SCOTTI wins PLoS Computational Biology Research Prize

Work from our group has been recognised in the PLoS Computational Biology 2017 Research Prizes. SCOTTI, which infers transmission routes from genetic and epidemiological information, won the Breakthrough in Advance/Innovation category. The citation reads

Our Breakthrough Advance/Innovation winning article presents a new computational tool, called SCOTTI (Structured COalescent Transmission Tree Inference), developed by Nicola De Maio of the University of Oxford (UK), and colleagues. De Maio says, “SCOTTI represents a convenient tool to reconstruct who-infected-whom within outbreaks… [and] has been used in particular for the study of bacterial hospital outbreaks”. It combines epidemiological information about patient exposure with genetic information about the infectious agent itself.

Work is nominated and selected as described in the announcement:

The journal invited the community to nominate their favorite 2016 published Research Articles. From these nominations the PLOS Computational Biology Research Prize Committee, made up of Editorial Board members Dina Schneidman, Nicola Segata, Maricel Kann, Isidore Rigoutsos, Avner Schlessinger, Lilia Iakoucheva, Ilya Ioshikhes, Shi-Jie Chen, and Becca Asquith, selected the winners. To help support future work, the authors of each winning paper will receive award certificates and a $2,000 (USD) prize.

You can read more about SCOTTI and the accompanying paper, written by Nicola De Maio, Jessie Wu and me, here.

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.

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.