PICRUSt2 Full pipeline (version 2.5.3+galaxy0)

Sequence placement options

Sequence placement options 0

Hidden state prediction (HSP) options

Hidden state prediction (HSP) options 0

Metagenome prediction options

Metagenome prediction options 0

Infer pathways:

Customized table mapping of pathways to reactions:

Default mapping file is Maps MetaCyc reactions to prokaryotic MetaCyc pathways

Run MinPath to identify which pathways are present as a first pass:

Perform gap filling before predicting pathway abundances:

Regroup input gene families to reactions:

Mapfile of ids to regroup gene families to before running MinPath:

Keep empty to use the default mapping file (ec_level4_to_metacyc_rxn.tsv contained in PICRUSt2)

Calculate pathway abundances for each individual predicted genome:

The output will be the predicted pathway abundance contributed by each individual sequence. This is in contrast to the default stratified output, which is the contribution to the community-wide pathway abundances. Note this will greatly increase the runtime. Experimental pathway coverage stratified by contributing sequence will also be output when --coverage is set

Calculate pathway coverages as well as abundances:

Experimental and only useful for advanced users

Functional database to use as reactions for inferring pathway abundances:

Default functional database:

Keep intermediate files of:

What it does

PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) is a tool for predicting functional abundances based only on marker gene sequences.

Read more about the tool: https://github.com/picrust/picrust2/wiki

PICRUSt2 full pipeline

Run sequence placement with EPA-NG and GAPPA to place study sequences (i.e. OTUs and ASVs) into a reference tree. Then runs hidden-state prediction with the castor R package to predict genome for each study sequence. Metagenome profiles are then generated, which can be optionally stratified by the contributing sequence. Finally, pathway abundances are predicted based on metagenome profiles. By default, output files include predictions for Enzyme Commission (EC) numbers, KEGG Orthologs (KOs), and MetaCyc pathway abundances. However, the tool enables users to use custom reference and trait tables to customize analyses.

Note

The standard pipeline will generate metagenome predictions for 16S rRNA gene data.

Input

A FASTA of amplicon sequences variants (ASVs; i.e. your representative sequences, not your raw reads)
A BIOM table of the abundance of each ASV across each sample.

Output

Output tree with placed study sequences.
Metagenome Predictions
Pathway level predictions