethambutol, fusidic acid, gentamicin, isoniazid, penicillin, pyrazinamide, methicillin, rifampicin, tetracycline, tobramycin and trimethoprim) across four species (E. coli, K. pneumoniae, M. tuberculosis and S. aureus) build on earlier work investigating beta-lactam resistance in S. pneumoniae, and convincingly demonstrate the potential for bacterial GWAS to discover new genes underlying important traits under strong selection.

What about traits under less strong selection, which probably includes pretty much every other bacterial trait? We show in this context that coinheritance poses a major challenge, based on detailed simulations. Often it may not be possible to use GWAS to pinpoint individual variants responsible for different traits because they are coinherited with - possibly many - other uninvolved variants.

But all is not lost. We show that even when individual locus-level effects cannot be pinpointed, there is often excellent power to characterize lineage-level differences in phenotype between strains. This is helpful for multiple reasons: (1) we often conceptualize trait variability in bacteria at the level of strain-to-strain differences (2) these differences can be highly predictive (3) we can prioritize variants for functional follow-up based on their contribution to strain-level differences.

These concepts represent a substantial departure from regular GWAS. In the human setting for instance, lineage-level differences are usually discarded as uninteresting or artefactual, and variants are almost always prioritized based on statistical evidence for involvement over-and-above any contribution to lineage-level differences. In the bacterial setting, we are forced to depart from these conventions because a large proportion of all genetic variation is strongly strain-stratified. To find out more, see the paper and try our methods.

CRyPTIC: rapid diagnosis of drug resistance in TB

The Modernising Medical Microbiology consortium has announced a new worldwide collaboration called CRyPTIC to speed up diagnosis of antibiotic resistant tuberculosis (TB).

TB infects nearly 10 million people each year and kills 1.5 million, making it one of the leading causes of death worldwide. Almost half a million people each year develop multidrug-resistant (MDR) TB, which defies common TB treatments. Time consuming tests must be run to identify MDR-TB and which drugs will work or fail. This delays diagnosis and creates uncertainty about the best drugs to prescribe to individual patients.

CRyPTIC aims to hasten the identification of MDR-TB using whole genome sequencing to identify genetic variants that give resistance to particular drugs. The project is funded by a $2.2m grant from the Bill & Melinda Gates Foundation and a £4m grant from the Wellcome Trust and MRC Newton Fund.

CRyPTIC aims to collect and analyse 100,000 TB cases from across the world, providing a database of MDR-TB that will underpin diagnosis using WGS. Samples from across Africa, Asia, Europe and the Americas will be collected by teams at more than a dozen centres They will conduct drug resistance testing and much of the genome sequencing. Read more information here.

Snow Monkeys in Japan

Recently got back from the SMBE Satellite meeting on Pathogen Genomics in Japan. The organizers did a fantastic job and the talks were great. There was also time to visit the Japanese macaques at Snow Monkey Park, where one of the little guys climbed on to my shoulders

Thanks Ashlee Earl for the video and Koji Yahara, Alan McNally and Nick Croucher for additional commentary!

Nature Reviews Microbiology: Within-host evolution of bacterial pathogens

Our new review of what genomics has taught us about Within-host evolution of bacterial pathogens has been published in Nature Reviews Microbiology.

New paper: Rapid host switching in Campylobacter

Our new open access paper Rapid host switching in generalist Campylobacter strains erodes the signal for tracing human infections was published last week in the ISME Journal.

 

With Bethany Dearlove, Sam Sheppard and colleagues, we investigated common strains of campylobacter, the most frequent cause of bacterial gastroenteritis worldwide. Campylobacter infection is associated with food poisoning, particularly contaminated chicken. But in previous work, we found that certain strains (the ST-21, ST-45 and ST-828 complexes) are often found contaminating a range of meat and poultry, making it difficult to trace the source of human infection.

That previous work was based on partial genome sequencing known as MLST. In MLST, less than 1% of the information in the genome is captured. Now that whole genome sequencing is available, the expectation was that we should be able to distinguish easily between between ST-21, 45 and 828 strains contaminating poultry versus beef versus lamb, and so on.

What we found was surprising. Instead of these strains harbouring previously unobserved sub-structure that allowed them to be associated with different animal sources, we found rapidly mixing populations undergoing extremely fast transmission between animal species, with campylobacter strains ricocheting among animal species on a timescale of just a few years. This is faster than they can accumulate enough mutations to differentiate populations colonizing different animal species.

