Genome-wide Reconstruction of Complex Structural Variants, or GROC-SVs, is a software pipeline for identifying large-scale structural variants, performing sequence assembly at the breakpoints, and reconstructing the complex structural variants using the long-fragment information from the 10x Genomics platform.

Prerequisites: the following programs must be installed prior to running GROC-SVs:

• python -- GROC-SVs has been tested on python 2.7 on Mac and Linux; it is not compatible with python 3.x
• idba_ud -- please use this version, as the version distributed by the original author does not support paired reads longer than 128 bp
• samtools and htslib -- version 1.0 or later of the samtools, bgzip, and tabix programs must all be in your $PATH
• bwa-mem
• graphviz (required for pygraphviz visualization; see here if you have trouble installing)
• (optional): rpy2 which is required for generation of the visualizations; this stage will be skipped if rpy2 can't be found. (Note that you may re-start the pipeline after installing rpy2 in order to complete this step, picking up where it left off.)

We recommend setting up a virtualenv prior to installing GROC-SVs (or using virtualenvwrapper):

sudo pip install virtualenv
virtualenv grocsvs_env

The virtualenv can be activated by running the following command:

source grocsvs_env/bin/activate

Then, to install grocsvs:

cd /path/to/grocsvs
pip install .

To test that GROC-SVs is installed correctly, you can simply run grocsvs from the commandline, which should show help text without any error messages.

Overview:

1. extract barcodes and align your 10x sequencing data to the reference genome
2. setup a configuration.json file describing your samples and your compute (eg cluster) setup
3. run grocsvs

There are two options to align 10x Genomics data to the reference genome for downstream use by GROC-SVs. The simplest option is to use the accompanying simple_demux_map script followed by read alignment using bwa mem. Note that this will extract the 10x droplet barcodes for use by GROC-SVs (optionally demultiplexing pooled samples) but does not perform barcode-aware read alignment.

The second option is to use the 10x Genomics longranger align pipeline, which can optionally perform barcode-aware alignment. While not necessary, the full 10x longranger pipeline may be run, which adds phasing information that GROC-SVs can include in its analysis.

Because GROC-SVs can analyze multiple samples jointly, and each sample can involve multiple input files, GROC-SVs uses a configuration file to specify inputs and settings for each run. See the examples directory for an example configuration.json file.

The configuration file is in the JSON format, and contains the following three parts:

1. reference genome paths
2. sample information
3. compute cluster settings

Reference genome The following paths must be defined:

• ref_fasta: path to the reference genome FASTA file (hg19.fa, GRCh38.fa, etc)
• bwa_index: path to the genome index used by bwa-mem

In addition, the following optional paths may be specified:

• blacklists: a list of paths of blacklist regions, either bed or bedpe format
• binaries: a hash containing paths for any of the following binaries: idba_ud, bwa, samtools, bgzip, tabix. If these are in your $PATH, there is no need to specify them in the configuration file.

Sample information Samples is a hash with key specifying sample name, and the value is a list of datasets. Each sample must have one 10x dataset specified, and may optionally specify a separate standard Illumina short-frag library or a mate-pair library (these are used for validation and comparison only).

Each dataset is defined as a hash. 10x datasets must define the following items:

• bam: the path of the bam file produced by longranger. From the root longranger output directory, this is typically the file $ROOT/outs/possorted_bam.bam.
• id: this is a name used to identify the dataset; typically, it would be something like "sample_name_10x"
• type: this should be the string "TenXDataset"

Tumor/normal, trio and other multi-sample analyses GROC-SVs performs variant calling jointly on all samples specified in the configuration.json file, and no additional arguments are required to indicate the biological meaning of the sample. See the Output section below for descriptions of the "genotypes.tsv" and "classes.txt" files, which can be filtered in order to obtain events that are somatic (ie private to the tumor, and not present in the germline sample) or de novo in the child (ie private to the child and not present in either parent).

Compute cluster settings This has defines the compute environment being used to perform the analysis. A standard cluster setup looks like this:

"cluster_settings": {
    "cluster_type": "IPCluster",
    "processes": 128,
    "cluster_options": {
        "scheduler": "slurm",
        "queue": "normal",
        "start_wait": 120,
        "extra_params": {"mem":16}
    }
}

Where processes specifies the maximum number of separate jobs (1 processor per job) to allow. scheduler may be any of the clusters supported by ipython-cluster-helper. Currently, these are Platform LSF ("lsf"), Sun Grid Engine ("sge"), Torque ("torque"), and SLURM ("slurm").

Note that the optional start_wait parameter determines how long grocsvs will wait for jobs to start running after they have been submitted to the scheduler. If you expect particularly long queueing times, you can set this to a much higher value - the default is 16 minutes (rather short for most cluster setups!) and as shown in the example above, it's been set to 120 minutes.

