MKado 御門

A modern Python implementation of the McDonald-Kreitman test toolkit for detecting selection in molecular evolution.

Documentation | PyPI

Features

• Standard MK test: Classic 2x2 contingency table with Fisher's exact test
• Polarized MK test: Uses a second outgroup to assign mutations to lineages
• Asymptotic MK test: Frequency-bin α estimates with exponential extrapolation (Messer & Petrov 2013); supports both per-bin (--sfs-mode at) and inclusive cumulative-tail (--sfs-mode above, Uricchio et al. 2019) SFS constructions
• Imputed MK test: Bias-corrected α via low- vs high-frequency synonymous-polymorphism imputation (Murga-Moreno et al. 2022)
• Tarone-Greenland α_TG: Weighted multi-gene estimator that corrects for sample size heterogeneity (Stoletzki & Eyre-Walker 2011)
• Omega decomposition: dN/dS plus adaptive (ω_a) / non-adaptive (ω_na) rates with 95% CIs (Gossmann, Keightley & Eyre-Walker 2012; Coronado-Zamora et al. 2019)
• Bootstrap CI option: --ci-method bootstrap runs case-resampling refit per replicate as an alternative to the parametric Monte-Carlo CI, with parallelized inner loop
• Alternate genetic codes: 24 NCBI genetic code tables (mitochondrial, plastid, etc.) selectable by name
• VCF input: Go directly from VCF + reference + GFF3 annotation to MK test results (no FASTA alignment needed)
• Batch processing: Process multiple genes with parallel execution and Benjamini-Hochberg correction for multiple testing
• Volcano plots: Visualize batch results with publication-ready volcano plots
• Multiple output formats: Pretty-print, TSV, and JSON, with --output / -O PATH to write to a file

Installation

# Install with uv (recommended)
uv pip install mkado

# Or install with pip
pip install mkado

Development Installation

# Clone the repository
git clone https://github.com/andrewkern/mkado.git
cd mkado

# Install with uv
uv sync

Quick Start

# Standard MK test (combined alignment file)
mkado test alignment.fa -i "dmel" -o "dsim"

# Asymptotic MK test
mkado test alignment.fa -i "dmel" -o "dsim" -a

# Polarized MK test
mkado test alignment.fa -i "dmel" -o "dsim" --polarize-match "dyak"

# Batch process a directory
mkado batch alignments/ -i "dmel" -o "dsim"

# Batch with asymptotic test and 8 parallel workers
mkado batch alignments/ -i "dmel" -o "dsim" -a -w 8

# Run MK test directly from VCF files
mkado vcf --vcf pop.vcf.gz --outgroup-vcf out.vcf.gz --ref genome.fa --gff genes.gff3

# Get file info
mkado info sequences.fa

Usage Modes

mkado supports two modes for specifying ingroup/outgroup sequences:

Combined File Mode (Recommended)

Use -i and -o to filter sequences by name pattern from a single alignment file:

mkado test alignment.fa -i "speciesA" -o "speciesB"
mkado batch alignments/ -i "speciesA" -o "speciesB"

Separate Files Mode

Provide separate FASTA files for ingroup and outgroup:

mkado test ingroup.fa outgroup.fa
mkado batch genes/ --ingroup-pattern "*_in.fa" --outgroup-pattern "*_out.fa"

Commands

mkado test

Run MK test on a single alignment.

mkado test FASTA [OUTGROUP_FILE] [OPTIONS]

Key Options:

Option	Short	Description
--ingroup-match	-i	Ingroup sequence name pattern (combined mode)
--outgroup-match	-o	Outgroup sequence name pattern (combined mode)
--asymptotic	-a	Use asymptotic MK test
--polarize	-p	Second outgroup file (separate files mode)
--polarize-match		Second outgroup pattern (combined mode)
--bins	-b	Frequency bins for asymptotic test (default: 10)
--plot-asymptotic		Generate alpha(x) plot for asymptotic test (PNG, PDF, or SVG)
--code-table		Genetic code (e.g. vertebrate-mito, or NCBI ID)
--format	-f	Output format: pretty, tsv, json
--reading-frame	-r	Reading frame 1-3 (default: 1)

Examples:

