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Predicting the effect of toxic compounds on individuals: Crowdsourcing initiative for systems biomedicine

Date:
August 10, 2015
Source:
European Bioinformatics Institute EMBL-EBI
Summary:
An international study presents the combined results of a 2013 DREAM Challenge: a crowd-sourcing initiative to test how well the effects of a toxic compound can be predicted in different people. The study, which is relevant to public and occupational health, shows that computational methods can be used to predict some toxic effects on populations, although they are not yet sensitive enough to predict such effects in individuals. It also presents algorithms useful for environmental risk assessment.
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An international study published in Nature Biotechnology presents the combined results of a 2013 DREAM Challenge: a crowd-sourcing initiative to test how well the effects of a toxic compound can be predicted in different people. The study, which is relevant to public and occupational health, shows that computational methods can be used to predict some toxic effects on populations, although they are not yet sensitive enough to predict such effects in individuals. It also presents algorithms useful for environmental risk assessment.

If we could use computers to predict whether a compound would have a toxic effect on people, chemical safety testing would be a lot simpler. In a community-based challenge led and organised by scientists from EMBL-EBI, Sage Bionetworks, IBM, the University of North Carolina, and the NIH's National Institute of Environmental Health Sciences (NIEHS) and National Center for Advancing Translational Sciences (NCATS), hundreds of computational biologists from all over the world tried their hand at predicting the toxicities of environmental compounds that had potential adverse health effects.

The organisers used 884 lymphoblastoid cell lines that had SNP and gene-expression data available through the 1000 Genomes Project. They measured cellular toxicity of 156 compounds on these cell lines, which represented individuals from nine subpopulations throughout Europe, Africa, Asia and the Americas. Participants were challenged to develop algorithms that could predict toxic response in different individuals and across populations, all based on the structural attributes of the compounds.

"Our partners in the US took 1000 Genomes Project cell lines and treated them with different compounds, so we knew which compound had a toxic effect for each cell line," explains Julio Saez-Rodriguez, former Research Group Leader at EMBL-EBI now at RTWH Aachen University. "So we wanted to know, can you predict that? For a given compound, how will it affect people? For a given person, what compounds will they be sensitive to? This is really important for things like manufacturing, where people might be exposed to a new compound that hasn't been tested yet."

Dozens of teams submitted 179 predictions based on state-of-the-art computational models, and the organisers compared them against the experimental results. In the great tradition of crowd sourcing in bioinformatics, the organisers integrated the results, taking the best of each and forming a new tool to predict toxicity.

Predictions were slightly better than random for individuals, but the combined results could roughly predict population-level response to different compounds. However, improved accuracy is needed before it is possible to predict health risks associated with unknown compounds accurately.

One key benefit of the study is that it offers new methodologies for improvements in some areas of hazard evaluation and assessment.

"This partnership and challenge offer a way to provide both powerful scientific insights and meaningful public health impact by accelerating the pace of toxicity testing," says Allen Dearry, Director of the NIEHS Office of Scientific Information Management. "The winning computational models provide significant advances in our ability to predict toxicity risk for environmental chemicals and set the stage for future data-driven challenges and competitions in environmental health science."

"The ability of the top teams to predict population-level toxicity for unknown compounds -- based on similarities in chemical structure to known compounds -- far surpassed our anticipations," says Lara Mangravite, Director of Systems Biology at Sage Bionetworks. "This was a true case where crowd-sourcing the problem provided answers that would otherwise never have been found."

"We had hundreds of people from all over the world participating, from prestigious labs to people who don't even work in biology," says Federica Eduati, who carried out the analyses and is an EMBL interdisciplinary postdoctoral fellow (EIPOD) at EMBL-EBI. "You don't need to be at a top-tier institute to play with great data -- if you've got a good idea, you can share it."


Story Source:

Materials provided by European Bioinformatics Institute EMBL-EBI. Note: Content may be edited for style and length.


