The One Codex Blog

Announcing SMS-seq: The world’s first protocol for ultra-portable microbial sequencing data analysis and taxonomic identification.

We heard you! One Codex’s most-requested feature is here. You can now upload your paired-end sequencing data straight from your web browser, and get started with your microbiome analysis faster than ever.

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:

Today at the Advances in Genome Biology & Technology (AGBT) conference, we are excited to unveil the largest searchable database of microbial genomes. Curated from its larger collection of hundreds of thousands of genomes, the latest One Codex Database includes >80,000 genomes and provides unprecedented sensitivity and specificity for metagenomic applications.

The latest One Codex Database enables:

• Highly sensitive identification of microbes in complex samples
• Precise quantification of microbial abundances from whole genome sequencing (WGS) data
• Community-wide characterization of complex microbial samples, including the human microbiome

Spanning the tree of life

Our latest release includes over 80,000 genomes, representing more than 43,000 distinct species and 69,000 strains across all microbial domains. We’ve increased coverage of all branches of the tree of life, with nearly 1,700 fungal genomes, another 1,700 archaeal genomes, 27,000 viruses, and more than 53,000 bacteria. Collectively, this database provides the most comprehensive snapshot of complex microbial samples.

When analyzing microbiome data, it’s very important to know that you are detecting the microbes that are truly present and that the predicted abundances are accurate.¹ However, it can be a lot of work to test and validate microbiome analysis tools across a wide range of conditions. We are very grateful to a group of academic researchers from Weill Cornell Medicine, UC-Riverside, IBM, University of Vermont, HudsonAlpha, & Drexel University who performed those evaluations and contributed them to the community. Today we’re happy to present some results from an independent academic evaluation.² Using the same datasets and accuracy metrics shows the performance of One Codex is superior to a range of other tools for microbiome analysis.