# Combined file mode
mkado test alignment.fa -i "dmel" -o "dsim"
mkado test alignment.fa -i "dmel" -o "dsim" -a -b 20
mkado test alignment.fa -i "dmel" -o "dsim" -a --plot-asymptotic alpha_fit.png
mkado test alignment.fa -i "dmel" -o "dsim" --polarize-match "dyak"

# Separate files mode
mkado test ingroup.fa outgroup.fa
mkado test ingroup.fa outgroup.fa -a
mkado test ingroup.fa outgroup.fa -p outgroup2.fa

mkado batch

Run MK test on multiple alignment files.

mkado batch DIRECTORY [OPTIONS]

Key Options:

Option	Short	Description
--ingroup-match	-i	Ingroup pattern (enables combined file mode)
--outgroup-match	-o	Outgroup pattern (required with -i)
--asymptotic	-a	Use asymptotic MK test
--alpha-tg		Compute weighted α_TG (Stoletzki & Eyre-Walker 2011)
--aggregate/--per-gene		Aggregate results or per-gene (asymptotic)
--pattern		File glob pattern (default: auto-detect *.fa, *.fasta, *.fna)
--workers	-w	Parallel workers (0=auto, 1=sequential)
--bins	-b	Frequency bins for asymptotic test
--code-table		Genetic code (e.g. vertebrate-mito, or NCBI ID)
--format	-f	Output format: pretty, tsv, json
--volcano		Generate volcano plot (PNG, PDF, or SVG)
--plot-asymptotic		Generate alpha(x) plot for aggregated asymptotic test

Examples:

# Combined file mode (recommended)
mkado batch alignments/ -i "dmel" -o "dsim"
mkado batch alignments/ -i "dmel" -o "dsim" -a
mkado batch alignments/ -i "dmel" -o "dsim" -a --per-gene
mkado batch alignments/ -i "dmel" -o "dsim" --alpha-tg
mkado batch alignments/ -i "dmel" -o "dsim" -w 8

# Generate a volcano plot
mkado batch alignments/ -i "dmel" -o "dsim" --volcano results.png

# Generate asymptotic alpha(x) plot
mkado batch alignments/ -i "dmel" -o "dsim" -a --plot-asymptotic alpha_fit.png

# Separate files mode
mkado batch genes/ --ingroup-pattern "*_in.fa" --outgroup-pattern "*_out.fa"

mkado vcf

Run MK test from VCF + reference genome + GFF3 annotation. No pre-aligned FASTA files needed.

mkado vcf --vcf VCF --outgroup-vcf VCF --ref FASTA --gff GFF3 [OPTIONS]

Key Options:

Option	Short	Description
--vcf		Ingroup VCF (multi-sample, bgzipped+tabix recommended)
--outgroup-vcf		Single-sample outgroup VCF
--ref		Reference FASTA (plain or bgzipped, faidx-indexed)
--gff		GFF3 annotation (plain or gzipped)
--gene		Analyze a single gene by ID
--gene-list		File with gene IDs to analyze
--asymptotic	-a	Use asymptotic MK test
--alpha-tg		Compute weighted α_TG
--imputed		Use imputed MK test
--aggregate/--per-gene		Aggregate or per-gene results
--volcano		Generate volcano plot (PNG, PDF, or SVG)
--plot-asymptotic		Generate alpha(x) plot for aggregated asymptotic test
--workers	-w	Parallel workers (0=auto)
--format	-f	Output format: pretty, tsv, json
--verbose		Show htslib/VCF parsing warnings

Examples:

# Standard MK test across all genes
mkado vcf --vcf pop.vcf.gz --outgroup-vcf out.vcf.gz --ref genome.fa --gff genes.gff3

# Asymptotic MK test
mkado vcf --vcf pop.vcf.gz --outgroup-vcf out.vcf.gz --ref genome.fa --gff genes.gff3 -a

# Single gene analysis
mkado vcf --vcf pop.vcf.gz --outgroup-vcf out.vcf.gz --ref genome.fa --gff genes.gff3 --gene BRCA1

# With parallel workers and TSV output
mkado vcf --vcf pop.vcf.gz --outgroup-vcf out.vcf.gz --ref genome.fa --gff genes.gff3 -w 8 -f tsv

mkado codes

List available genetic code tables.

