REF readiness review activity: the good, the bad and the plain silly

One of the ‘perks’ of my professorial promotion is that I get to partake in our school’s REF (Research Excellence Framework) readiness review – that is, I get to read a stack of papers from my colleagues, and decide whether they deserve 1, 2, 3 or 4 stars (or even no stars at all) according to a scale that takes into account novelty, significance, rigour, and ‘other’ (that sprinkling of magic fairy dust that makes a 4* paper).  In doing this I realized that the process can be broken into two separate activities:

1. Reading research papers written by my colleagues, that are outside of my normal discipline. Actually, that is a really positive thing – something we should all do more often. I now know more about the research of some of my colleagues, know more about the world (some of the papers were genuinely interesting, even if outside my area of research), and have even learned some things that might be useful for my own group (an R package I wasn’t previously aware of). Verdict: let’s all make an effort to read our colleagues’ papers some more, perhaps especially those colleagues whose work we are not familiar with.

2. Grading my colleagues’ papers for REF starriness. This is plain silly, but no more silly than the real REF: I have some expertise in some of what my colleagues have written, but, in truth, I was not truly expert in any of the papers I scored – which is probably true of most of what happens on real REF panels too. Well, I completed the process, filled in the numbers, and seemed to be quite harsh in my judgment (~ a 1:2:3  ratio if 4*:3*:2* papers). How good can my scores really be? Was I really too harsh? Or is that about the right proportion? It is difficult to tell. One thing that was frustrating was realizing that I was doing this at the wrong time. When I read a paper, and, let’s say, at no point is written why the results of the paper might be important, surely it would be better to let my colleagues know before they publish it that they might want to explain why their results are important in their Conclusions section, rather than just have non-experts pass judgment after publication?

In the end, the whole exercise reminded me of a conversation I had a couple of years ago with our older daughter – who was 5 at the time – in which I explained the REF to her. “Every time we do some work, we get given some stars for it. If the work is not very good, we get one star; if it is quite good, we get two stars; if it is very good, we get three stars; if it is totally excellent, we get four stars.” Five-year-old completely understands the system, and says to me “Daddy, I will always give you four stars.” From this I learn two lessons: first, in the REF, we are treated like 5-year-olds; and second, that there are far more important things in life, so it is important not to get too emotional about the REF and its peculiarities.


New publication: DNA traffic in the environment and antimicrobial resistance

Our book chapter on DNA traffic in the environment and antimicrobial resistance is now published, with particular thanks to Steve Hooton who has taken the lead on this. The book is more generally about DNA traffic in the environment, co-edited by our amazing Japanese collaborator Taku Oshima. This is a really great review, bringing together the importance of the environment to gene transfer of ARGs into medically or veterinarily relevant organisms..

Hooton SP, Millard AD, Baker M, Stekel DJ and Hobman JL 2019. DNA Traffic in the Environment and Antimicrobial Resistance. In: Nishida H., Oshima T. (eds) DNA Traffic in the Environment. Springer, Singapore.


The seemingly insurmountable problem of antimicrobial resistance (AMR) in clinical, food, and agricultural environments requires considerable efforts to be made in order to mitigate associated risks. Understanding the dynamics of the multitude of processes contributing towards AMR development, spread, and persistence in microbial populations will prove paramount in resolving these problems. Mobile genetic elements (MGEs) such as plasmids, transposons/insertion sequences, and bacteriophages contribute towards horizontal gene transfer of antimicrobial resistance genes (ARGs) in the environment. ARGs can be transferred from naturally resistant, ubiquitously distributed microbial populations acting as reservoirs for these genes. When ARGs are introduced into pathogens or opportunistic pathogens, these microorganisms subsequently become problematic when introduced into human/animal populations.

The role of MGEs in the evolution and emergence of pathogens of significant clinical and veterinary importance is well-documented. From a microbiological perspective, improving our knowledge of MGE-mediated AMR transmission by the application of traditional microbial culture techniques, molecular biology methods, and genomic/metagenomic/transcriptomic approaches will enhance our understanding of the flow of genetic information in bacteria. Mathematical modelling will prove to be integral to developing testable hypotheses regarding gene transfer rates, the consequences of positive selection, persistence in the absence of selection, and the fitness cost of gaining/losing resistance.

Horizontal gene transfer of AMR genes has led to the emergence of significant globally distributed enterobacterial pathogens such as Escherichia coli, Salmonella spp., and Klebsiella spp. The consequences of the emergence of these pathogens pose significant risks for humans and veterinary medicine, in what is a highly convoluted and at present, a seemingly intractable problem.

PhD Opportunity: Modelling the Risks of Antimicrobial Resistance from Agricultural Waste to Animal and Human Health

We are advertising for a PhD in my laboratory – Modelling the Risks of Antimicrobial Resistance from Agricultural Waste to Animal and Human Health – jointly supervised with colleagues from the School of Mathematics and School of Veterinary Medicine, and jointly funded by the Medical Research Foundation and the University of Nottingham. This studentship is fully funded (fees and stipend) for four years for students from the UK or EU.

Project description

This project will use mathematical modelling to evaluate the risks to human and animal health of antibiotics and antimicrobials given to farm animals. In particular, we are concerned about the risks associated with application to land of slurry or manure, which contain antibiotics and antimicrobial resistant bacteria because of that use. You will look at two systems: the risk of antibiotic resistant bacteria in crops grown for human food; and the risk associated with sheep transferring resistance around fields that have received slurry.

