Structural variation analysis of 10X Genomics linked-read data

Snakemake workflow for structural rearrangement analysis of 10X Genomics linked-read WGS


This workflow will run the SvABA structural variation (SV) analysis for a set of tumor-normal pairs, starting from the BAM files aligned using Long Ranger software. The analysis includes structural variant prediction and assessment of barcode (BX) overlap from 10X linked-reads. It will also look for TitanCNA results and combine these results to output SV classes.
This analysis was used and described in the publication:
Viswanathan SR, Ha G, Hoff A, et al. Structural Alterations Driving Castration-Resistant Prostate Cancer Revealed by Linked-Read Genome Sequencing. *Cell 174, 433–447.e19 (2018).


Gavin Ha
Fred Hutchinson Cancer Research Center
contact: or
Date: August 7, 2018


Software packages or libraries

  • R-3.4 -optparse
  • Python 3.4
    • snakemake-3.12.0
  • samtools-1.3.1

Files in the workflow

Scripts used by the workflow

The following scripts are used by this snakemake workflow:

Tumour-Normal sample list config/samples.yaml

The list of tumour-normal paired samples should be defined in a YAML file. In particular, the Long Ranger (v2.2.2) analysis directory is listed under samples. See config/samples.yaml for an example. Both fields samples and pairings must to be provided. pairings key must match the tumour sample while the value must match the normal sample.

  tumor_sample_1:  /path/to/tumor/longranger/dir
  normal_sample_1:  /path/to/normal/longranger/dir

  tumor_sample_1:  normal_sample_1

Running the analysis

1. Invoking the snakemake workflow for SvABA on a local machine

The first snakemake file svaba.snakefile will a. Run the full SvABA analysis b. Compute barcode overlap values for barcode rescue in the next step.

# show commands and workflow
snakemake -s svaba.snakefile -np
# run the workflow locally using 5 cores
snakemake -s svaba.snakefile --cores 5

2. Integrating SV and copy number results (TitanCNA)

The second snakemake file combineSvabaTitan.snakefile will a. Combine SvABA and TitanCNA (10X analysis) results to assign copy number to the breakpoints and rearrangement classes to the SV events. b. Plot copy number and SV for each chromosome.

  snakemake -s combineSvabaTitan.snakefile --cores 5

The third snakemake file plotSVandCNAzoom.snakefile generates custom plots zoomed in for a region of interest. Users can specify the coordinates in the configPlotZoom.yaml file. See the description of the configuration below.

  snakemake -s plotSVandCNAzoom.snakefile --cores 5

3. Invoking the snakemake workflow for SvABA on a cluster

If you are using a cluster, then these are resource settings for memory and runtime limits, and parallel environments.
These are the default settings for all tasks that do not have rule-specific resources.
There are two cluster configurations provided: qsub and slurm

i. qsub

There are 2 separate files in use for qsub, which are provided as a template: config/ - This file contains other qsub parameters. Note that these settings are used for the Broad’s UGER cluster so users will need to modify this for their own clusters.
config/cluster_qsub.yaml - This file contains the memory, runtime, and number of cores for certain tasks.

To invoke the snakemake pipeline for qsub:

snakemake -s svaba.snakefile --jobscript config/ --cluster-config config/cluster_qsub.yaml --cluster-sync "qsub -l h_vmem={cluster.h_vmem},h_rt={cluster.h_rt} -pe {} -binding {cluster.binding}" -j 100

Here, the h_vmem (max memory), h_rt (max runtime) are used. For runSvaba task, the default setting is to use 4 cores and this can be set with -pe and -binding. Your SGE settings may be different and users should adjust accordingly.

ii. slurm

There is only one file in use for slurm: config/cluster_slurm.yaml - This file contains the memory, runtime, and number of cores for certain tasks. To invoke the snakemake pipeline for slurm:

snakemake -s svaba.snakefile --cluster-config config/cluster_slurm.yaml --cluster "sbatch -p {cluster.partition} --mem={cluster.mem} -t {cluster.time} -c {cluster.ncpus} -n {cluster.ntasks} -o {cluster.output}" -j 50

Configuration and settings

All settings for the workflow are contained in config/config.yaml. The settings are organized by paths to scripts and reference files and then by each step in the workflow.

