AccuSNV
AccuSNV is a computational pipeline that identifies single nucleotide variants (SNVs) in short-read whole genome sequencing data between genomes in a group of bacterial isolates.
AccuSNV takes FASTQ files from three or more bacterial isolates and a reference genome, and returns the genomic SNVs at which those isolates differ from each other, instead of just differences from references.
The pipeline then builds a parsimony phylogenetic tree from these SNVs, calculates the mutational distance to the most recent common ancestor (dMRCA), reports the impact on protein sequence for each SNV, and calculates dN/dS ratios.
AccuSNV classifies candidate SNV sites as true or false SNVs using a convolutional neural network (CNN) trained on real sequencing data from bacterial isolates to distinguish true variation from mapping errors, indels, or sequencing noise. It also runs a set of rule-based quality checks on each site, inherited from the WideVariant pipeline. Both the CNN and simple rules are reported for every position, so you can always see why a SNV was kept or dropped.
Where to start
Install AccuSNV, then run the two-minute test dataset that ships with the repository. The test dataset runs within a few minutes, to make sure the pipeline is working on your system.
Input files covers the sample sheet, how AccuSNV finds your FASTQ files, and how to prepare your reference genomes. Running AccuSNV covers local runs, cluster runs, and re-runs.
Start with the final SNV table, which documents every column. Output files explains everything else the run created.
Phylogeny and dMRCA explains how phylogenies are built and dNdS calculation explains how dN/dS ratios are calculated.
The basics
Set up the AccuSNV environment:
conda create -n accusnv python=3.12
conda activate accusnv
conda install -c conda-forge -c bioconda accusnv
Run it in local mode on the test data:
accusnv -m local -i Test_data/samples_cae_test_pe.csv -r Test_data/reference_genomes -o cae_accusnv_output
Run it on an HPC system with a SLURM scheduler:
accusnv -m slurm -sp <partition> -i Test_data/samples_cae_test_pe.csv -r Test_data/reference_genomes -o cae_pe_test_snakemake
Output files
AccuSNV creates a number of potentially useful intermediate and output files. You can read about all of them under Output files. However, the most important ones are:
File |
Description |
|---|---|
|
SNV calls, one row per position, with gene and protein annotation. This is the main result. |
|
An interactive web page for examining SNVs and showing them on an interactive tree. |
|
A table that also includes sites that were considered, but rejected, as potential SNVs. |
|
A maximum-parsimony tree of the isolates in Newick format. |
|
Genome-wide and per-gene dN/dS calculations. |
|
Per-sample distance to the inferred common ancestor (dMRCA). |
All AccuSNV command line parameters
usage: accusnv [-h] [--version] [-i CSV] [-r DIR] [-o DIR] [--exclude_positions FILE] [--include_positions FILE] [-c FILE] [-p FILE] [-m {dryrun,slurm,local}] [-j N] [-sp PARTITIONS] [--skip_samclip] [-e CMD] [output options]
AccuSNV v1.1.0
High-accuracy SNV calling for bacterial isolates using deep learning.
options:
--version Show version and exit
Inputs and Outputs:
-i, --input_sample_info CSV Input sample CSV (required)
-r, --ref_dir DIR Reference genomes dir (required)
-o, --output_dir DIR Output dir (default: accusnv_output)
--exclude_positions FILE File of genome_pos values, one per line, to exclude from the SNV set regardless of the model calls. Combine with --downstream_only to change the list without re-calling
SNVs.
--include_positions FILE File of genome_pos values, one per line, to keep in the SNV set regardless of the model calls.
Config:
-c, --config_file FILE Execution settings (config.yaml; default: autogenerated in out_dir)
-p, --pipeline_file FILE Pipeline params (pipeline.yaml; default: autogenerated in out_dir)
-m, --mode {dryrun,slurm,local} Run mode (default: local)
-j, --cores N Cores per cutadapt/mapping job, or the total cores a local run uses (default: the cores value in pipeline.yaml, 4)
-sp, --partition PARTITIONS SLURM partition(s) to submit to, comma-separated list for >1 (default: not specified)
--skip_samclip Do not pipe bwa alignments through samclip, which drops soft-clipped reads (on by default with bwa; samclip is never used with bowtie2)
-e, --env CMD Environment activation command, e.g. 'conda activate accusnv' (default: inherit current environment)
Output options:
--skip_all_downstream Skip all downstream evolutionary analyses and report generation
--skip_report Skip generating the HTML report
--skip_recombination Skip recombination detection. On by default: recombinant SNVs are flagged and excluded frmo dN/dS, tree building and dMRCA. This stops excluding them
--skip_dnds Skip dN/dS calculations
--skip_trees Skip parsimony tree building (dnapars)
--build_snv_trees Write one NEXUS tree per SNV, tips coloured by basecall, for viewing in FigTree (default: off)
--downstream_only Run only downstream analyses (Assumes AccuSNV SNV tables and output directory already exist)
Citation
Liao, Herui, Arolyn Conwill, Ian Light-Maka, Martin Fenk, Alyssa H. Mitchell, Evan B. Qu, Paul Torrillo, Jacob S. Baker, Felix M. Key, and Tami D. Lieberman. “High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV.” Genome Research, June 2026, Vol. 36, No. 6. doi:10.1101/gr.281341.125
If you have any questions and bug reports, please report them on the GitHub issue tracker, or email Herui Liao (herui728@mit.edu) or the maintainer Alex Crits-Christoph (crits@mit.edu).