This repository contains the full script developed by Talkowski Lab to detect structural variants.
As a snakemake workflow, the pipeline is intended to be cloned for each project, e.g.
git clone https://github.com/talkowski-lab/SV-Adjudicator.git MySVDiscoveryProject
Create and activate a new Anaconda environment with all the necessary dependencies.
cd MySVDiscoveryProject
conda env create -f environment.yaml
source activate sv_pipeline
After cloning the pipeline, edit config.yaml to update the configuration as
necessary for the project, then link or copy raw data into the data/ or
ref/ directories. (More detail below or to come. For current testing
purposes, symlink the data/ and ref/ directories in
/data/talkowski/Samples/SFARI/deep_sv/asc_540/sv-pipeline-devel/).
Optionally, run a dry run of snakemake to test the configuration, then run
the pipeline with snakemake.
$ vim config.yaml
$ ln -s ${raw_SV_calls} data/
$ cp ${reference_data} ref/
$ snakemake -np
$ snakemake
The pipeline can remove all files it has generated without affecting configuration or data files.
$ snakemake clean
svtools is required for the process of SV-Adjudicator and is included in this repository. To install svtools,
cd svtools
python setup.py install --user
For more details about svtools, refer to the subfolder.
If you would prefer to use your own python environment, the following packages are necessary to run the pipeline.
The pipeline requires Python 3. If you already have an established Python 3 environment, you can ignore this subsection. Otherwise, the recommended way to create a Python 3 environment with the required packages is with Anaconda.
$ conda create -n $environment -c bioconda python=3.5 numpy scipy pysam snakemake
$ source activate $environment
The pipeline is built with snakemake, which can be installed through pip or
conda with one of the two following commands.
$ pip install snakemake
$ conda install -c bioconda snakemake
snakemake is an excellent and recommended tool for building bioinformatics
pipelines, but a comprehensive understanding of the tool is not necessary to
run this pipeline. If interested in learning more, extended snakemake
documentation can be found on their Read the Docs
page. A
tutorial and
demonstrative slides are also
available.
The pipeline requires the svtools Python package, which is currently
available only on github.
$ git clone [email protected]:talkowski-lab/svtools.git
$ cd svtools
$ pip install -e .
The pipeline requires bedtools 2.26 or later. Earlier versions may throw an
error when bedtools merge is passed an empty file.
In order to perform per-chromosome parallelization, the master branch of
pybedtools is required (or at least commit b1e0ce0).
$ pip install git+git://github.com/daler/pybedtools.git@master
$ pip install git+git://github.com/daler/pybedtools.git@b1e0ce0
The pipeline consists of multiple independent modules. Documentation of each module is provided in the respective subdirectory.
- preprocessing
- algorithm_integration
- 02_evidence_assessment
- variant_filtering
- variant_resolution
- annotation
The preprocessing module here is provided for reproducibility and as an example implementation of SV algorithm standardization, but is not intended to be generalizable to all use cases.
If you would like to implement your own preprocessing before beginning integration and filtering, the pipeline can be bootstrapped to begin with the integration module by providing the following input files: