coVar

coVar is a tool for detecting physically-linked mutations in wastewater genomic sequencing data. Given a sorted, indexed BAM file, reference genome and gene annotation, coVar identifies and counts sequencing reads with unique physically linked mutations.

Installation

Bioconda (recommended)

conda install -c bioconda covar
covar --version

crates.io (recommended)

cargo install covar
covar --version

Local build from source (experimental)

git clone https://github.com/andersen-lab/covar.git
cd covar
cargo install --path .
covar --version

Usage

covar --input <INPUT_BAM> --reference <REFERENCE_FASTA> --annotation <ANNOTATION_GFF>

Required arguments

Flag	Description
-i, --input <INPUT_BAM>	Input BAM file (must be primer trimmed, sorted, and indexed).
-r, --reference <REFERENCE_FASTA>	Reference genome in FASTA format.
-a, --annotation <ANNOTATION_GFF>	Annotation GFF3 file for translating nucleotide to amino acid mutations.

Optional arguments

Flag	Default	Description
-o, --output <OUTPUT>	stdout	Output file path. If not provided, results will be printed to stdout.
-s, --start_site <START>	0	Genomic start position for variant calling.
-e, --end_site <END>	reference length	Genomic end position for variant calling. Defaults to the length of the reference genome.
-d, --min_depth <DEPTH>	1	Minimum coverage depth for a mutation cluster to be considered.
-f, --min_frequency <FREQ>	0.001	Minimum mutation frequency (cluster depth / total depth).
-q, --min_quality <QUAL>	20	Minimum base quality score for variant calling.
-t, --threads <THREADS>	1	Number of threads to use for processing.

Example Commands

Basic run

covar \
  -i sample.bam \
  -r reference.fasta \
  -a annotation.gff3

Specify genomic region and output file

covar \
  -i sample.bam \
  -r reference.fasta \
  -a annotation.gff3 \
  -s 1000 \
  -e 5000 \
  -o output.tsv

Multi-threaded run with custom depth, quality and frequency thresholds

covar \
  -i sample.bam \
  -r reference.fasta \
  -a annotation.gff3 \
  -d 5 \
  -q 30 \
  -f 0.01 \
  -t 4

Output

The output is a tab-delimited file (.tsv) with the following columns:

Column	Description
nt_mutations	Nucleotide mutations for this cluster
aa_mutations	Corresponding amino acid translations (where possible*)
cluster_depth	Total number of read pairs with this cluster of mutations
total_depth	Total number of reads spanning this cluster
frequency	Mutation frequency (cluster depth / total depth)
coverage_start	Maximum read start site for which this cluster was detected
coverage_end	Minimum read end site for which this cluster was detected

*Note: Not all nucleotide mutations will have a corresponding amino acid mutations. For example, SNPs in codons that span reads or frameshift indels will be translated as 'Unknown' and 'NA', respectively.