Use HUMAnN3 to estimate the functional potential of microbial communities from metagenomic reads. Generate gene family and metabolic pathway abundance tables, combine samples, examine species contributions, and compare pathway abundances across conditions.
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Profile functional potential of metagenomes using HUMAnN3 and similar tools. Use when obtaining pathway abundances, gene family counts, or functional annotations from metagenomic data.
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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:
pip show <package> then help(module.function) to check signatures<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"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.
humann --input reads.fastq --output results/ (HUMAnN3)Profile the functional potential of metagenomic samples using HUMAnN3 to get pathway and gene family abundances.
# 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
# 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
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
# Gene Family sample_Abundance-RPKs
UniRef90_A0A000|g__Bacteroides.s__Bacteroides_vulgatus 123.45
UniRef90_A0A001|unclassified 67.89
UNMAPPED 1000.0
# Pathway sample_Abundance
PWY-5100: pyruvate fermentation 456.78
PWY-5100|g__Bacteroides.s__Bacteroides_vulgatus 234.56
PWY-5100|unclassified 222.22
# 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
# 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 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
# 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
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)
# Check unmapped and unintegrated
humann_barplot -i merged_pathabundance.tsv \
-o pathabundance_barplot.png \
--focal-feature UNMAPPED
| Metric | Good | Concerning |
|---|---|---|
| UNMAPPED (gene families) | <30% | >50% |
| UNINTEGRATED (pathways) | <40% | >60% |
| Pathway coverage | >0.5 | <0.3 |
# 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
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'])
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')
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