One Codex Achieves Highest Overall Score in precisionFDA CFSAN Challenge 🥇
Identifying specific strains and mixtures of strains in complex metagenomic samples is a key challenge in epidemiology, environmental microbiology, and live biotherapeutics development (LBPs). We’ve long been working on this problem and are excited to announce that our in-house strain-calling pipeline recently achieved the highest overall score in the precisionFDA CFSAN Pathogen Detection Challenge. We’re still continuing to hone and test several approaches, but are excited to see that each performed extremely well across the 25+ submissions:
Figure 1. One Codex achieved the highest overall score in the precisionFDA CFSAN Pathogen Detection Challenge (sum of individual scores)
We’ve spent a lot of R&D time building what we believe to be the best strain identification workflow for microbiome bioinformatics. Although we’ve long supported strain identification at the read-level in our metagenomics classifier, our next release will extend these capabilities to better detect individual strains, assign confidences, and deal with complex strain mixtures.[^1] These capabilities will help with problems ranging from foodborne pathogen detection to tracking strains in healthy microbiome samples to monitoring the delivery of LBPs.
We’re working hard to make our improved strain pipeline available on One Codex and you can expect a larger announcement later this year. In the meantime, if you regularly work with strains from simple or complex mixtures, we’d love to hear more about your use case and needs! Please feel free to reach out to us by email or on Twitter @onecodex.
– The One Codex Team
1 The precisionFDA Challenge tested detection capabilities for a single strain against a metagenomic background. An even greater challenge is detection and deconvolution of several closely related strains. We've built our new pipeline with this goal in mind and believe that better strain mixture characterization will open the door to interesting new research avenues.