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’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.

Ok, anthrax might sound a bit scary, but this is a story about something that should make you feel good.

Wait, is that really Anthrax?

Back in the Spring of 2015, researchers studying the microbial ecology of the built environment generated a very large amount of genomic data from microbes found in the New York City subway system. To give you some idea of the magnitude, they generated 10.4 billion sequence reads across 1,457 samples. That’s a lot of microbiome data.

Today we added automated Multi-Locus Sequence Typing (MLST) to One Codex.

MLST is a powerful epidemiological tool that is based on curated collections of conserved mutations in core marker genes. This common reference standard is used to differentiate closely-related isolates of the same species, with many common species having hundreds or thousands of defined MLST profiles.

Datasets that are identified as being isolates or single-genome assemblies will be automatically analyzed and tagged with the detected ST label. This MLST tagging is implemented for the most commonly analyzed bacteria, including E. coli, S. enterica, and L. monocytogenes.¹

Today is a big day for One Codex, where we’re launching out of beta!

This marks the introduction of a number of new features (and many under-the-hood improvements), and incorporates much of the feedback our users have provided this year. You will notice some changes to our website, and the platform can now be found at app.onecodex.com (note that all previous links to the beta site will still work).

Today we’re launching our new sample comparison tool on the One Codex Beta Platform.

The tool enables quick selection and comparison of any of your samples and displays the abundance of taxonomic groups in each sample as a stacked bar graph:

The comparison view supports large side-by-side comparisons, viewing data at a particular taxonomic level and/or abundance, filtering to specific clades, and relative vs. absolute scaling modes.

We’re happy to announce that we’ve just released larger, improved microbial reference libraries on One Codex.

More data

The RefSeq Database now includes over 7,000 reference and representative genomes from NCBI, while the One Codex Database holds nearly 34,000 different bacterial, viral, fungal, and archeal genomes. This is more than a 45% and 20% increase, respectively, from the last releases of the RefSeq and One Codex databases. As with all of our data releases, you should continue to see improvements in the specificity of your analyses by using our more comprehensive reference libraries.

This week, we’re excited to announce a few improvements to help you to better organize and share your data on the One Codex beta platform.

First, you can now make individual samples public, at which point you can share an analysis with anyone (and, of course, you’re always able to make the sample private again too). Here’s a sample public analysis.

Second, we’ve added the ability to both sort and filter your samples in the main datasets view. This is particularly useful for some of our users who are beginning to accumulate large numbers of samples.