MethanoHunt

A pipeline to profile methane cyclers from taxonomic profiling data or functional marker genes.

Overview

MethanoHunt provides four handy workflows:

1. Profile: Summarizes relative abundance of methane cyclers from a taxonomic profiles (e.g. results from MetaPhlAn/SingleM/Sylph).
2. Taxonomy: Find methane cyclers according to GTDB taxonomy.
3. Gene: A pipeline to detect, classify, and quantify methane cycling marker genes (McrA, PmoA, MmoX) from protein sequences.
4. Genome: A pipeline to detect and classify methane cyclers from genomes/metagenome-assembled genomes.

Installation

Dependencies

MethanoHunt requires:

• Python 3.8+
• Snakemake > 9.0
• HMMER3
• raxml-ng, epa-ng, gappa
• Minimap2, Samtools
• MicrobeCensus
• Python packages: pandas, natsort, plotly, click, pysam
• PaPaRa (install independently)

Use conda to install all depencies except PaPaRa which needs another step.

git clone https://github.com/SilentGene/MethanoHunt.git
conda env create -f methanohunt.yaml  # create a conda env named "methanohunt and install dependencies"
conda activate methanohunt
methanohunt setup  # to install PaPaRa

Note

During the environment installation, you may see a message from kofamscan asking you to download its database from ftp.genome.jp. Please ignore this message. You do NOT need to download the official KOfam database, as MethanoHunt relies on its own curated databases.

Profile Workflow

Analyze taxonomic abundance tables.

$ methanohunt profile --help
Usage: methanohunt profile [OPTIONS]

  Run taxonomy-based profiling.

  Accepts either --input_wide or --input_long files.

Options:
  --input_wide TEXT     Input TSV profile file in wide format (first column:
                        taxonomy, rest: samples)
  --input_long TEXT     Input TSV profile file in long format with columns:
                        sample, taxonomy, relative_abundance
  -db, --database TEXT  Path to MethanoHunt database file
  -o, --output TEXT     Output directory path (will be created if not exists)
                        [required]
  -g, --group TEXT      Tab-separated file for grouping samples
                        (Sample\tGroup)
  --help                Show this message and exit.

Input profile file format

MethanoHunt accepts two input profile file formats:

1. Wide format: (--input_wide) First column is taxonomy, rest are samples
2. Long format: (--input_long) Columns are sample, taxonomy, relative_abundance

➡️ An example workflow from raw reads to MethanoHunt profile result via SingleM

Suppose you have 10 paired-end metagenomic samples in FASTQ format with filenames like sample1_R1.fastq.gz/sample1_R2.fastq.gz, sample2_R1.fastq.gz/sample2_R2.fastq.gz, and so on.

Step 1: Run singleM to generate taxonomic profiles

You don't need to trim or QC the reads before running singleM

cd /my/raw_reads/  # change to the directory with your FASTQ files
SAMPLES=$(ls *_R1.fastq.gz | sed 's/_R1.fastq.gz//')  # get sample names
conda activate singlem  # activate your singleM conda environment

cd ..
mkdir -p singleM_results  # create singleM output directory

# Run singleM for each sample
for SAMPLE in $SAMPLES; do
    # Run singleM pipe for each sample
    singlem pipe -1 /my/raw_reads/${SAMPLE}_R1.fastq.gz -2 /my/raw_reads/${SAMPLE}_R2.fastq.gz --threads 4 \
        --taxonomic-profile singleM_results/"$SAMPLE"_singlem.tax.tsv \
        --taxonomic-profile-krona singleM_results/"$SAMPLE"_singlem.tax-krona.html \
        --otu-table singleM_results/"$SAMPLE"_singlem.otu.tsv
    # Summarise the taxonomic profile to get long format profile
    singlem summarise --input-taxonomic-profile singleM_results/"$SAMPLE"_singlem.tax.tsv \
        --output-taxonomic-profile-with-extras singleM_results/"$SAMPLE"_singlem.long.tsv
done

# Merge all the long format files
awk 'FNR==1 && NR>1 {next} NF>0' singleM_results/*_singlem.long.tsv > singleM_results/singleM_long_merged.tsv

Step 2: Run MethanoHunt on the generated taxonomic profiles

methanohunt profile --input_long singleM_results/singleM_long_merged.tsv -o methanohunt-profile_dir

➡️ An example workflow from raw reads to MethanoHunt profile result via Sylph

Suppose you have 10 paired-end metagenomic samples in FASTQ format with filenames like sample1_R1.fastq.gz/sample1_R2.fastq.gz, sample2_R1.fastq.gz/sample2_R2.fastq.gz, and so on.

Step 1: Run Sylph to generate taxonomic profiles

cd /my/raw_reads/  # change to the directory with your FASTQ files
SAMPLES=$(ls *_R1.fastq.gz | sed 's/_R1.fastq.gz//')  # get sample names
conda activate sylph  # activate your sylph conda environment

cd ..
mkdir -p sylph_results  # create sylph output directory

# run sylph
sylph profile $DB_DIR/gtdb-r226-c200-dbv1.syldb \
    -1 *_R1.fastq.gz \
    -2 *_R2.fastq.gz \
    -t $(nproc) \
    > sylph_results/sylph_profile.txt

# run sylph-tax
mkdir -p sylph_results/sylphmpa
sylph-tax taxprof sylph_results/sylph_profile.txt -t GTDB_r226 -o sylph_results/sylphmpa/

# merge results to wide format
sylph-tax merge sylph_results/sylphmpa/*.sylphmpa --column relative_abundance -o sylph_results/sylph-tax_profile.tsv

Step 2: Run MethanoHunt on the generated wide format taxonomic profiles

methanohunt profile --input_wide sylph_results/sylph-tax_profile.tsv -o methanohunt-profile_dir

Note

Taxonomy-based classifications may have false positives. Verification with functional gene analysis is recommended.

