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The summarize_taxa.py script provides summary information of the representation of taxonomic groups within each sample. It takes an OTU table that contains taxonomic information as input. The taxonomic level for which the summary information is provided is designated with the -L option. The meaning of this level will depend on the format of the taxon strings that are returned from the taxonomy assignment step. The taxonomy strings that are most useful are those that standardize the taxonomic level with the depth in the taxonomic strings. For instance, the Greengenes database uses the following levels: Level 1 = Kingdom (e.g Bacteria), Level 2 = Phylum (e.g Actinobacteria), Level 3 = Class (e.g Actinobacteria), Level 4 = Order (e.g Actinomycetales), Level 5 = Family (e.g Streptomycetaceae), Level 6 = Genus (e.g Streptomyces), Level 7 = Species (e.g mirabilis). By default, the relative abundance of each taxonomic group will be reported, but the raw counts can be returned if -a is passed.
By default, taxa summary tables will be output in both classic (tab-separated) and BIOM formats. The BIOM-formatted taxa summary tables can be used as input to other QIIME scripts that accept BIOM files.
Usage: summarize_taxa.py [options]
There are two possible output formats depending on whether or not a mapping file is provided with the -m option. If a mapping file is not provided, a table is returned where the taxonomic groups are each in a row and there is a column for each sample. If a mapping file is provided, the summary information will be appended to this file. Specifically, a new column will be made for each taxonomic group to which the relative abundances or raw counts will be added to the existing rows for each sample. The addition of the taxonomic information to the mapping file allows for taxonomic coloration of Principal coordinates plots in Emperor. As described in the Emperor documentation, principal coordinates plots can be dynamically colored based on any of the metadata columns in the mapping file. Dynamic coloration of the plots by the relative abundances of each taxonomic group can help to distinguish which taxonomic groups are driving the clustering patterns.
Summarize taxa based at taxonomic levels 2, 3, 4, 5, and 6, and write resulting taxa tables to the directory ‘./tax’
summarize_taxa.py -i otu_table.biom -o ./tax
Summarize taxa based at taxonomic levels 2, 3, 4, 5, and 6, and write resulting mapping files to the directory ‘./tax’
summarize_taxa.py -i otu_table.biom -o tax_mapping/ -m Fasting_Map.txt