A toolkit to harmonize and filter structural variations across methods and samples.
Important: The document is under construction. We have tested HarmoniSV to perform population-scale SV calling using SV calls from Sniffles2, cuteSV, and SVIM. It should be able to work with any SV callers whose output follows VCF specification. Please open an issue for questions or bug reports.
- Harmonize SVs discovered by different SV calling methods
- Filter high-confidence SVs with a random forest classifier
- Fast VCF manipulation, annotation, and conversion
git clone https://github.com/Han-Cao/HarmoniSV.gitHarmoniSV is written in python3.8. The following python modules are required:
pysam
pandas
numpy
matplotlib
scikit-learn
pyranges
cd src/harmoniSV
./harmonisv
HarmoniSV: A toolkit to harmonize and filter structural variantions across methods and samples
Version: 0.1.0
Usage: harmonisv <command> [options]
Commands:
-- VCF manipulation
harmonize Harmonize SV VCFs from across samples and SV calling methods
harmonize-header Harmonize VCF headers
sample2pop Convert single-sample VCF to multi-sample VCF
intersect Intersect SVs with genomic features
-- Analysis on SV callset
represent Select the representative SV from merged SVs
genotype Genotype SVs across SV genotyping methods
filter Random forest filter for SVs
concordance Calculate genotype concordance between two VCFs
Note:
1. All input VCFs MUST follow the VCF specification
2. Some commands assume specific variant ID format to index SVs from different methods and samples,
please check the required ID format before you use
3. The input/output VCF format (i.e., vcf, vcf.gz, bcf) will be automatically detected. However, a
temporary uncompressed VCF file will be generated if the output is vcf.gz or bcf.
For help on a specific command, run:
harmonisv <command> -h