One Codex is a platform that lets you analyze genomic data from microbial samples. If you don't have an account, you can sign up for free now.

Just enter some basic contact info, select a password, and start analyzing your data!
​    

You can add data into your account on the One Codex Platform by navigating the Upload page, which lets you drag and drop FASTA or FASTQ files directly into One Codex.

Don't have a file handy? Feel free to use this file for the rest of the tutorial: sample.fastq.

From your web browser, you can upload FASTA and FASTQ files up to 20GB in size, optionally compressed with gzip. For very large file uploads (or uploading many large files at once), we recommend using our command-line client. Please note that we replace spaces in filenames with underscores.
​     

After uploading, you should see your FASTQ or FASTA file appear on the Samples page. To view the results for any given sample, click on the green "View Results" button.
​

The results page shows the organisms that were detected from the One Codex Database of ~140K complete genomes (bacteria, viruses, fungi, protists, and archaea). Below is an example analysis on One Codex:

The results page summarizes the organisms that were detected in several ways, and includes a number of different interactive controls that allow you to search through the organisms by abundance, name, or taxonomic ranking. There are also a few different download buttons that allow you to save the raw data, summary tables, and publication-grade images presented on the page.

To learn more, we have a complete walkthrough of the results on this page, as well as more details on the underlying analysis.
​    

Now that you have seen the results for individual samples, you can compare abundances of microbes between samples using our Quick Compare feature. Learn more about that here.

For more advanced options on comparing samples in One Codex, take a look at Custom Plots, which allows you to plot some typical microbiome metrics for your samples, including grouping using your metadata. Learn how to do that here. You can also read more about whole-genome clustering and analysis notebooks for further advanced analyses.

Now that you've had an introduction feel free to start analyzing data! When more questions come up, feel free to dive deeper into our technical documentation, or reach out to us directly. Happy researching!