PEMA supports multiple options for running 16S or/and 18S rRNA data.
You may choose between OTUs clustering or ASVs inference ;PEMA supports both.
parameters.tsv file, you will find the
clusteringAlgoFor16S_18SrRNA parameter. By setting this as
algo_vsearch PEMA will use the vsearch algorithm to get OTUs.
Otherwise, by setting this as
algo_swarm PEMA implements the swarm algorithm to infer ASVs.
Silva includes two different Silva versions you may choose.
Silva versions besides the increased number of available sequences, they may include different taxonomic frameworks.
For the taxonomy assignment step, PEMA supports two ways when it comes to 16S/18S rRNA data.
alignment, PEMA will use the CREST algorithm and the Silva version you selected and will return you an alignment based taxonomy assignment.
On the contrary, by setting this parameter as
phylogeny PEMA will use a reference tree of 1.000 consensus taxa we have built by using Silva 128, the RAxML-ng and the EPA algorithms to return you a phylogeny based taxonomy assignment which you will be able to view via tools such as iTOL.
Here is the workflow we used to build PEMA’s reference tree.
You may ask for PEMA to move on after the taxonomy step and run a
phyloseq analysis. Phyloseq is a R package for further exploring microbiome profiles.
To do so, you need to set the
phyloseq parameter in the
parameters.tsv file as
Yes and add the
phyloseq_in_PEMA.R script in the
analysis_directory with all the rest of your input~
You may get a version of this script here. As mentioned on the script, the first sections need to be exactly like this.
However, you may add or remove
phyloseq modules according to your needs.
You always need to remember the
metadata.csv file, you may find an example here.
You need to add your own metadata there keeping the format of the file as it is!
You always have to keep the names of these scripts exactly as they are, otherwise PEMA will return you an error!
Plots and all the phyloseq-related output will be saved in a seperate subdirectory called