Bio Workflows Metagenomics Pipeline

Version Compatibility

Reference examples tested with: Bowtie2 2.5.3+, Bracken 2.9+, HUMAnN 3.8+, Kraken2 2.1+, MetaPhlAn 4.1+, fastp 0.23+, samtools 1.19+, matplotlib 3.8+, pandas 2.2+, 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.

Metagenomics Pipeline

"Analyze my metagenomic samples from FASTQ to taxonomic and functional profiles" -> Orchestrate controls and host depletion, Kraken2/Bracken taxonomic classification, MetaPhlAn profiling, and HUMAnN3 functional analysis - reporting results relative to a consistent pipeline, never as a direct observation of the community.

Complete workflow from metagenomic FASTQ to taxonomic and functional profiles. This is a workflow skill: it owns the chaining decisions and hand-offs, not the internals of any one step.

The governing principle

A taxonomic/functional profile is a position in a choice-chain (extraction -> depletion -> depth -> classifier -> DB -> normalization), never a direct observation; the trustworthy result is decided at these seams.

1. The reference DB + version is THE inherited commitment. The Kraken2/GTDB (or standard/RefSeq/UHGG) DB chosen at classification fixes what is detectable — a zero means below-detection OR not-in-DB OR lost-in-extraction OR removed-by-depletion, almost never biological absence. Pin the DB build (version alone moves species/genus calls); match DB to habitat (UHGG for gut); report the CLASSIFIED FRACTION (a low fraction is the tell that the DB is wrong).
2. Host removal against T2T-CHM13 is a made-once commitment done BEFORE profiling. Prefer the complete T2T over gapped GRCh38 (which lets human reads masquerade as novel microbes); mask rDNA; discard both mates if either maps host. It is a privacy obligation (leaked human reads are identifiable), not just QC.
3. Controls-first is a design commitment, not a step added later. Extraction blanks + a whole-cell mock carried through the WHOLE workflow. NO low-biomass result (skin, BAL, CSF, blood, tissue) is interpretable without blanks + DNA-concentration + decontam; at near-zero biomass the signal IS the kitome (Salter 2014). A blank cannot be retrofitted.
4. Read-fraction is not cell-fraction, and tools/DBs are not comparable. Kraken2 read-fraction (genome-size/copy-number biased) and MetaPhlAn cell-fraction must never be merged into one table. Holding tool+DB constant within a study is the only way a comparison measures biology and not the tool.

Made-once commitments

Workflow Overview

FASTQ files (+ extraction blanks, mock)
    |
    v
[0. QC, Host Removal & Controls] --> fastp + Hostile/Bowtie2(T2T) + blanks/decontam + Nonpareil depth check
    |
    v
[1. Taxonomic Classification]
    |
    +---> Kraken2 (+confidence, +hit-groups) + Bracken -> read fraction
    |
    +---> MetaPhlAn 4 (marker-based, pinned --index) -> cell fraction (NOT comparable to Bracken %)
    |
    v
[2. Functional Profiling] --> HUMAnN (potential, not activity; keep UNMAPPED)
    |
    v
Taxonomic profiles + Pathway abundances (+ AMR/strain via their own skills)

Primary Path: Kraken2 + Bracken + HUMAnN

Step 0: Quality Control, Host Removal, and Controls

Carry extraction blanks and a mock through the whole workflow; host-deplete against T2T-CHM13; confirm depth with Nonpareil. See metagenomics/contamination-controls for the controls/decontam detail.

# QC with fastp (trimming mechanics: read-qc/fastp-workflow)
for sample in sample1 sample2 sample3; do
    fastp -i ${sample}_R1.fastq.gz -I ${sample}_R2.fastq.gz \
        -o trimmed/${sample}_R1.fq.gz -O trimmed/${sample}_R2.fq.gz \
        --detect_adapter_for_pe \
        --qualified_quality_phred 20 \
        --length_required 50 \
        --html qc/${sample}_fastp.html
done

# Remove host reads - Hostile with a T2T-CHM13 index removes >99.5% host with low microbial loss.
# Report the reads removed; host depletion can halve usable depth.
for sample in sample1 sample2 sample3; do
    hostile clean --fastq1 trimmed/${sample}_R1.fq.gz --fastq2 trimmed/${sample}_R2.fq.gz \
        --index human-t2t-hla --aligner bowtie2 --output host_removed/
    # hostile names each paired output from its OWN input basename: fastq1 -> {R1}.clean_1.fastq.gz,
    # fastq2 -> {R2}.clean_2.fastq.gz. Rename to the ${sample}_R1/_R2.fq.gz the steps below consume.
    mv host_removed/${sample}_R1.clean_1.fastq.gz host_removed/${sample}_R1.fq.gz
    mv host_removed/${sample}_R2.clean_2.fastq.gz host_removed/${sample}_R2.fq.gz
done
# Then run decontam on the classifier output table using the blanks (contamination-controls),
# and confirm depth adequacy with Nonpareil before interpreting any non-detection.

