- What is ConsensuSV?
- Citation
- Requirements
- Parameters
- Structure of the data folder
- Comparison to gold-standard set
- Examples
The tool designed for getting consensus out of multiple SV callers' results.
Important: for the completly automatised fastq-to-vcf (8 SV callers + SNP / Indel calling included) pipeline see: https://github.com/SFGLab/ConsensuSV-pipeline
If you use ConsensuSV in your research, we kindly ask you to cite the following publication:
@article{Chilinski_ConsensuSVfrom_the_whole-genome_2022,
author = {Chiliński, Mateusz and Plewczynski, Dariusz},
doi = {10.1093/bioinformatics/btac709},
journal = {Bioinformatics},
title = {{ConsensuSV—from the whole-genome sequencing data to the complete variant list}},
year = {2022}
}
Requirements:
- bcftools (https://samtools.github.io/bcftools/) in PATH
Options:
| Short option | Long option | Description |
|---|---|---|
| -f | --sv_folder | older containing folders of samples with raw outputs from SV callers (comma-separated). More information on the structure of the samples folder is shown below. |
| -mod | --model | Model used for SV discovery (default: pretrained.model). |
| -o | --output | Output file prefix (default: consensuSV_). |
| -m | --min_overlap | File with minimum numbers of SVs in the neighbourhood for the SV to be reported (default min_overlaps). |
| -of | --output_folder | Output folder (default: "output/"). |
| -s | --samples | Samples to include. By default all in the sv_folder. Comma-separated |
| -c | --callers | Callers to include. By default all in the folders. Comma-separated. |
| -t | --train | Creates new model. Requires truth.vcf to be present in all the sv folders. VCF file truth.vcf is preprocessed even if flag --no_preprocess is set. If the model is trained, it is required to rerun the program to get the consensus. |
| -np | --no_preprocess | Flag used for skipping the preprocessing process - all the preprocessed files should be in temp/ folder. |
The samples should follow the rule seen in the following figure:
The workflow of the algorithm is presented in the following figure:
The example command used for the training of the neural network model:
python main.py -f /home/ConsensuSV/data/ -tThe example command used for getting the consensus SVs (the model included in the package is trained on the 11 SV callers shown on the example sample folder structure):
python main.py -f /home/ConsensuSV/data/ -o consensuSV