The One Codex Blog

Scientists that study the microbiome generally use two different methods to analyze samples – sequencing all of the DNA in a sample (whole genome sequencing) or targeting a specific marker gene (e.g., 16S, 18S, ITS). While whole genome sequencing (WGS) enables high-resolution taxonomic and functional characterization of microbiome samples, 16S sequencing is a cost effective technique for broad community surveys across large numbers of samples. Today, we’re excited to announce that One Codex is launching a powerful new tool for 16S and other amplicon sequencing – making high-quality, reference-based analysis of marker gene studies easier, faster, and more accessible.

Today, we’re extremely excited to announce several new features we’ve been working on for the past few months. Collectively, these should make it both easier and faster to perform custom, large-scale analyses, explore your data, and build applications atop the One Codex platform:

• A new easier-to-use, more powerful API (read the docs)
• A new version of our command-line interface and an accompanying Python client library for quickly getting started with the new API (take a peek on Github)
• And last but not least – interactive notebooks built directly into the platform!

The new Notebooks feature allows you to launch secure Jupyter (née IPython) notebooks automatically configured for access to your samples and analyses on One Codex. Internally, we’ve already found these incredibly useful for both quick, flexible explorations of metagenomic data as well as more sophisticated, formal analyses.

Today we’d like to tell you about a new feature on One Codex that allows you to run new analyses against your samples, including AMR gene panels and whole-genome alignments.

Running New Analyses

When samples are uploaded to One Codex, we automatically classify them using the One Codex Database of ~40K complete microbial genomes. However, metagenomic classification is just one of a range of microbial analysis tools provided by the One Codex platform. While in the past we’ve configured additional analyses to run automatically where appropriate (e.g., MLST for common bacterial isolates) or at the request of users, our new Run Analysis page allows you to run an in silico panel or perform whole-genome alignments against any of your samples.

Today we’d like to tell you about a set of panels on One Codex designed to help detect antimicrobial resistance (AMR) in two important foodborne pathogens – Escherichia coli and Campylobacter coli / C. jejuni.

AMR in E. coli and Campylobacter coli / C. jejuni

Antibiotic resistant infections are a huge challenge for modern healthcare, and there is a global effort underway to improve our identification and treatment of these hardy infections. Both E. coli and C. coli / C. jejuni are dangerous foodborne pathogens that can be highly resistant to antibiotics. These panels are designed to help microbiologists use genomic sequencing to track the spread of foodborne illness, and predict what antibiotics may not be effective for treating these pathogens.

Today we’re excited to announce a major update to One Codex, which includes both improvements to our core metagenomics pipeline and an expansion of our reference database. Along with this update, we’ve also re-analyzed all samples previously uploaded to One Codex (all older analyses of course remain available).

Improved classifier: Better filtering, while maintaining sensitivity

Over the past few years, a number of new k-mer based metagenomic classifiers tools have been developed, including Kraken, GOTTCHA, CLARK, and our own. These methods have enabled ever-larger reference libraries and provided extremely sensitive detection. As a consequence of their design, however, they have also been more prone to false positives than more conservative alignment- or marker-based approaches.