Step 1A: Kraken2 Classification

# Classify reads. Raise --confidence above the default 0 to suppress single-k-mer false positives,
# and require >=2 hit groups. The database defines what can be detected.
for sample in sample1 sample2 sample3; do
    kraken2 --db kraken2_db \
        --threads 8 \
        --paired \
        --confidence 0.1 \
        --minimum-hit-groups 2 \
        --report kraken/${sample}.report \
        --output kraken/${sample}.output \
        host_removed/${sample}_R1.fq.gz \
        host_removed/${sample}_R2.fq.gz
done

Step 1B: Bracken Abundance Estimation

# Estimate species abundance
for sample in sample1 sample2 sample3; do
    bracken -d kraken2_db \
        -i kraken/${sample}.report \
        -o bracken/${sample}.species.txt \
        -r 150 \
        -l S \
        -t 10
done

# Combine samples into abundance matrix
combine_bracken_outputs.py \
    --files bracken/*.species.txt \
    -o bracken/combined_species.txt

Step 1C: Alternative - MetaPhlAn Profiling

# Profile with MetaPhlAn 4. Pin --index (DB version is a batch variable). MetaPhlAn % is a cell
# fraction - do NOT merge it with Bracken read fractions. In 4.2 --bowtie2out is renamed --mapout.
for sample in sample1 sample2 sample3; do
    metaphlan host_removed/${sample}_R1.fq.gz,host_removed/${sample}_R2.fq.gz \
        --bowtie2out metaphlan/${sample}.bowtie2.bz2 \
        --index mpa_vJun23_CHOCOPhlAnSGB_202403 \
        --input_type fastq \
        --nproc 8 \
        -o metaphlan/${sample}_profile.txt
done

# Merge profiles
merge_metaphlan_tables.py metaphlan/*_profile.txt > metaphlan/merged_abundance.txt

Step 2: Functional Profiling with HUMAnN

# Run HUMAnN
for sample in sample1 sample2 sample3; do
    # Concatenate paired reads
    cat host_removed/${sample}_R1.fq.gz host_removed/${sample}_R2.fq.gz > \
        host_removed/${sample}_concat.fq.gz

    humann --input host_removed/${sample}_concat.fq.gz \
        --output humann/${sample} \
        --threads 8 \
        --metaphlan-options "--bowtie2db metaphlan_db"
done

# Normalize and join tables. HUMAnN names outputs from the input STEM, so the ${sample}_concat.fq.gz
# input above yields sample1_concat_pathabundance.tsv (not sample1_pathabundance.tsv).
humann_renorm_table --input humann/sample1/sample1_concat_pathabundance.tsv \
    --output humann/sample1/sample1_concat_pathabundance_cpm.tsv \
    --units cpm

# --search-subdirectories: per-sample outputs live in humann/<sample>/ subdirs; the join is
# non-recursive by default and would otherwise find zero files.
humann_join_tables --input humann \
    --search-subdirectories \
    --output humann/merged_pathabundance.tsv \
    --file_name pathabundance

Visualization

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load Bracken species table. combine_bracken_outputs.py emits name, taxonomy_id, taxonomy_lvl,
# then per-sample {sample}_num / {sample}_frac columns. Keep ONLY the fractions: taxonomy_lvl is a
# string ('S') and summing it raises TypeError; summing taxonomy_id would be meaningless anyway.
species = pd.read_csv('bracken/combined_species.txt', sep='\t', index_col=0)
species = species.filter(regex='_frac$').rename(columns=lambda c: c.replace('_frac', ''))

# Top 20 species heatmap
top20 = species.sum(axis=1).nlargest(20).index
plt.figure(figsize=(12, 8))
sns.heatmap(species.loc[top20], cmap='viridis', annot=False)
plt.title('Top 20 Species Abundance')
plt.tight_layout()
plt.savefig('top20_species_heatmap.pdf')

# Stacked bar plot
species_norm = species.div(species.sum()) * 100
top10 = species_norm.sum(axis=1).nlargest(10).index
other = species_norm.loc[~species_norm.index.isin(top10)].sum()

plot_data = species_norm.loc[top10].T
plot_data['Other'] = other
plot_data.plot(kind='bar', stacked=True, figsize=(10, 6))
plt.ylabel('Relative Abundance (%)')
plt.legend(bbox_to_anchor=(1.05, 1))
plt.tight_layout()
plt.savefig('species_barplot.pdf')

