Identify redundant (highly similar) genomes.
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This program uses the user’s similarity metric of choice to identify genomes that are highly similar to each other, and groups them together into redundant clusters. The program finds representative sequences for each cluster and outputs them into fasta files.
You have two options for the input to this program:
the results of anvi-compute-genome-similarity (a genome-similarity directory). If you used
pyANI when you ran anvi-compute-genome-similarity, provide this using the parameter
--ani-dir; if you used sourmash, use the parameter
an internal-genomes, external-genomes or a series of fasta files (each of which represents a genome), in which case anvi’o will run anvi-compute-genome-similarity for you. When providing these inputs, you can also provide any of the parameters that anvi-compute-genome-similarity can take, including the
--program you want to use (out of PyANI, fastANI, sourmash) and their parameters. Details about all of this can be found in the help menu for anvi-compute-genome-similarity.
By default, the output of this program is a directory containing two descriptive text files (the cluster report and fasta report) and a subdirectory called
-The cluster report describes is a tab-delimited text file where each row describes a cluster. This file contains four columns: the cluster name, the number of genomes in the cluster, the representative genome of the cluster, and a list of the genomes that are in the cluster. Here is an example describing 11 genomes in three clusters:
DEREPLICATION-0.97 $ head CLUSTER_REPORT.txt cluster size representative genomes cluster_000001 1 G11_IGD_MAG_00001 G11_IGD_MAG_00001 cluster_000002 8 G11_IGD_MAG_00012 G08_IGD_MAG_00008,G33_IGD_MAG_00011,G01_IGD_MAG_00013,G06_IGD_MAG_00023,G03_IGD_MAG_00021,G05_IGD_MAG_00014,G11_IGD_MAG_00012,G10_IGD_MAG_00010 cluster_000003 2 G03_IGD_MAG_00011 G11_IGD_MAG_00013,G03_IGD_MAG_00011
GENOMES contains fasta files describing the representative genome from each cluster. For example, if your original set of genomes had two identical genomes, this program would cluster them together, and the
GENOMES folder would only include one of their sequences.
-The fasta report describes the fasta files contained in the subdirectory
GENOMES. By default, this describes the representative sequence of each of the final clusters. It tells you the genome name, its source, its cluster (and the representative sequence of that cluster), and the path to its fasta file in
GENOMES. So, for the example above, the fasta report would look like this:
DEREPLICATION-0.97 $ head FASTA_REPORT.txt name source cluster cluster_rep path G11_IGD_MAG_00001 fasta cluster_000001 G11_IGD_MAG_00001 GENOMES/G11_IGD_MAG_00001.fa G11_IGD_MAG_00012 fasta cluster_000002 G11_IGD_MAG_00012 GENOMES/G11_IGD_MAG_00012.fa G03_IGD_MAG_00011 fasta cluster_000003 G03_IGD_MAG_00011 GENOMES/G03_IGD_MAG_00011.fa
You can also choose to report all genome fasta files (including redundant genomes) (with
--report-all) or report no fasta files (with
--skip-fasta-report). This would change the fasta files included in
GENOMES and the genomes mentioned in the fasta report. The cluster report would be identical.
You are required to set the threshold for two genomes to be considered redundant and put in the same cluster.
For example, if you had the results from an anvi-compute-genome-similarity run where you had used
PyANI and wanted the threshold to be 90 percent, you would run:
anvi-dereplictate-genomes --ani-dir genome-similarity \ -o path/to/output \ --similiarity-threshold 0.90
If instead you hadn’t yet run anvi-compute-genome-similarity and instead wanted to cluster the genomes in your external-genomes file with similarity 85 percent or more (no fasta files necessary) using sourmash, you could run:
anvi-dereplictate-genomes -e external-genomes \ --skip-fasta-report \ --program sourmash \ -o path/to/output \ --similiarity-threshold 0.85
You can change how anvi’o picks the representative sequence from each cluster with the parameter
--representative-method. For this you have three options:
Qscore: picks the genome with highest completion and lowest redundancy
length: picks the longest genome in the cluster
centrality(default): picks the genome with highest average similiarty to every other genome in the cluster
You can also choose to skip checking genome hashes (which will warn you if you have identical sequences in separate genomes with different names), provide a log path for debug messages or use multithreading (relevant only if not providing
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.