STAMP preparation - LangilleLab/microbiome_helper GitHub Wiki

There are many possible ways to analyze amplicon sequencing data. STAMP is one tool that can be used that requires no background in scripting languages. This tool is very useful for visualizing microbiome data, but note that there are much better alternatives for conducting statistical tests with compositional data (such as ANCOM and the ALDEx2 R package).

1: Install STAMP (this has to be installed on a local machine, not a server since it has a graphical interface).

• Windows: Download and install.
• Mac or Linux:

conda install -c bioconda stamp

Then simply type the following to start (try STAMP if the below command fails):

stamp

2: Prep input files for STAMP. If you just ran the QIIME2 SOP you can prep the files using the below commands (the scripts are part of the Microbiome Helper repository). Note that there are a couple of file manipulations included here since certain SILVA taxonomy labelling problems need to be fixed for STAMP. Also, the --replace_ambig_letter_format option is for taxa ids that start with "p__", "o__", etc. If your taxa ids instead start with "D_0__", "D_1__", etc. then you can use the --replace_ambig_D_format option.

biom_to_stamp.py -m taxonomy \
                 deblur_output_exported/feature-table_w_tax.biom \
                 > deblur_output_exported/feature-table_w_tax.spf

fix_spf.py -i deblur_output_exported/feature-table_w_tax.spf \
           -o deblur_output_exported/feature-table_w_tax_final.spf \
           --replace_ambig_letter_format

3: Start up STAMP and load in feature-table_w_tax_final.spf as the Profile File, and your metadata file as the Group metadata file. You can now explore the data overall with heatmaps and ordination tools like PCA (although note that QIIME 2 already outputs more valid ordinations) or alternatively compare boxplots of the relative abundance of individual features.