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Announcing the 2017 PLOS Computational Biology Research Prize Winners

Earlier this year, PLOS Computational Biology launched the “PLOS Computational Biology Research Prize” program with the aim of recognizing the journal’s best Research Articles published last year (2016) in three prize categories: Breakthrough Advance/Innovation, Exemplary Methods/Software, and Public Impact.

The PLOS Computational Biology Editors-in-Chief would like to congratulate our 2017 winners:


Breakthrough in Advance/ Innovation

SCOTTI: Efficient Reconstruction of Transmission within Outbreaks with the Structured Coalescent

Authors: Nicola De Maio, Chieh-Hsi Wu, Daniel J. Wilson


Exemplary Methods/Software

MEGAN Community Edition – Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data

Authors: Daniel H. Huson, Sina Beier, Isabell Flade, Anna Górska, Mohamed El-Hadidi, Suparna Mitra, Hans-Joachim Ruscheweyh, Rewati Tappu


Public Impact

Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks

Authors: Robin N. Thompson, Christopher A. Gilligan, Nik J. Cunniffe


These three studies span diverse fields ranging from advanced research in disease transmission to microbiome analysis and epidemic forecasts.

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.

Another computational tool was the focus of the winning study in the Exemplary Methods/Software category. Study author Daniel Huson of the University of Tübingen (Germany), and colleagues developed a new, open source program called MEGAN (MEtaGenome ANalyzer) Community Edition to analyze the genetic diversity of microbial communities, or microbiomes. The tool can rapidly process large datasets involving billions of DNA sequence fragments in order to identify microbial species and their activity within a microbiome.

The winner of the Public Impact category is a study from Robin Thompson of the University of Cambridge (UK), and colleagues. “This publication…had a big impact on my career as a scientist…”, says Robin Thompson, “[and] I hope to continue in the field of mathematical epidemiology throughout my career.” The paper highlights a major challenge to predicting whether a disease outbreak in its earliest stages might develop into a widespread epidemic, using mathematical modeling to show that accurate prediction is impossible without available data on the number of people who are infected but not yet showing symptoms.


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.

“We received a wide range of research article nominations, all of which exemplified the outstanding work published by our international community,” says PLOS Computational Biology Editor-in-Chief Jason Papin. “It is a pleasure to announce our 2017 winners, and we look forward to the second round of our Research Prize program in 2018.”

Further details about the prize can be found here:


Featured image credit: Tekin et al.,

Text adapted from press release provided by science writer Sarah Stanley.


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