The application of next-generation sequencing in the study of microbial communities has fueled the rapid growth of interest in microbiome research. However, difficulties with the accuracy of computational analyses of these complex datasets have limited the translation of microbiome research into practical applications.

Two community challenges focus on one common goal: evaluate and advance the ability of computational methods to accurately detect known microbial strains in metagenomic samples in the context of human health and food safety.

Mosaic Community Challenge: Clinical Strain Detection

Challenge closing date extended to June 25, 2018.

It is critical to accurately determine the type and quantity of microbes in a sample at the strain-level in order to bring safe and effective products to market, and to precisely monitor their status within the human body. Join the Challenge that aims to speed the translation of microbiome science into novel products by tracking the presence of certain known strains in a sample!

Challenge participants will contribute to the improvement of strain-level microbial analysis methods while also testing industry or proprietary tools in a secure and collaborative environment. Insights from the Challenge will provide an objective comparison of the performance of different tools, helping to benchmark and advance computational methods for future targeted strain detection.

This Challenge is hosted by Janssen Research & Development, LLC.




PrecisionFDA CFSAN Pathogen Detection Challenge

Join the Challenge that utilizes metagenomics to help improve pathogen detection and traceback, ultimately impacting the future of food safety!

Challenge participants will develop and use bioinformatics pipelines to identify the MLST (Multilocus Sequence Typing) type and Salmonella strains in metagenomic samples. Participants will be provided with a series of metagenomic samples and will be evaluated on their ability to accurately identify MLST types and Salmonella strains in the samples. Accurately identifying MLST types and Salmonella strains allows for faster pinpointing of the source of an outbreak by directly linking pathogens identified in a patient with pathogens found in food.