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.

Attention all metagenomicists: put your pinky in the corner of your mouth & say "1 million dollars"

Already posted this to Twitter and Facebook but had to post here too.  This is wild.  DTRA has announced a $1 million prize for metagenomic analysis: DTRA Algorithm Challenge | Landing Page.  From their page
The Prize:
As nth generation DNA sequencing technology moves out of the research lab and closer to the diagnostician's desktop, the process bottleneck will quickly become information processing. The Defense Threat Reduction Agency (DTRA) and the Department of Defense are interested in averting this logjam by fostering the development of new diagnostic algorithms capable of processing sequence data rapidly in a realistic, moderate-to-low resource setting. With this goal in mind, DTRA is sponsoring an algorithm development challenge. 
The Challenge:
Given raw sequence read data from a complex diagnostic sample, what algorithm can most rapidly and accurately characterize the sample, with the least computational overhead?

My instinct is to keep this to myself because, well, I want to win.  But my sharing side of things won out and I am posting here.  Maybe we (i..e, the community) can develop an open, collaborative project to do this?  Just a thought ...