Deep brain stimulation in Parkinson’s disease, modelling chromatin dynamics, ant obstacle courses

  Deep brain stimulation in Parkinson’s disease, modelling chromatin dynamics, ant obstacle courses   post-info Check out our Editors-in-Chief’s selection of papers from the May issue of PLOS Computational Biology. Quantitative theory of deep brain stimulation of

Winners of the 2018 PLOS Computational Biology Research Prize

  Winners of the 2018 PLOS Computational Biology Research Prize   post-info It’s time to celebrate the best of PLOS Computational Biology!  In 2017 PLOS Computational Biology launched the “PLOS Computational Biology Research Prize” program

Announcing the PLOS Computational Biology Research Prize 2018 – Nominate your favorite articles by April 13

0000-0002-8715-2896 Announcing the PLOS Computational Biology Research Prize 2018 – Nominate your favorite articles by April 13   post-info We’re pleased to announce that nominations are now open for this year’s PLOS Computational Biology Research

Machine Learning in Health and Biomedicine: A PLOS Medicine, PLOS Computational Biology, and PLOS ONE Call for Papers

0000-0002-8715-2896 Machine Learning in Health and Biomedicine: A PLOS Medicine, PLOS Computational Biology, and PLOS ONE Call for Papers   post-info PLOS Medicine, PLOS Computational Biology and PLOS ONE announce a cross-journal Call for Papers

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.

A neuron’s eye view, recovering from intestinal cleanout, and models of cancer cell line metabolism

0000-0002-8715-2896 A neuron’s eye view, recovering from intestinal cleanout, and models of cancer cell line metabolism   post-info AddThis Sharing Buttons above Check out our highlights from the PLOS Computational Biology August issue: A systems

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.

Anaesthesia Response, Controlling Cas9, and How to Use Github: the PLOS Comp Biol January Issue

social_norms_in_small_scale_societies-5-690x320AddThis Sharing Buttons above Here are our highlights from the PLOS Computational Biology January issue: Brain Connectivity Dissociates Responsiveness from Drug Exposure during Propofol-Induced Transitions of Consciousness Scientific understanding of how brain networks generate consciousness

Slice of PLOS: The Awesomeness of Bats

Flying-bats-640x320Just how awesome are bats? It’s easy to forget that one in five species of mammal on this planet have wings capable of delivering spectacularly acrobatic flying abilities. Equally incredibly, two-thirds of these 1,200 species

Happy Fins: Plesiosaurs Flapped like Penguins

plesiosaur_striking-crop-690x320One of the most infuriating things about being a paleontologist is being able to study some of the coolest organisms that have ever inhabited the Earth, yet never being able to see one in life.