The One Codex Blog

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.

Whether surveying the human microbiome, environmental sites, or other microbial communities, researchers have generally adopted one of two approaches – sequencing all of the DNA in a sample (WGS) or focusing on a specific marker gene (such as the 16S rDNA locus). While I tend to advocate for analyzing total DNA (via WGS), 16S sequencing remains popular for large sequencing projects due to its cost effectiveness.

Today, I’m excited to announce improved support for 16S analysis on the One Codex platform – which we hope will both make high quality 16S analysis more accessible, while also making it easier for researchers to integrate sample data from both 16S and shotgun sequencing projects.

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.

We get the question from people jumping into metagenomic sequencing for the first time, “How many reads do I need per sample?”

The way that I like to break this up is by thinking about what you’re hoping to get from an experiment:

Pathogen detection

If you’re looking for a low abundance organism, increasing the depth of sequencing will linearly improve (lower) the limit of detection. A good rule of thumb is that you need 100-1000 reads to confidently identify an organism (this varies widely by the organism you’re looking for, but it’s a reasonable range). A little mental math tells us that sequencing 1 million reads will give us a limit of detection of 0.1-0.01%, 10 million reads will give us an LoD of 0.01-0.001%, so on and so forth. So plug in your desired LoD and you’re set.