Taxonomy Workflow

Annotate taxonomic profiles with methane cycler information according to GTDB taxonomy.

Example:

methanohunt taxonomy -i MAG_classification.tsv -c Taxonomy -o methanohunt_annotate_results.tsv

• -i: Input tsv file, including a column with taxonomic classification. This table MUST contain header.
• -c: Column name of the taxonomic classification in the input tsv file.
• -o: Output tsv file.
• -db: (Optional) Custom database path. If not provided, it will use the default database installed along with the pipeline.

Output

This workflow will generate a tsv file with the following two additional columns added to the input tsv file:

• MethanoHunt_classification: methane cycling role
• MethanoHunt_subgroup: methane cycling subgroup

Gene Workflow

Analyze functional genes from protein sequences.

Example:

Classify genes and build phylogenetic trees with reference sequences

methanohunt gene --prot assembly_genes.faa -o my_results --tree

With abundance calculation (requires nucleotide genes and reads):

methanohunt gene \
    --prot assembly_genes.faa \
    --nucl assembly_genes.ffn \
    -1 sample_R1.fq.gz -2 sample_R2.fq.gz \
    -o my_results \
    --marker McrA,PmoA,MmoX \
    --tree \
    --threads 8

• --prot: Input protein FASTA (.faa).
• --nucl: (Optional) Corresponding gene nucleotide FASTA (.ffn), required for abundance.
• -1, -2: (Optional) Reads for abundance quantification. Support multiple files or glob patterns (e.g., *.fq.gz). You can rely on shell expansion (e.g. 2015*_1.fq.gz).
• -o: Output directory.
• --marker: (Optional) Marker genes to analyze. Default is McrA,PmoA,MmoX.
• --tree: (Optional) Build phylogenetic trees together with reference markers using fasttree. Default is False.
• -db: Database folder. Default is the database folder included in the package.

Gene Module Output

Important results:

• methanohunt_gene_classification.tsv: Detected genes and their functional classification.
• MethanoHunt_report.html: Abundance visualization (RPKG based).
• RPKG/: RPKG abundance tables (classified, subtype, combined).
• classified_sequences/: Detected sequences and their functional classification in fasta format.
• tree/: Phylogenetic trees of detected sequences (if --tree is specified).

Other results:

• bam/: Mapping results and reference.
• microbecensus/: Genome equivalent estimation results.
• hmm/, hits/, placement/, classification/, Intermediate results.

Here is an example of the output (MethanoHunt_gene_report.html)[docs/MethanoHunt_gene_report.html].

Screenshot of the interactive chart:

Genes that are able to be classified in this module

Homologue	KO	Classification	Description	Refs	Note
McrA	K00399	Methanogen	Anaerobic methanogenesis	Chadwick et al., (2022)
McrA	K00399	ANME	Anaerobic methane oxidation	Chadwick et al., (2022)
McrA	K00399	ANKA	Anaerobic alkane oxidation	Chadwick et al., (2022)
PmoA/AmoA	K10944	PmoA	Aerobic methane oxidation (particulate)	Leu et al., (2025)
PmoA/AmoA	K10944	AmoA	Aerobic ammonia oxidation	Leu et al., (2025)
PmoA/AmoA	K10944	HMO	Aerobic hydrocarbon oxidation	Leu et al., (2025)
MmoX	K16157	MmoX	Aerobic methane oxidation (soluble)	KEGG K16157
MmoX	K16157	PrmA	Aerobic propane oxidation	KEGG K16157
MmoX	K16157	MimA	Aerobic alkane oxidation	KEGG K16157
PhnJ	K06163	PhnJ	Aerobic methanogenesis	Boden et al., 2024	To be implemented

Genome Workflow

Detect and classify methane cyclers from genomes/metagenome-assembled genomes.

Example:

methanohunt genome \
    --genome_dir my_genomes_fna_dir \
    --suffix fna \
    --threads 32 \
    --taxonomy_tsv my_genomes_taxonomy.tsv \
    --taxonomy_col GTDB_classification \
    --name_col bin_id \
    --output_dir methanohunt-genome_output

$ methanohunt genome --help
Usage: methanohunt genome [OPTIONS]

  Run genome-based methane cycler classification.

Options:
  -i, --genome_dir TEXT    Input directory containing genome DNA FASTA files
  --faa_dir TEXT           Input directory containing genome protein FASTA
                           files (.faa)
  -s, --suffix TEXT        File extension suffix (e.g., fna, faa)  [required]
  -f, --taxonomy_tsv TEXT  TSV file containing genome taxonomy mapping
  -a, --name_col TEXT      Column name of the genome name in the input tsv
                           file
  -c, --taxonomy_col TEXT  Column name containing GTDB taxonomy in the
                           taxonomy file
  -t, --threads INTEGER    Number of threads to use (default: 8)
  --strict                 Use strict mode (requires If_key_in_enzyme to be
                           TRUE for KO mapping)
  --snake-args TEXT        Additional arguments to pass directly to snakemake
                           (e.g. '--unlock')
  -o, --output_dir TEXT        Output directory path (default:
                           methanohunt_genome_out)
  --help                   Show this message and exit.

Output

Database

The required database files for both taxonomy and gene modules are included in the package. Experts are welcome to edit the database files for your own needs.

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