Our results present an unforeseen roadblock to tracing transmission with whole genome sequencing, and suggests these strains are adapted to a generalist lifestyle, shedding new light on the ecology of this pathogen. These findings push back against the tide of opinion that whole genome sequencing is necessarily a panacea for detecting transmission, and demonstrate that going forwards, a detailed understanding of the biology of zoonotic bacteria (those transmitting between multiple species) and intensive sampling of potential sources are essential for effectively tracing the source of human infection.

New Journal: Microbial Genomics

This week sees the launch of Microbial Genomics, a new open access journal from the Society for General Microbiology. Here's an excerpt from the journal's mission statement:

"Microbial Genomics (MGen) publishes high quality, original research on archaea, bacteria, microbial eukaryotes and viruses. MGen welcomes papers that use genomic approaches to understand microbial evolution, population genomics and phylogeography, outbreaks and epidemiological investigations, impact of climate or changing niche, metagenomic and whole transcriptome studies, and bioinformatic analysis covering the breadth of microbiology, from clinically important pathogens to microbial life in diverse ecosystems."

The journal, whose tag line is Bases to Biology, will publish microbiological discoveries and innovations in research methods and bioinformatics. The journal is headed by renowned Wellcome Trust Sanger Institute scientists Stephen Bentley and Nicholas Thompson with an impressive editorial board that I joined earlier this year. Article processing charges have been waived during the journal's launch year - so get in there fast!

Cheltenham Science Festival

Earlier this week members of the group represented the Nuffield Department of Medicine at the Cheltenham Science Festival with our Modernising Medical Microbiology stall, featuring the Antibiotic Resistance Coconut Shy and the Genome Evolution Dance Mat.

Antibiotic Resistance Coconut Shy

Antibiotic Resistance Coconut Shy: The children (and adults) visiting the stall were given five bean bags (antibiotics) to throw at the coconuts (bacterial pathogens) to try to knock them off. The front row of coconuts, representing bacteria more susceptible to antibiotics, were easier to knock off than the back row, which represented more resistant bacteria. The aim was to show the children that an unwanted side effect of using antibiotics is to increase the frequency of resistant bacteria, because they were usually the ones left standing.

The game was more difficult than it looks, and just one visitor knocked off all five coconuts. We gave out NDM pens to the sixty visitors who managed to knock off three or more.

Microscope and Top Trumps

Digital Microscope: We brought along a light microscope to show the children what bacteria really look like, which helps emphasize how small they are since they are difficult to see even under the highest magnification. We prepared slides for several Gram positive and Gram negative species, and provided a key to help identify them. We also brought along a number of games that have been used in previous departmental outreach activities, including Pathogen Top Trumps and Fact or Fiction.

Genome Evolution Dance Mat

Genome Evolution Dance Mat: In this game, the children had to copy a bacterial DNA sequence by replicating a sequence of dance moves (up=A, left=C, right=G, down=T) without introducing new errors (mutations). Any mutations that were introduced were passed on to the next template sequence. In this way we aimed to show how mutations occur by errors in DNA replication, and that they are inherited. This generates unique DNA fingerprints for bacteria, which we can use to track the spread of outbreaks.

Outbreak Map

The game, which was kindly programmed by Gareth Jenkin-Jones, included a form of natural selection, so that if too many errors were introduced at once, the sequence was considered inviable and did not survive to be passed on. There was also a speed control, which was handy since some people appear to have spent a lot more of their youth playing dance mats than others.

Outbreak Map: We made an Outbreak Map to show the reach of our stall over the day, with visitors that scored highly on the coconut shy pushing in pins to show where they had travelled from. Had we been handing out germs instead of pens, we could have started outbreaks as far afield as Edinburgh, France and Spain, as well as a large cluster in Cheltenham and the surrounding counties.

Other research groups are representing the department throughout the week.

NDM Microbiology Stall at the Cheltenham Science Festival (L-R): Sarah Earle, Louise Pankhurst, Danny Wilson, Liz Batty, Dilrini De Silva, Jess Hedge, Catrin Moore. Amy Mason, Gareth Jenkin-Jones and Jane Charlesworth also helped with the preparations, and Jen Bardsley co-ordinated all the NDM Stalls.

