VILOCA is an open source project for the analysis of next generation sequencing data. It is designed to analyse genetically heterogeneous samples. Its tools are written in different programming languages and provide error correction, haplotype reconstruction and estimation of the frequency of the different genetic variants present in a mixed sample. VILOCA takes an alignment file as input, and subsequently generates mutation calls and local haplotypes.
The corresponding manuscript can be found here: https://www.biorxiv.org/content/10.1101/2024.06.06.597712v1
For installation miniconda is recommended: https://docs.conda.io/en/latest/miniconda.html. We recommend to install VILOCA in a clean conda environment:
conda create --name env_viloca --channel conda-forge --channel bioconda viloca
conda activate env_viloca
If you want to install the master branch use:
conda create --name env_viloca --channel conda-forge --channel bioconda libshorah
conda activate env_viloca
pip install git+https://github.com/cbg-ethz/VILOCA@master
To test your installation run VILOCA tests/data_1:
viloca run -b test_aln.cram -f test_ref.fasta --mode use_quality_scores
Another example can be found in tests/data_6:
If the sequencing amplicon strategy is known, we recommend using the amplicon-mode of the program, which takes as input the <smth>.insert.bed - file:
viloca run -f reference.fasta -b reads.shotgun.bam -w 90 --mode use_quality_scores -z scheme.insert.bed
If there is no information on the sequencing amplicon strategy available, run:
viloca run -f reference.fasta -b reads.shotgun.bam -w 90 --mode use_quality_scores
If the sequencing quality scores are not trustable, the sequencing error parameters can also be learned:
viloca run -f reference.fasta -b reads.shotgun.bam -w 90 --mode learn_error_params.
There are several parameters available:
-b [mandatory] sorted bam format alignment file
-f [mandatory] reference genome in fasta format for mutation calling
-z path to an (optional) insert file (primer tiling strategy), if available we highly recommend providing this file
--mode mode to use:
learn_error_params: model that is learning the error rate from the datause_quality_scores: model incorporating the sequencing quality scores that are passed through the alignment fileshorah: use the tool ShoRAH (https://github.com/cbg-ethz/shorah)
--extended_window_mode: flag to call insertions (default: this flag is turned off)
--exclude_non_var_pos_threshold: Percentage threshold for positions exclusion. Positions with base variations below this threshold will be excluded from the analysis, instead this position will be treated as if it only contains the reference base. This means that mutations of frequency < exclude_non_var_pos_threshold will not be called.
--windowsize: In case no insert file is provided, the genome is tiled into uniform local regions. windowsize determines the length of those local regions. It should be of roughly the length of the reads. This is also the length of the haplotypes that are produced.
haplotypes This directory contains the reconstructed local haplotypes as separate fasta files per local region.
coverage.txt List of each local region with start and end positions, and number of reads considered in the region.
cooccurring_mutations.csv All mutation calls including the information on which haplotype it occurred on.
The following command in the root directory will let you interact with the project locally through Docker.
docker run --name viloca --rm -w="/usr/app" -it $(docker build -q .) bashRun the following commands to copy the contents into the container and install VILOCA inside Docker.
docker cp . viloca:/usr/app # run outside Docker
poetry install --only-root # run inside DockerThis is the same setup as used in the CI at .github/workflows/test.yaml.
poetry run python3 -m cProfile -m shorah shotgun ...You can find several applications of VILOCA at https://github.com/cbg-ethz/viloca_applications.