mutme 0.1.2

Generic mutation and mutation-of-interest calling from consensus genomes

mutme

mutme is a lightweight Python command-line tool for detecting and annotating mutations of interest in viral consensus genome sequences.

It runs Nextclade against a user-supplied reference genome and GFF3 annotation, then matches amino-acid substitutions, deletions, insertions, and premature stop codons against a curated mutation annotation table.

mutme helps you answer a simple question at scale:

“Which mutations of interest are present in my consensus genomes?"

It can also provide generic annotations associated with each mutation, for example antiviral-resistance mutations, lineage markers, or any other genotype/phenotype you're interested in.

You provide:

• a reference genome (FASTA)
• a genome annotation (GFF3)
• a mutation annotation table (CSV/TSV)
• a query sequence file containing the consensus genomes (FASTA)

[!IMPORTANT] mutme is intentionally generic and database-agnostic. It does not attempt to interpret biological meaning beyond what you encode in your annotation table.

Key Features

• Exact string matching against mutation-of-interest tables
• Supports:
  • Amino-acid substitutions
  • Deletions
  • Insertions
  • Premature stop codons
• Robust handling of multiple annotation rows per mutation
• Long-format output suitable for downstream analysis

Table of Contents

• Installation
  • Requirements
  • Install from source
  • Install with Anaconda
• Quick Start
• Annotations Table
  • How to write the annotations table
  • Mutation encoding (what goes in the mutation column)
• Output Table
• Examples
  • I only care if these mutations are present
  • I want to annotate mutations with mAb susceptibility values
• Command reference
  • mutme run
  • mutme subset-gff3
• External Dependencies
• Citation

Installation

Requirements

• Python >= 3.10
• Nextclade >= v3.18 installation available on $PATH

Install from source

pip install git+https://github.com/vidrl/mutme

Install with Anaconda

mamba create -n mutme -c conda-forge -c bioconda python=3.14 nextclade=3.18
mamba activate mutme
pip install git+https://github.com/vidrl/mutme

Directly from repository:

mamba create -f https://raw.githubusercontent.com/vidrl/mutme/main/conda.yml

Quick Start

mutme run \
  --sequences sequences.fasta \
  --reference reference.fasta \
  --gff reference.gff3 \
  --annotations mutations.csv \
  --output results.csv

Notes:

• --sequences can contain one or many consensus sequences.
• Output will contain a seq_name column so you can see which result belongs to which input sequence.

Annotations Table

How to write the annotations table

Your annotation table must have a mutation column (case-insensitive). Everything else is optional — add as many extra columns as you like. One special case is a comment column, which can contain e.g. links to the mutation phenotype study.

Mutation encoding (what goes in the mutation column)

Gene names must be defined in the GFF3 (see below). If CDS are included ensure that the Name attribute matches the gene name used in the annotation table. Nextclade will prefer the Name attribute for amino acid mutation prefixes.

• Substitution: {gene}:{aa}{pos}{aa} (S:N87Y)
• Deletion: {gene}:{pos}- or {gene}:{aa}{pos}- (S:87- or S:N87-)
• Insertion: {gene}:{pos}{aa-ins} (S:214:EPE)
• Stop codon: {gene}:{aa}{pos}* or {gene}:{pos} (S:N87* or S:87)

Output Table

Default output columns (always present):

• seq_name - the FASTA record name (so multiple sequences are kept separate)
• seq_quality - Nextclade QC overall status
• mutation - the matched mutation from your annotation table

If your annotation table has extra columns, those columns are included too.

Examples

“I only care if these mutations are present”

Input annotation table (CSV)

mutation
S:N87Y
S:87-
S:214:EPE
S:87

Output table (example)

seq_name,seq_quality,mutation
sample_01,good,S:N87Y
sample_01,good,S:87-
sample_02,mediocre,S:214:EPE
sample_03,good,S:87

"I want to annotate mutations with mAb susceptibility values”

Here’s a more “real” example with multiple numeric columns. The values below are just an illustration of what your table can look like.

Input annotation table (CSV)

mutation,casirivimab_fold,imdevimab_fold,sotrovimab_fold,comment
S:E484K,12.5,8.1,1.2,Reduced neutralization for several mAbs
S:K417N,3.4,1.1,0.9,May reduce some class 1 mAbs
S:214:EPE,,,,Insertion seen in some lineages
S:87,,"",,Premature stop at position 87
S:87-,,"",,Deletion at position 87

[!TIP] Columns can be floats, ints, strings, or left empty. If you include a comment column, you can choose to carry it into the output with --include-comments.