The role of hospital transmission in Clostridium difficile infection

This week the Modernising Medical Microbiology consortium at Oxford published the findings of a six-year study into the transmission of the hospital "superbug" Clostridium difficile. The research, which appears in the New England Journal of Medicine, shows that the majority of new cases cannot be traced to other infections in hospital, and indicates instead that there must be a large, as yet unidentified, reservoir of C. difficile infectious to humans. This finding is important because it suggests that there is a limit to which more and more intense hospital cleaning - important though it has been - can continue to have in reducing C. difficile infection.

The research, which is the result of a tireless effort by a large number of my colleagues - notably David Eyre, Tim Peto and Sarah Walker - used bacterial whole genome sequencing to detect within-hospital transmission by searching for extremely closely related bacterial strains among more than 1200 cases of C. difficile infection that occurred in Oxfordshire between September 2007 and March 2011. The consortium is currently developing the approach for routine microbiology diagnostics and infection control, with a view to eventual roll-out across the NHS.

Detecting mixed strain infections with whole genome sequencing

Whole genome sequencing in near-to-real time is set to become a routine tool for outbreak detection by hospital and public health microbiology labs, following successful pilot studies in the UK last year. Typically, the bacteria are cultured from a clinical sample, and a single colony is picked for sequencing. Since a bacterial colony grows from a single cell, this procedure ensures that all the cells picked for sequencing are genetically identical, and this in turn helps piece the genome back together again following sequencing.

But it exposes the system to a flaw. What would happen if a patient sick with two strains transmitted one, but not the other to a second patient? Characterizing the genome of just one of the strains in the first patient risks missing the transmission event entirely, because the "wrong" strain might have been sequenced.

One safeguard would be to sequence multiple bacterial colonies per sample, three for example. But this would increase the cost of routine surveillance three-fold.

In a new paper published this month in PLoS Computational Biology, with David Eyre, Madeleine Cule, Sarah Walker and others, we have investigated an alternative solution, where by a large number of colonies gets sequenced all together. The cost is the same as that of sequencing a single colony. But the downstream bioinformatics analysis is complicated considerably by the presence of multiple strains. To cope with this, we developed a new computational method that reconstructs the identities of the multiple strains, using a panel of reference genomes to help where possible.

By applying the approach to 26 clinical samples of Clostridium difficile hospital infections with known epidemiological relationships, we detected four mixed strain infections, one of which revealed a previously undetected transmission event within the hospital. For full details, read the open access paper.

Within-host evolution of Staphylococcus aureus during asymptomatic carriage

Given its notoriety as one of the world's major causes of infection-related deaths, it may come as a surprise that one in three healthy adults carry the human pathogen Staphylococcus aureus in their noses without adverse effects. Indeed, most people carry the bacteria at some point in their lives. So carriage must be seen as the normal state of affairs in the human-S. aureus interaction, and by understanding this state better we can improve our understanding of why, in some people, the bacteria go on to cause life-threatening invasive disease.

This month sees publication of an investigation by my colleagues and me into the evolution of S. aureus during this normal healthy carriage state. The carriers in our study harboured populations of the bacteria that were very closely related but typically not identical, implying that the bacteria had evolved within the human body. The nose appears to be a microcosm of evolution for S. aureus, showing all the different types of genetic variation known at the species level within the noses of these individual carriers. For the most part, within-host evolution of the bacteria was very conservative, but certain proteins expressed on the surface of the bacteria and toxins secreted by the bacteria showed evidence of involvement in a host-pathogen arms race.

The paper, whose lead authors include Tanya Golubchik, Liz Batty, Derrick Crook and Rory Bowden, has received coverage on the EveryONE blog and F1000. I liked Gerald Pier's conclusion, made on the post-publication peer review website: "Given that about 30% of the world's seven billion-plus humans, and an unknown number of animals, are chronically colonized with S. aureus, the tremendous opportunity provided to this organism for generating genetic variation to counteract human efforts to prevent S. aureus infections may be one of the most formidable barriers to overcome in order to develop vaccines and highly effective interventions to lessen the impact of this organism on human and animal health."

PLoS Pathogens Review Published!

Published today in PLoS Pathogens:

Post-doc Positions in Pathogen Genomics

Post-doc positions in Pathogen Genomics are available in my group and Derrick Crook's lab. We will be hiring people to work on pathogen whole genome sequence analysis and bioinformatics. More details available soon. In the meantime, find out about our research:

• Nuffield Department of Medicine: Dr Daniel Wilson
• Nuffield Department of Medicine: Prof Derrick Crook
• Oxford Martin School: Health and Medicine
• Modernising Medical Microbiology
• Oxford Biomedical Research Centre: Infection Theme

If you are interested, please get in touch.