Parameter Recommendations

Common Errors

References

• Salter SJ, Cox MJ, Turek EM, et al (2014) Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biology 12:87. DOI 10.1186/s12915-014-0087-z. (the kitome.)
• Costea PI, Zeller G, Sunagawa S, et al (2017) Towards standards for human fecal sample processing in metagenomic studies. Nature Biotechnology 35:1069-1076. DOI 10.1038/nbt.3960. (extraction dominates.)
• Meyer F, Bremges A, Belmann P, et al (2019) Assessing taxonomic metagenome profilers with OPAL. Genome Biology 20:51. DOI 10.1186/s13059-019-1646-y.
• Sczyrba A, Hofmann P, Belmann P, et al (2017) Critical Assessment of Metagenome Interpretation (CAMI). Nature Methods 14:1063-1071. DOI 10.1038/nmeth.4458.

Complete Pipeline Script

#!/bin/bash
set -e

THREADS=8
KRAKEN_DB="kraken2_standard_db"
HOST_INDEX="human_bt2_index"   # MUST be built from T2T-CHM13 (CHM13v2, +HLA) per the made-once host-removal commitment, not a legacy GRCh38 index
SAMPLES="sample1 sample2 sample3"
OUTDIR="metagenomics_results"

mkdir -p ${OUTDIR}/{trimmed,host_removed,kraken,bracken,metaphlan,humann,qc}

# Step 1: QC
echo "=== QC ==="
for sample in $SAMPLES; do
    fastp -i ${sample}_R1.fastq.gz -I ${sample}_R2.fastq.gz \
        -o ${OUTDIR}/trimmed/${sample}_R1.fq.gz \
        -O ${OUTDIR}/trimmed/${sample}_R2.fq.gz \
        --length_required 50 \
        --html ${OUTDIR}/qc/${sample}_fastp.html -w ${THREADS}
done

# Host removal
echo "=== Host Removal ==="
for sample in $SAMPLES; do
    # -f 12 (read unmapped AND mate unmapped) keeps only pairs where NEITHER mate hit the host.
    # --un-conc-gz would instead keep every non-CONCORDANT pair, retaining pairs whose mate mapped
    # human -- a privacy leak, not just a QC lapse. -F 256 drops secondary alignments.
    bowtie2 -p ${THREADS} -x ${HOST_INDEX} --very-sensitive \
        -1 ${OUTDIR}/trimmed/${sample}_R1.fq.gz \
        -2 ${OUTDIR}/trimmed/${sample}_R2.fq.gz \
        2> ${OUTDIR}/qc/${sample}_host.log \
      | samtools view -b -f 12 -F 256 - \
      | samtools sort -n -@ ${THREADS} - \
      | samtools fastq -1 ${OUTDIR}/host_removed/${sample}_R1.fq.gz \
                       -2 ${OUTDIR}/host_removed/${sample}_R2.fq.gz \
                       -0 /dev/null -s /dev/null -n -
done

# Step 2: Kraken2
echo "=== Kraken2 ==="
for sample in $SAMPLES; do
    kraken2 --db ${KRAKEN_DB} --threads ${THREADS} --paired \
        --confidence 0.1 --minimum-hit-groups 2 \
        --report ${OUTDIR}/kraken/${sample}.report \
        --output ${OUTDIR}/kraken/${sample}.output \
        ${OUTDIR}/host_removed/${sample}_R1.fq.gz \
        ${OUTDIR}/host_removed/${sample}_R2.fq.gz
done

# Bracken
echo "=== Bracken ==="
for sample in $SAMPLES; do
    bracken -d ${KRAKEN_DB} \
        -i ${OUTDIR}/kraken/${sample}.report \
        -o ${OUTDIR}/bracken/${sample}.species.txt \
        -r 150 -l S -t 10
done

echo "=== Pipeline Complete ==="
echo "Kraken reports: ${OUTDIR}/kraken/"
echo "Bracken abundances: ${OUTDIR}/bracken/"

Related Skills

• database-access/sra-data - Pull metagenomic FASTQ from SRA / ENA (16S amplicon or shotgun)
• database-access/ncbi-datasets-cli - Bulk-pull reference genomes for read mapping
• database-access/remote-homology - DIAMOND --ultra-sensitive for predicted-ORF annotation
• metagenomics/contamination-controls - Host depletion, blanks/decontam, depth checks up front
• metagenomics/kraken-classification - Kraken2 details
• metagenomics/metaphlan-profiling - MetaPhlAn parameters
• metagenomics/abundance-estimation - Bracken options and compositional handling
• metagenomics/functional-profiling - HUMAnN workflow
• metagenomics/amr-detection - Community resistome from the same reads
• metagenomics/strain-tracking - Strain resolution from the same reads
• metagenomics/metagenome-visualization - Plotting and community statistics