anvi-compute-functional-enrichment-in-pan

A program that computes functional enrichment within a pangenome..

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Authors

Can consume

misc-data-layers pan-db genomes-storage-db functions

Can provide

functional-enrichment-txt

Usage

This program computes functional enrichment within a pangenome and generates a functional-enrichment-txt file.

For its sister programs, see anvi-compute-metabolic-enrichment and anvi-compute-functional-enrichment-across-genomes.

Please also see anvi-display-functions which can both calculate functional enrichment, AND provide an interactive interface to display the distribution of functions.

Enriched functions in a pangenome

To execute this program, you must provide a pan-db and genomes-storage-db pair, along with misc-data-layers that associates genomes in your pangenome database with categorical data. The program will identify functions that are enriched in each group (i.e., functions associated with gene clusters that are characteristic of the genomes in that group).

Note that your genomes-storage-db must contain at least one functional annotation source for this analysis to work.

This analysis helps identify functions associated with specific groups of genomes in a pangenome and determines the functional core of your pangenome. For example, in the Prochlorococcus pangenome (analyzed in the pangenomics tutorial, where you can find more information about this program), this program identifies that Exonuclease VII is enriched in the low-light genomes and not in high-light genomes. The output file provides various statistics about the confidence of this functional association.

How does it work?

The analysis performed by this program can be broken down into three steps:

1. Determine groups of genomes. The program uses a misc-data-layers variable (containing categorical, not numerical, data) to partition genomes in a pangenome into two or more groups. For example, in the pangenome tutorial, the categorical variable named light partitioned genomes into low-light and high-light groups.

2. Determine the “functional associations” of gene clusters. This step involves collecting the functional annotations for all genes within each cluster and assigning the most frequently occurring annotation to represent the entire cluster.

3. Quantify the distribution of functions in each group of genomes. The program determines the extent to which particular functions are enriched in specific groups of genomes and reports this information as a functional-enrichment-txt file. This analysis is performed by executing the script anvi-script-enrichment-stats.

The script anvi-script-enrichment-stats was implemented by Amy Willis, and was first described in this paper.

Check out Alon’s behind the scenes post, which provides a more detailed explanation of this process.

Basic usage

The simplest way to execute this program is as follows:

The pan-db must contain at least one categorical data layer in misc-data-layers, and you must select one of these categories to define your pangenome groups using the --category-variable parameter. You can view available variables using the anvi-show-misc-data program with the parameters -t layers --debug.

The genomes-storage-db must contain at least one functional annotation source, and you must specify one of these sources using the --annotation-source parameter. If you are unsure which functional annotation sources are available in your genomes-storage-db, you can use the --list-annotation-sources parameter to identify them.

Additional options

By default, gene clusters with identical functional annotations will be merged. However, if you provide the --include-gc-identity-as-function parameter and set the annotation source to ‘IDENTITY’, anvi’o will treat gene cluster names as functions and enable you to investigate the enrichment of each gene cluster independently. This is accomplished as follows:

To generate a functional occurrence table that describes the number of times each functional association occurs in each genome under analysis, use the --functional-occurrence-table-output parameter as shown below:

Edit this file to update this information.

Additional Resources

• A description of the enrichment script run by this program can be found in Shaiber et al 2020

• An example of pangenome functional enrichment in the context of the Prochlorococcus metapangenome from Delmont and Eren 2018 is included in the pangenomics tutorial

Are you aware of resources that may help users better understand the utility of this program? Please feel free to edit this file on GitHub. If you are not sure how to do that, find the __resources__ tag in this file to see an example.