Output table (example with --include-comments)

seq_name,seq_quality,mutation,casirivimab_fold,imdevimab_fold,sotrovimab_fold,comment
sample_01,good,S:E484K,12.5,8.1,1.2,Reduced neutralization for several mAbs
sample_01,good,S:214:EPE,,,,Insertion seen in some lineages
sample_02,good,S:K417N,3.4,1.1,0.9,May reduce some class 1 mAbs
sample_03,mediocre,S:87,,"",,Premature stop at position 87
sample_03,mediocre,S:87-,,"",,Deletion at position 87

[!NOTE] The output keeps your original columns (and their values) attached to each mutation hit. A sequence with no hits won’t appear in the output unless you choose to emit empty rows (not enabled by default).

Command reference

mutme provides two main commands: run (the core workflow) and subset-gff3 (a helper for trimming GFF3 files based on mutations in an annotation table).

mutme run

Run Nextclade using a custom reference and GFF3, then match detected amino-acid mutations against an annotation table.

Basic usage

mutme run \
  --sequences sequences.fasta \
  --reference reference.fasta \
  --gff reference.gff3 \
  --annotations mutations.csv \
  --output results.csv

[!NOTE] Input sequence file sequences.fasta may contain one or many consensus sequences. Each FASTA record is processed independently and results are distinguished by sequence name in the output table.

---

Required options

Option	Description
--sequences, -s	Input FASTA with one or more consensus sequences
--reference, -r	Reference genome FASTA
--gff, -g	Genome annotation in GFF3 format
--annotations, -a	Mutation annotation table (CSV/TSV)
--output, -o	Output file path (CSV/TSV)

---

Common optional options

Option	Description
--annotations-delimiter	Delimiter used by annotation table (default ,, use \t for TSV)
--output-delimiter	Delimiter for output table (default ,)
--include-comments, -c	Include a comment column in output if present in annotation table
--alignment-preset	Nextclade alignment preset (default, high-diversity, short-sequences)
--nextclade-bin	Path or name of Nextclade executable (default nextclade)
--nextclade-extra-args	Extra arguments passed directly to Nextclade
--nextclade-keep-tsv	Keep intermediate Nextclade TSV output

---

Example: TSV annotations, CSV output

mutme run \
  -s sequences.fasta \
  -r reference.fasta \
  -g reference.gff3 \
  -a annotations.tsv \
  -o results.csv \
  --annotations-delimiter '\t'

---

Example: keep Nextclade output for debugging

mutme run \
  -s sequences.fasta \
  -r reference.fasta \
  -g reference.gff3 \
  -a mutations.csv \
  -o results.csv \
  --nextclade-keep-tsv

---

mutme subset-gff3

Subset a GFF3 file to only genes (and optionally CDS features) referenced by mutation prefixes in an annotation table.

Basic usage

mutme subset-gff3 reference.gff3 mutations.csv --output subset.gff3

---

How it works

• Reads the annotation table
• Extracts prefixes from the mutation column (e.g. S from S:E484K)
• Keeps matching GFF3 records using gene or CDS features
• Preserves GFF3 headers and comments

---

Required arguments

Argument	Description
gff3_path	Input GFF3 file
table_path	Annotation table (CSV/TSV)

---

Common optional options

Option	Description
--output, -o	Output GFF3 path (if omitted, no file is written)
--delimiter, -d	Annotation able delimiter (default ,, use \t for TSV)
--mutation-column	Name of mutation column (default mutation)
--prefix-separator	Separator used in mutation strings (default :)
--no-strip-prefixes	Do not strip whitespace around prefixes
--gene-feature-type	GFF3 feature type treated as genes (default gene)
--gene-attr-key	GFF3 attribute used to match gene features to annotation prefix (default gene)
--include-cds	Also include CDS features
--cds-feature-type	GFF3 feature type for CDS records (default CDS)
--cds-gene-attr-key	GFF3 attribute key used to match CDS features (default gene)
--json	Emit machine-readable JSON summary instead of text

---

Example: subset genes only

mutme subset-gff3 \
  reference.gff3 \
  mutations.csv \
  --output subset.gff3

---

Example: include CDS records and emit JSON summary

mutme subset-gff3 \
  reference.gff3 \
  mutations.tsv \
  --include-cds \
  --output subset.gff3 \
  --json

---

Example: inspect prefixes without writing a file

mutme subset-gff3 \
  reference.gff3 \
  mutations.csv

This prints summary statistics but does not write an output GFF3.

External Dependencies

mutme relies on Nextclade for sequence alignment and mutation calling.

Aksamentov et al. (2021) - Nextclade: clade assignment, mutation calling and quality control for viral genomes - Journal of Open Source Software

Citation

If you use mutme in published work, please cite Nextclade (see link above) and acknowledge this repository.