1. Path to tools

  • svaba_exe is the compiled SvABA executable
    svaba_exe:  /path/to/svaba
    samTools:  /path/to/samtools

2. Path to scripts

These are provided in this repo under code/.

tenX_funcs:  code/tenX_utils.R
svaba_funcs:  code/svaba_utils.R
plot_funcs:  code/plotting.R
bxRescue_script:  code/barCodeOverlap.R
combineSVCN_script:  code/combineSVABAandTITAN.R
plotSVCN_script:  code/plotTitanSvaba.R

3. Path to R package files

Specify the directory in which TitanCNA is installed.
Set these if the R files in these libraries have been modified or updated but not yet installed or updated in R.

titan_libdir:  /path/to/TitanCNA/

4. Path to TitanCNA 10X snakemake results

Specifies the TitanCNA 10X snakemake location containing result files to be merged with SvABA results in combineSvabaTitan.snakefile.

titan_results:  /path/to/TitanCNA/snakemake_results/

5. Reference files and settings

Global reference files used by the snakefiles and scripts.

  • refGenome specify the reference genome used in the Long Ranger analysis
  • genomeStyle specifies the chromosome naming convention to used for output files. Input files can be any convention as long as it is the same genome build. Only use UCSC (e.g. chr1) or NCBI (e.g. 1).
  • cytobandFile is used for plotting the chromosome idiograms and only needs to specify data/cytoBand_hg38.txt if using hg38.
  • chrs specifies the chromosomes to analyze; users do not need to be concerned about chromosome naming convention here as the code will handle it based on the genomeStyle.
    refGenome:  /path/to/ref/genome.fasta
    genomeBuild:  hg38
    genomeStyle:  UCSC
    cytobandFile:  data/cytoBand_hg38.txt # only need if hg38
    chrs:  c(1:22, \"X\")

6. Long Ranger bam files

Set this to the filenames that are used for the BAM files generated by Long Ranger. The current filenames are ones generated by Long Ranger v2.2.2

bamFileName:  phased_possorted_bam.bam

7. svaba.snakefile settings: SvABA

The cluster resources for SvABA are set here. 3G of memory for each of the 4 cores totals to 12G set as the limit.

svaba_dbSNPindelVCF:  /path/to/dbsnp_indel.vcf
svaba_numThreads:  4  # should match cluster settings for number of cores

8. svaba.snakefile settings: Barcode counting

Minimum thresholds and settings for barcode (BX) counting.

  • bxRescue_minLength sets minimum length of the intra-chromosomal SV event to be consider for barcode counting. Shorter events are excluded, except for fold-back inversions.
  • bxRescue_windowSize sets the window size region to the left or right of the breakpoint for barcode counting.
    bxRescue_minMapQ:  20
    bxRescue_minLength:  10000
    bxRescue_windowSize:  1000
    bxRescue_minReadOverlapSupport:  2

9. combineSvabaTitan.snakefile settings: Plotting

Settings used for plotting copy number and SV results.

  • plot_geneFile is a text file listing the regions to annotate in the plot. 4 column file: name, chr, start, stop.
    plot_zoom:  FALSE
    plot_chrs:  c(1:22, \"X\") # "None" will also plot all chromosomes
    plot_startPos:  None
    plot_endPos:  None
    plot_geneFile:  data/AR_coord.txt ## include list of genes to annotate on plot
    plot_ylim:  c(-2,6)
    plot_size:  c(8,4)
    plot_type:  titan ## titan - will include haplotype fraction
    plot_format:  png

10. plotSVandCNAzoom.snakefile settings in configPlotZoom.yaml

  • plot_id to use for naming the output directory containing the zoomed plots for each sample.
  • plot_zoom indicates the plot should be focused on a specific region smaller than a whole chromosome. If set to TRUE, then plot_chrs (should only be a single chr), plot_startPos, plot_endPos should be set.
  • ‘plot_type is set to ichor for total copy number only (black dots). Set it to titan` to also include the allelic fraction panel as a second track.
    plot_id: AR_enhancer_zoom
    plot_zoom:  TRUE
    plot_chrs:  c(\"X\") 
    plot_startPos:  66500000
    plot_endPos:  67900000
    plot_geneFile:  data/AR_coord.txt ## include list of genes to annotate on plot
    plot_ylim:  c(-2,6)
    plot_size:  c(8,4)
    plot_type:  ichor ## use "titan" to also plot the allelic fraction panel 
    plot_format:  png