Journal Reference:

  1. Federica Eduati, Lara M Mangravite, Tao Wang, Hao Tang, J Christopher Bare, Ruili Huang, Thea Norman, Mike Kellen, Michael P Menden, Jichen Yang, Xiaowei Zhan, Rui Zhong, Guanghua Xiao, Menghang Xia, Nour Abdo, Oksana Kosyk, Federica Eduati, Lara M Mangravite, J Christopher Bare, Thea Norman, Mike Kellen, Michael P Menden, Stephen Friend, Gustavo Stolovitzky, Allen Dearry, Raymond R Tice, Ruili Huang, Menghang Xia, Anton Simeonov, Nour Abdo, Oksana Kosyk, Ivan Rusyn, Fred A Wright, Tao Wang, Hao Tang, Xiaowei Zhan, Jichen Yang, Rui Zhong, Guanghua Xiao, Yang Xie, Hao Tang, Jichen Yang, Tao Wang, Guanghua Xiao, Yang Xie, Salvatore Alaimo, Alicia Amadoz, Muhammad Ammad-ud-din, Chloé-Agathe Azencott, Jaume Bacardit, Pelham Barron, Elsa Bernard, Andreas Beyer, Shao Bin, Alena van Bömmel, Karsten Borgwardt, April M Brys, Brian Caffrey, Jeffrey Chang, Jungsoo Chang, Eleni G Christodoulou, Mathieu Clément-Ziza, Trevor Cohen, Marianne Cowherd, Sofie Demeyer, Joaquin Dopazo, Joel D Elhard, Andre O Falcao, Alfredo Ferro, David A Friedenberg, Rosalba Giugno, Yunguo Gong, Jenni W Gorospe, Courtney A Granville, Dominik Grimm, Matthias Heinig, Rosa D Hernansaiz, Sepp Hochreiter, Liang-Chin Huang, Matthew Huska, Yunlong Jiao, Günter Klambauer, Michael Kuhn, Miron Bartosz Kursa, Rintu Kutum, Nicola Lazzarini, Inhan Lee, Michael K K Leung, Weng Khong Lim, Charlie Liu, Felipe Llinares López, Alessandro Mammana, Andreas Mayr, Tom Michoel, Misael Mongiovì, Jonathan D Moore, Ravi Narasimhan, Stephen O Opiyo, Gaurav Pandey, Andrea L Peabody, Juliane Perner, Alfredo Pulvirenti, Konrad Rawlik, Susanne Reinhardt, Carol G Riffle, Douglas Ruderfer, Aaron J Sander, Richard S Savage, Erwan Scornet, Patricia Sebastian-Leon, Roded Sharan, Carl Johann Simon-Gabriel, Veronique Stoven, Jingchun Sun, Hao Tang, Ana L Teixeira, Albert Tenesa, Jean-Philippe Vert, Martin Vingron, Tao Wang, Thomas Walter, Sean Whalen, Zofia Wiśniewska, Yonghui Wu, Guanghua Xiao, Yang Xie, Hua Xu, Jichen Yang, Xiaowei Zhan, Shihua Zhang, Junfei Zhao, W Jim Zheng, Rui Zhong, Dai Ziwei, Stephen Friend, Allen Dearry, Anton Simeonov, Raymond R Tice, Ivan Rusyn, Fred A Wright, Gustavo Stolovitzky, Yang Xie, Julio Saez-Rodriguez. Prediction of human population responses to toxic compounds by a collaborative competition. Nature Biotechnology, 2015; DOI: 10.1038/nbt.3299

Cite This Page:

European Bioinformatics Institute EMBL-EBI. "Predicting the effect of toxic compounds on individuals: Crowdsourcing initiative for systems biomedicine." ScienceDaily. ScienceDaily, 10 August 2015. <www.sciencedaily.com/releases/2015/08/150810111037.htm>.
European Bioinformatics Institute EMBL-EBI. (2015, August 10). Predicting the effect of toxic compounds on individuals: Crowdsourcing initiative for systems biomedicine. ScienceDaily. Retrieved May 23, 2017 from www.sciencedaily.com/releases/2015/08/150810111037.htm
European Bioinformatics Institute EMBL-EBI. "Predicting the effect of toxic compounds on individuals: Crowdsourcing initiative for systems biomedicine." ScienceDaily. www.sciencedaily.com/releases/2015/08/150810111037.htm (accessed May 23, 2017).

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