mkado codes

mkado info

Display information about a FASTA file.

mkado info FASTA [-r READING_FRAME]

Example Output

$ mkado test alignment.fa -i "kreitman" -o "mauritiana"

Found 11 ingroup, 1 outgroup sequences
MK Test Results:
  Divergence:    Dn=6, Ds=8
  Polymorphism:  Pn=1, Ps=8
  Fisher's exact p-value: 0.176
  Neutrality Index (NI):  0.1667
  Alpha (α):              0.8333
  DoS:                    0.3175

Python API

from mkado import mk_test, asymptotic_mk_test, SequenceSet

# Run MK test
result = mk_test("ingroup.fa", "outgroup.fa")
print(f"Alpha: {result.alpha}")
print(f"P-value: {result.p_value}")

# Run asymptotic MK test
result = asymptotic_mk_test("ingroup.fa", "outgroup.fa")
print(f"Asymptotic Alpha: {result.alpha_asymptotic}")
print(f"95% CI: {result.ci_low} - {result.ci_high}")

# Combined file mode - filter by sequence name
all_seqs = SequenceSet.from_fasta("combined.fa")
ingroup = all_seqs.filter_by_name("dmel")
outgroup = all_seqs.filter_by_name("dsim")
result = mk_test(ingroup, outgroup)

Interpretation

Neutrality Index (NI)

• NI = 1: Neutral evolution
• NI > 1: Excess polymorphism (segregating weakly deleterious variants)
• NI < 1: Excess divergence (positive selection)

Alpha (α)

• α = 0: No adaptive substitutions
• α > 0: Proportion of substitutions driven by positive selection
• α < 0: Excess polymorphism relative to divergence

Direction of Selection (DoS)

From Stoletzki & Eyre-Walker (2011), DoS = Dn/(Dn+Ds) - Pn/(Pn+Ps):

• DoS = 0: Neutral evolution
• DoS > 0: Positive selection (excess adaptive substitutions)
• DoS < 0: Slightly deleterious polymorphisms

DoS is bounded [-1, +1] and symmetric around 0, making it easier to interpret than NI.

Development

# Install dev dependencies
uv sync

# Run tests
uv run pytest

# Run linter
uv run ruff check src/

# Run formatter
uv run ruff format src/

Examples

Example data and tutorials are available in the examples/ directory:

# Run batch MK test on example data
mkado batch examples/anopheles_batch/ -i gamb -o afun

# Run asymptotic MK test
mkado batch examples/anopheles_batch/ -i gamb -o afun -a

See the documentation for detailed tutorials and API reference.

Citation

If you use MKado in your research, please cite:

Rivera-Colón, A. G., Rehmann, C. T., & Kern, A. D. (2026). MKado: a toolkit for McDonald-Kreitman tests of natural selection. bioRxiv. https://doi.org/10.64898/2026.03.02.709122

References

• Haller, B. C., & Messer, P. W. (2017). asymptoticMK: A web-based tool for the asymptotic McDonald–Kreitman test. G3: Genes, Genomes, Genetics, 7(5), 1569-1575. https://doi.org/10.1534/g3.117.039693
• McDonald, J. H., & Kreitman, M. (1991). Adaptive protein evolution at the Adh locus in Drosophila. Nature, 351(6328), 652-654. https://doi.org/10.1038/351652a0
• Messer, P. W., & Petrov, D. A. (2013). Frequent adaptation and the McDonald–Kreitman test. PNAS, 110(21), 8615-8620. https://doi.org/10.1073/pnas.1220835110
• Rand, D. M., & Kann, L. M. (1996). Excess amino acid polymorphism in mitochondrial DNA: contrasts among genes from Drosophila, mice, and humans. Molecular Biology and Evolution, 13(6), 735-748. https://doi.org/10.1093/oxfordjournals.molbev.a025634
• Smith, N. G. C., & Eyre-Walker, A. (2002). Adaptive protein evolution in Drosophila. Nature, 415(6875), 1022-1024. https://doi.org/10.1038/4151022a
• Stoletzki, N., & Eyre-Walker, A. (2011). Estimation of the Neutrality Index. Molecular Biology and Evolution, 28(1), 63-70. https://doi.org/10.1093/molbev/msq249

License

MIT License