To run in parallel on a single machine, use cluster_type":"multiprocessing" and specify the desired number of processes.

To override the cluster options in the configuration.json file, use --local to specify single-core mode or --multiprocessing to specify running in parallel using all cores on a single machine.

To run GROC-SVs, use the grocsvs /path/to/experiment/configuration.json command. If you are using a virtualenv, remember to run source grocsvs_env/bin/activate to activate the virtualenv prior to running grocsvs.

The output will be placed in the directory containing configuration (in this case, in /path/to/experiment/), so make sure this filesystem has enough space for the analysis (~40GB per sample). GROC-SVs typically requires about 12-16 GB of memory in order to run, though this depends on your samples. If you have less than 16 GB of memory available on your machine, a warning will be output but the pipeline will continue to run as best as it can.

Note that the grocsvs command will continue running until all steps have completed. The grocsvs command itself is lightweight, and so can be run from a head node on your cluster.

Logging output for each step will be put in /path/to/experiment/logs. The final results will be put in /path/to/experiment/results.

Final results of interest might be:

• results/MergeGenotypesStep/genotypes.tsv: the structural variant calls, including coordinates, information on which samples are positive for each event, which events together form complex events, and some filtering information (eg blacklist annotations provided above, genome gaps, etc) to remove potential false-positives
• results/QCStep/qc_report.tsv: some basic quality control statistics, including fragment lengths and number of barcodes per sample
• results/AssemblyStep/assembly.i: the sequence assemblies for event i; in this directory, contigs.sorted.bam contain the contigs aligned back to the reference genome (this file may be viewed with IGV)
• results/FinalClusterSVsStep/edges.tsv: full information relating breakpoints in complex structural variants
• results/PostprocessingStep/classes.txt: this file includes a simple presence/absence call for each structural variant for each sample, denoted as a 0 for absence and a 1 for presence. For example, if your tumor sample were the first sample, and the matched normal sample were the second sample, a "10" would indicate a somatic event and a "11" would indicate a germline event. These classes are determined using a simple allele-frequency cutoff which in our experience has been quite robust. More statistically motivated filters can be established by filtering on the p-values for each sample, which are indicated in this file as "sarcoma_p_resampling" if your sample name were "sarcoma" (note that missing p-values should be treated as 1).

A docker image is available for grocsvs. If you wish to download and run grocsvs on an example dataset (~1.3GB required), you can run the following commands:

# use 'curl -O' if you're on a mac without wget
wget http://mendel.stanford.edu/public/noah/grocsvs_example.tar.gz
tar -xzf grocsvs_example.tar.gz

Assuming docker is installed, the following command can be used to analyze the example data from within docker (make sure you are in the same directory where you downloaded and extracted grocsvs_example.tar.gz):

docker run -v `pwd`:/data -w /data/grocsvs_example/ grocsvs/grocsvs-docker grocsvs configuration.json --local

This requires ~16GB of memory to run and will take ~1 hour to complete. If you are running docker for Mac, please make sure that your virtual machine has access to at least 16GB of memory.

The output can be found in grocsvs_example/results.

Briefly, GROC-SVs was designed to detect and characterize complex structural variants such as those frequently found in cancer or in orphan diseases. The Long Ranger software available from 10x Genomics can also perform SV detection using inferred long-fragment sequence information, but is more well-suited to analysis of individual germline genomes - eg, Long Ranger includes a module to detect modest-sized deletions common in the germline. Note that both GROC-SVs and Long Ranger are being actively developed, and so some features may migrate between packages.

GROC-SVs:

• performs sequence assembly of structural variants
• reconstructs large-scale complex structural variants
• is designed for multi-sample analyses (tumor/normal, or trios) - this is important when identifying somatic or de novo germline events, as analyzing multiple samples separately can result in false negative calls in the control or parent samples

The grocsvs /path/to/experiment/configuration.json command may be run multiple times to resume the pipeline.

If you are having trouble installing or running grocsvs, the docker file (see above) may help you diagnose the issue.

If an error arises, the output from grocsvs or the log files may be informative.

ShortSequence: Sequence is too long. If you get this error during assembly, please make sure you are using the grocsvs fork of idba_ud.

Please submit issues on the github page for grocsvs.

Make sure that you have Conda installed (miniconda <https://docs.conda.io/en/latest/miniconda.html>)

conda create -n groc python=2.7

conda activate groc

conda install -c conda-forge pygraphviz -y

conda install -c bioconda pybedtools tabix idba samtools bwa bwa-mem2 htslib -y

Clone the environment and then make sure to use the modified setup.py or change the requirement to networkx==2.0

pip install .

To pack the environment

conda install conda-pack -y

conda-pack -n groc -o groc.tar

Copy and install the file

tar xf /PATH/groc.tar -C /PATH/.conda/envs/groc/

conda activate or source /PATH/.conda/envs/groc/

conda unpack