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The One Codex Blog

Running new analyses & whole-genome alignments

One Codex Team  on September 26, 2016
new features , announcements , metagenomics , antibiotics

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.

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Detecting Antimicrobial Resistance in Foodborne Pathogens

Sam Minot  on August 18, 2016
antibiotics , food safety , public health

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.

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2.0!

Nick Greenfield  on April 25, 2016
new features , metagenomics , announcements , reference data

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.

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New features: Whole genome clustering and BaseSpace integration

Nick Greenfield  on February 11, 2016
epidemiology , clustering , BaseSpace , Illumina , isolates , metagenomics

Today we’re happy to announce two new features for all users on One Codex: whole genome clustering and integration with Illumina’s BaseSpace. The first opens the door to new types of analyses, while we hope the second will allow many to spend less time moving their data around and more time exploring it!

Whole (meta)genome clustering

In addition to our previous sample comparison tool, we’re very excited to announce that One Codex now supports arbitrary, interactive exploration and clustering of your isolates and metagenomic samples. This cluster view (login/free registration required) enables rapid, reference-free exploration and comparison of NGS samples, often both highlighting expected similarities and revealing important inter-sample differences.

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Cautious Metagenomics: A Story of Anthrax in the NYC Subway

Sam Minot  on January 11, 2016
new features , metagenomics , metasub , anthrax

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.

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