Two different modelling paradigms will be compared: Bayesian Network models, that allow for probabilistic description of impact of different factors on outcomes; and Dynamical Systems models, that consider changes in populations over time. The project will benefit from extensive data we have generated both on characterising dairy slurry and its impact upon soil, and on resistance in sheep hooves, as well as data from other laboratories.


  • Prof Dov Stekel, School of Biosciences
  • Dr Sabine Tötemeyer, School of Veterinary Medicine and Science
  • Dr Theodore Kypraios, School of Mathematical Sciences

Benefits of joining the Medical Research Foundation National PhD Training Programme in AMR Research.

  • All PhD projects will be based within interdisciplinary research consortia funded by the UKRI Cross-Council AMR Initiative.
  • All students will have access to enhanced training opportunities including residential skills and training courses, cohort-building activities, and annual conferences. All are designed to expose students to a range of discipline-specific languages and interdisciplinary research skills, which are essential for enabling them to thrive as multidisciplinary AMR researchers.
  • PhD students will undertake a fully-funded 3-month interdisciplinary AMR project allowing them to work outside of their primary research area or an elective placement in industry, publishing, media, policy development or in AMR-relevant charities and organisations.
  • All Medical Research Foundation-funded PhD students will also be part of a wider cohort of 150 PhD students from across the UK who are also studying AMR. The cohort will have access to a bespoke, innovative online learning environment, which will facilitate peer-to-peer networking, question setting and mentoring.

Further information can be found on the programme’s website:

To apply

Apply here:

You should have a BSc and/or MSc degree in a quantitative subject, such as mathematics, statistics, physics or computer science, and a strong interest in applying your quantitative skills to a biomedical problem of huge public importance. Potential applicants interested in further information are encouraged to contact me at

Please give the project title and name Professor Dov Stekel as the main supervisor. You do not need to provide a research proposal as requested on the form; instead indicate that you are applying for a Medical Research Foundation National PhD Training Programme in AMR Research funded project. Please contact me at if you are intending to make an application so that your application can be processed quickly. You can also contact me with informal enquiries or help with the application.

Why we do our job: it is who we are

A couple of nights ago I had the privilege to hold a two hour Skype conversation with a young person who wants to apply to do a PhD with me. Because of the time zone (and children’s bed time), we held this conversation from 8-10pm UK time, on a day that I was technically off sick. The student had read some papers I had written with another PhD student some 10 years ago, really appreciated them, had lots of questions, which led to a wide ranging conversation about science, including that work, antimicrobial resistance, evolution, and possible PhD projects.

After the conversation, my wife said “your students are very lucky to have you” – to which I responded that she wasn’t my student – she was a prospective PhD applicant – and that anyway, what could be better than having a two hour conversation with a young person with a burning passion and interest for science.

And this led me to think – why do we do our jobs? After all, it was out of working hours and I was even off work sick, but I still made the time to do this. We do these because it is who we are. We love science, we love doing science, we love talking about science, we love meeting people around the world who share our interests, we love teaching the next generation about science. Sometimes I think our employers manage to simultaneously forget this (in management practices) and exploit it (in pay awards). We don’t do this for the good of our employers (though we want to work for successful universities); we don’t do it for our annual appraisals (though we appreciate a pay raise); we don’t do it for the crazy neoliberal metrics to which our universities are subjected – REF, TEF, FEC and all that gumf (though we appreciate that our institutions need the money these systems bring). We are very privileged to do work that we love, and that our organizations are successful when we do what we do well, that our work is structured that at least some of the time we can do the things that we love.

New Publication: DirtyGenes: testing for significant changes in gene or bacterial population compositions from a small number of samples.

As always, delighted when new papers are published. I really like this paper. It takes an important problem, and does it well. I have seen so much data presented at conferences or published in papers without appropriate statistical analysis / p-values and this paper provides a way to fix that.

Shaw LM, Blanchard A, Chen Q, An X, Davies P, Tötemeyer S, Zhu Y-G and Stekel DJ 2019. DirtyGenes: testing for significant changes in gene or bacterial population compositions from a small number of samples. Scientific Reports 9: 2373.


High throughput genomics technologies are applied widely to microbiomes in humans, animals, soil and water, to detect changes in bacterial communities or the genes they carry, between different environments or treatments. We describe a method to test the statistical significance of differences in bacterial population or gene composition, applicable to metagenomic or quantitative polymerase chain reaction data. Our method goes beyond previous published work in being universally most powerful, thus better able to detect statistically significant differences, and through being more reliable for smaller sample sizes. It can also be used for experimental design, to estimate how many samples to use in future experiments, again with the advantage of being universally most powerful. We present three example analyses in the area of antimicrobial resistance. The first is to published data on bacterial communities and antimicrobial resistance genes (ARGs) in the environment; we show that there are significant changes in both ARG and community composition. The second is to new data on seasonality in bacterial communities and ARGs in hooves from four sheep. While the observed differences are not significant, we show that a minimum group size of eight sheep would provide sufficient power to observe significance of similar changes in further experiments. The third is to published data on bacterial communities surrounding rice crops. This is a much larger data set and is used to verify the new method. Our method has broad uses for statistical testing and experimental design in research on changing microbiomes, including studies on antimicrobial resistance.