home Antimicrobials and Resistance, Bioinformatics, Job postings, Sequencing Job: antibiotic resistance in 100k TB genomes

Job: antibiotic resistance in 100k TB genomes

Hi All

I’d just like to highlight a job we are advertising, to lead the bioinformatic (sequence+variation) analysis of 100,000 M tuberculosis genomes which we are sequencing (WGS), ~50,000 of which will be phenotyped for 12 drugs, and the remainder for some subset. Note the deadline is very soon – Monday 6th March!

This project is run by a global consortium (http://modmedmicro.nsms.ox.ac.uk/cryptic/) including CDC Atlanta, CDC China, Oxford, Germany, Peru, Vietnam, and many others, funded by the Gates Foundation and Wellcome Trust/Newton Fund. The goal is to produce a better catalog of drug resistance mutations than the one we have now, in order to enable WGS for clinical application. All sequence and phenotypes will be placed in the public domain.

The job description is here


with a link to a fuller specification. Basically the job is to work with me on the variation/genome analysis of these 100k genomes; our immediate “customers” would be the statistics, GWAS and machine learning teams primarily at Oxford. In addition, you would get to work with the other half of my team, working on public TB “graph genome” database. Here’s a video of a precursor:


This is an unusual bioinformatician role where there is a clear and direct route from the work done to concretely improving human health. The UK has already moved to sequencing by default when faced with potential TB patients, and our software is already embedded in that pipeline. The learnings from this project could go on to make a direct impact on clinical care across the world.

The job is based at the European Bioinformatics Institute, just outside Cambridge, UK, although he employer is Oxford University. Here’s a picture of the campus:

and here from further away

Recent work that is related from our group includes 

 – sequencing Mtb from liquid culture (https://www.ncbi.nlm.nih.gov/pubmed/25631807)

 – applying the above prospectively in a public health lab (https://www.ncbi.nlm.nih.gov/pubmed/26669893)

 – Rapid and automated antibiotic resistance prediction for Staph and TB (http://www.nature.com/ncomms/2015/151221/ncomms10063/full/ncomms10063.html)

 – Same-day WGS of Mtb direct from sputum without culture, using lllumina and Nanopore (http://biorxiv.org/content/early/2016/12/16/094789 )


Zamin Iqbal

Zamin Iqbal builds computational models of microbial genomes. These models are important tools for understanding the key forces that shape evolution, establishing how genotype affects biological traits and exploring concepts such as ancestry and infection. The standard model for analysing a species is to take a random individual, reconstruct its genome and make an assumption that all other genomes from that species are mostly the same. This reduces sequence analysis to a simple string matching problem (“read mapping”). When this assumption is true, the method works well; however, for many regions of genomes (e.g. the human MHC, surface antigens in P. falciparum, the non-core part of any bacterial genome), this assumption is false. Our group works on this problem, developing computational methods for representing genetic variation, and using them to study bacteria and parasites. We work closely with species experts, and draw on concrete exemplars of challenging cases to fine-tune our models and methods. Our methods include Cortex and more recently a modified Burrows Wheeler Transform approach that naturally models the impact of recombination, finding mosaic paths through a reference panel of genomes. Our translational work focuses on applying whole-genome sequencing to pathogens in a clinical setting. We have developed a rapid, lightweight app (called Mykrobe Predictor) for predicting antibiotic resistance given sequence data from a sample of S. aureus or M. tuberculosis, and are working on testing, updating and extending this to other species. To enable strain and resistance surveillance, we are building online genome graph databases of pathogen variation.

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

%d bloggers like this: