metagenomics-functional-profiling

name	metagenomics-functional-profiling
description	Profile functional potential of metagenomes using HUMAnN3 and similar tools. Use when obtaining pathway abundances, gene family counts, or functional annotations from metagenomic data.
tool_type	cli
primary_tool	humann

Version Compatibility

Reference examples tested with: HUMAnN 3.8+, MetaPhlAn 4.1+, matplotlib 3.8+, pandas 2.2+, scanpy 1.10+, scipy 1.12+, seaborn 0.13+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Functional Profiling

"What metabolic pathways are present in my metagenome?" -> Profile functional potential of metagenomic samples to obtain pathway abundances and gene family counts using translated search against UniRef and MetaCyc.

• CLI: humann --input reads.fastq --output results/ (HUMAnN3)

Profile the functional potential of metagenomic samples using HUMAnN3 to get pathway and gene family abundances.

HUMAnN3 Workflow

Installation

# Install via conda (recommended)
conda create -n humann -c bioconda humann
conda activate humann

# Download databases
humann_databases --download chocophlan full /path/to/databases
humann_databases --download uniref uniref90_diamond /path/to/databases

# Update config with database paths
humann_config --update database_folders nucleotide /path/to/databases/chocophlan
humann_config --update database_folders protein /path/to/databases/uniref

Basic Usage

# Run HUMAnN3 on a single sample
humann --input sample.fastq.gz --output sample_humann

# With MetaPhlAn taxonomic profile (faster)
humann --input sample.fastq.gz \
       --taxonomic-profile sample_metaphlan.txt \
       --output sample_humann

# Paired-end reads (concatenate first)
cat sample_R1.fq.gz sample_R2.fq.gz > sample_concat.fq.gz
humann --input sample_concat.fq.gz --output sample_humann

Output Files

sample_humann/
├── sample_genefamilies.tsv     # Gene family abundances (UniRef90)
├── sample_pathabundance.tsv    # MetaCyc pathway abundances
├── sample_pathcoverage.tsv     # Pathway coverage (0-1)
└── sample_humann_temp/         # Intermediate files

Output Format

Gene Families

# Gene Family   sample_Abundance-RPKs
UniRef90_A0A000|g__Bacteroides.s__Bacteroides_vulgatus   123.45
UniRef90_A0A001|unclassified                              67.89
UNMAPPED                                                  1000.0

Pathway Abundance

# Pathway                                    sample_Abundance
PWY-5100: pyruvate fermentation              456.78
PWY-5100|g__Bacteroides.s__Bacteroides_vulgatus  234.56
PWY-5100|unclassified                        222.22

Batch Processing

# Process multiple samples
for fq in *.fastq.gz; do
    sample=$(basename $fq .fastq.gz)
    humann --input $fq --output ${sample}_humann --threads 8
done

# Join tables across samples
humann_join_tables -i . -o merged_genefamilies.tsv --file_name genefamilies
humann_join_tables -i . -o merged_pathabundance.tsv --file_name pathabundance

Normalization

# Normalize to relative abundance
humann_renorm_table -i merged_genefamilies.tsv \
                    -o genefamilies_relab.tsv \
                    -u relab

# Normalize to copies per million (CPM)
humann_renorm_table -i merged_pathabundance.tsv \
                    -o pathabundance_cpm.tsv \
                    -u cpm

Regroup Gene Families

# Regroup to different functional categories
# EC numbers
humann_regroup_table -i genefamilies.tsv \
                     -g uniref90_level4ec \
                     -o genefamilies_ec.tsv

# KEGG Orthologs
humann_regroup_table -i genefamilies.tsv \
                     -g uniref90_ko \
                     -o genefamilies_ko.tsv

# GO terms
humann_regroup_table -i genefamilies.tsv \
                     -g uniref90_go \
                     -o genefamilies_go.tsv

# Pfam domains
humann_regroup_table -i genefamilies.tsv \
                     -g uniref90_pfam \
                     -o genefamilies_pfam.tsv

Stratification

Split by Organism

# Unstratify (remove organism info, sum across species)
humann_split_stratified_table -i merged_pathabundance.tsv \
                               -o .

# Creates: merged_pathabundance_unstratified.tsv
#          merged_pathabundance_stratified.tsv

Species Contributions

import pandas as pd

df = pd.read_csv('merged_pathabundance.tsv', sep='\t', index_col=0)

unstratified = df[~df.index.str.contains('\\|')]
stratified = df[df.index.str.contains('\\|')]

def get_species_contrib(pathway, df):
    '''Get species contributions to a pathway'''
    mask = df.index.str.startswith(pathway + '|')
    return df[mask]

contrib = get_species_contrib('PWY-5100', stratified)

Quality Control

# Check unmapped and unintegrated
humann_barplot -i merged_pathabundance.tsv \
               -o pathabundance_barplot.png \
               --focal-feature UNMAPPED

Key QC Metrics

Metric	Good	Concerning
UNMAPPED (gene families)	<30%	>50%
UNINTEGRATED (pathways)	<40%	>60%
Pathway coverage	>0.5	<0.3

Differential Analysis

LEfSe Format

# Format for LEfSe
humann_join_tables -i . -o merged.tsv --file_name pathabundance
humann_renorm_table -i merged.tsv -o merged_relab.tsv -u relab

Python Analysis

Goal: Identify differentially abundant metabolic pathways between conditions from HUMAnN3 output.

Approach: Load unstratified pathway abundances, split samples by condition using metadata, run Mann-Whitney U tests per pathway, and apply FDR correction.

import pandas as pd
from scipy import stats

df = pd.read_csv('pathabundance_cpm.tsv', sep='\t', index_col=0)
metadata = pd.read_csv('metadata.tsv', sep='\t', index_col=0)

group1 = metadata[metadata['condition'] == 'healthy'].index
group2 = metadata[metadata['condition'] == 'disease'].index

results = []
for pathway in df.index:
    if '|' not in pathway and pathway != 'UNMAPPED':
        vals1 = df.loc[pathway, group1]
        vals2 = df.loc[pathway, group2]
        stat, pval = stats.mannwhitneyu(vals1, vals2)
        fc = vals2.mean() / (vals1.mean() + 1e-10)
        results.append({'pathway': pathway, 'pvalue': pval, 'fold_change': fc})

results_df = pd.DataFrame(results)
results_df['padj'] = stats.false_discovery_control(results_df['pvalue'])

Visualization

import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv('pathabundance_relab.tsv', sep='\t', index_col=0)
df = df[~df.index.str.contains('\\|')]
df = df.drop(['UNMAPPED', 'UNINTEGRATED'], errors='ignore')
top = df.mean(axis=1).nlargest(20).index

plt.figure(figsize=(12, 8))
sns.heatmap(df.loc[top].T, cmap='viridis', xticklabels=True)
plt.tight_layout()
plt.savefig('pathway_heatmap.png')

Related Skills

• metagenomics/metaphlan-profiling - Taxonomic profiling (input for HUMAnN)
• metagenomics/kraken-classification - Alternative taxonomy
• metagenomics/metagenome-visualization - Visualization methods
• pathway-analysis/kegg-pathways - KEGG pathway interpretation