Estimates taxonomy at genome and metagenome level. This program is the entry point to estimate taxonomy for a given set of contigs (i.e., all contigs in a contigs database, or contigs described in collections as bins). For this, it uses single-copy core gene sequences and the GTDB database.
🔙 To the main page of anvi’o programs and artifacts.
profile-db contigs-db scgs-taxonomy collection bin metagenomes
This program quickly makes taxonomy estimates for genomes, metagenomes, or bins stored in your contigs-db.
This is the final step in the scg-taxonomy workflow (described in its entirety here). Before running this program, you’ll need to have run both anvi-setup-scg-taxonomy and anvi-run-scg-taxonomy on the contigs-db you’re working with for this project.
This tutorial also includes a comprehensive overview of what this program can do. See that page for more information on all of the features described below.
Keep in mind that this uses single-copy core genes and their hits in GTDB, so it will not work well in bins with low completion or for Eukaryotic organisms.
This program is implicitly run in the interactive interface, when you turn on “Realtime taxonomy estimation for bins (whenever possible).” So, if you’ve ever wondered where those estimates were coming from, now you know.
So, what can this program do?
By default, this program wll assume your contigs-db contains only one genome, and will try to use the single-copy core genes (that were associated with taxonomy when you ran anvi-run-scg-taxonomy) to try to identify the taxonomy of your genome.
When you run
anvi-estimate-scg-taxonomy -c contigs-db
It will give you the best taxonomy hit for your genome. If you would like to see how it got there (by looking at the hits for each of the single-copy core genes), just use the
--debug flag to see more information, as so:
anvi-estimate-scg-taxonomy -c contigs-db \ --debug
By running this program in metagenome mode, it will assume that your contigs-db contains multiple genomes and will try to give you an overview of the taxa within it. To do this, it will determine which single-copy core gene has the most hits in your contigs (for example
Ribosomal_S6), and then will look at the taxnomy hits for that gene across your contigs. The output will be this list of taxonomy results.
anvi-estimate-scg-taxonomy -c contigs-db \ --metagenome-mode
If you want to look at a specific gene (instead of the one with the most hits), you can also tell it to do that. For example, to tell it to look at Ribosomal_S9, run
anvi-estimate-scg-taxonomy -c contigs-db \ --metagenome-mode \ --scg-name Ribosomal_S9
If you provide a profile-db or single-profile-db, then you’ll be able to look at the relative abundance of your taxonomy hits (through a single-copy core gene) across your samples. Essentially, this adds additional columns to your output (one per sample) that descrbe the relative abundance of each hit in each sample.
Running this will look something like this,
anvi-estimate-scg-taxonomy -c contigs-db \ --metagenome-mode \ --p profile-db \ --compute-scg-coverages
For an example output, take a look at this page.
This program basically looks at each of the bins in your collection as a single genome and tries to assign it taxonomy information. To do this, simply provide a collection, like this:
anvi-estimate-scg-taxonomy -c contigs-db \ --C collection
You can also look at the relative abundances across your samples at the same time, by running something like this:
anvi-estimate-scg-taxonomy -c contigs-db \ --C collection \ --p profile-db \ --compute-scg-coverages
You can even use this program to look at multiple metagenomes by providing a metagenomes artifact. This is useful to get an overview of what kinds of taxa might be in your metagenomes, and what kinds of taxa they share.
anvi-estimate-scg-taxonomy --metagenomes metagenomes \ --output-file-prefix EXAMPLE
will give you an output file containing all taxonomic levels found and their coverages in each of your metagenomes.
For a concrete example, check out this page.
Edit this file to update this information.
Are you aware of resources that may help users better understand the utility of this program? Please feel free to edit this file on GitHub. If you are not sure how to do that, find the
__resources__ tag in this file to see an example.