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* Please name fasta files of sample reads with <sample id>.fa<*> and place them into one directory without any other file in that directory (for example, no hidden files that the operating system may generate, are allowed in that directory)
* Note: When making your QIIME Metadata file, the sample id's must match the fasta file prefix names
* Fasta files of reads must have a suffix that starts with .fa (e.g.: .fasta and .fa are valid while .fna is NOT)
* Modify the top of the Matlab/Octave scripts for <input_directory>, <output_directory>, <output_filename>, and <training_database_filename>
To use with QIIME, one must run the QIIME conversion tool on our OTU table output:
convert_biom.py -i <quikr_otu_table.txt> -o <quikr_otu>.biom --biom_table_type="otu table"
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4-step QIIME procedure after using Quikr to obtain 3D PCoA graphs:
(Note: Our code works much better with WEIGHTED Unifrac as opposed to
Unweighted.)
Pre-requisites: 1) <quikr_otu_table.txt>, 2) the tree of the database sequences that were used (e.g.
rdp7_mafft.fasttree, gg_94_otus_4feb2011.tre, etc.), and 3) your-defined <qiime_metadata_file.txt>
1. convert_biom.py -i <quikr_otu_table.txt> -o <quikr_otu>.biom --biom_table_type="otu table"
2. beta_diversity.py -i <quikr_otu>.biom -m weighted_unifrac -o beta_div -t <tree file (example: rdp7_mafft.fasttree)>
3. principal_coordinates.py -i beta_div/weighted_unifrac_<quikr_otu>.txt -o <quikr_otu_project_name>_weighted.txt
4. make_3d_plots.py -i <quikr_otu_project_name>_weighted.txt -o <3d_pcoa_plotdirectory> -m <qiime_metadata_file>
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