enrichM has three main purposes

1. To provide a very basic pipeline for annotating population genomes and assemblies.
2. To compare user-defined groups of genomes and identify genes or metabolic pathways that are enriched within and between groups.
3. To construct metabolic networks from annotated population genomes.

enrichM is currently in a state of flux and currenty does not have unit tests so while you're welcome to use it, do so at your own risk. Currently, there are the following sub-commands:

  Genome annotation
    annotate    -> Annotate contigs with KO, PFAM, and TIGRfam IDs

  Enrichment analysis
    classify    -> Interpret KO annotations and report completeness of KEGG modules that a genome encodes
    enrichment  -> Compare groups of genomes and determine what groups of genes or functions are enriched in one set vs another.
    
  Network analysis
    pathway     -> Generate a Cytoscape-readable metabolic network from specific KEGG module or compounds.
    explore     -> Explore a metabolic network from a given compound            

EnrichM has the following non-python dependencies:

• fxtract >= 2.3
• hmmer >= 3.1b
• seqmagick >= 0.6.1
• diamond >= 0.9.6
• prodigal >= 2.6.3

This can be done, and super-user privileges may be needed:

pip install enrichm

EnrichM will be available on DockerHub soon (Sorry!).

EnrichM will also be packaged using GNU Guix soon (Sorry!).

Before running enrichm, you'll need to download the back-end databases. This can be done using a command in enrichm. You may need super user privelages:

enrichm data

This should take approx. 15 minutes. To check for updates and install updates, simply run the same command.

Annotate is a function that allows you to annotate your population genomes with KO, PFAM, TIGRFAM. The result will be a .gff file for each genome, and a frequency matrix for each annotation type where the rows are annotation IDs and the columns are genomes.

See the annotate help page for more

Classify quickly reads in KO annotations in the form of a matrix (KO IDs as rows, genomes as columns) and determines which KEGG modules are complete. Annotation matrices can be generated using the annotate function.

See the classify help page for more

Enrichment will read in KO or PFAM annotations in the form of a matrix (IDs as rows, genomes as columns) and a metadata file that separates genomes into groups to compare, and will run some basic stats to determine the enrichment of modules or pfam clans between and within the groups.

See the enrichment help page for more

Pathway reads in a KO matrix and generates a Cytoscape-readable metabolic network and metadata file. Only reactions that are possible given the KOs present in the input matrix are shown, and the modules and reactions that are included in the output can be customized.

See the pathway help page for more

Explore is similar to pathway, but rather than generating a specified pathway it will start from a given query compound ID, and explore the possible reactions that use that compound given the enzymes present in the input KO matrix.

See the explore help page for more