The purpose of this program is to affiliate tRNA gene sequences in an anvi'o contigs database with taxonomic names. A properly setup local tRNA taxonomy database is required for this program to perform properly. After its successful run,
anvi-estimate-trna-taxonomy will be useful to estimate taxonomy at genome-, collection-, or metagenome-level)..
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This program associates the tRNA reads found in your contigs-db with taxonomy information.
Once these associations are stored in your contigs-db (represented by a trna-taxonomy artifact), you’ll be able to run anvi-estimate-trna-taxonomy to use the associations to estimate taxonomy on a larger scale (i.e. for a genome or metagenome).
To run this program, you’ll need to have set up two things:
This program will then go through the tRNA hits in your contigs database and search them against the sequences in the GTDB databases that you downloaded to assign them taxonomy.
The following is a basic run of this program:
anvi-run-trna-taxonomy -c contigs-db
If you have set up the two requirements listed above, this should run smoothly.
When changing these parameters, it might be a good idea to run anvi-estimate-trna-taxonomy with the
--debug flag so that you can see what your results look like under the hood.
--max-num-target-sequences: the number of hits that this program considers for each tRNA sequence before making a final decision for the taxonomy association. The default is 100, but if you want to ensure that you have accurate data at the expense of some runtime, you can increase it.
--min-percent-identity: the minimum percent alignment needed to consider something a hit. The default is 90, but if you’re not getting any hits on a specific sequence, you can decrease it at the risk of getting some nonsense results.
Finally, this program does not usually have an output file, but if desired you can add the parameter
--all-hits-output-file to store the list of hits that anvi’o looked at to determine the consensus hit for each sequence.
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.