MosaiCatcher pipeline

Structural variant calling from single-cell Strand-seq data - summarized in a Snakemake pipeline.

For Info on Strand-seq see

• Falconer E et al., 2012 (doi: 10.1038/nmeth.2206)
• Sanders AD et al., 2017 (doi: 10.1038/nprot.2017.029)

Overview of this workflow

This workflow uses Snakemake to execute all steps of MosaiCatcher in order. The starting point are single-cell BAM files from Strand-seq experiments and the final output are SV predictions in a tabular format as well as in a graphical representation. To get to this point, the workflow goes through the following steps:

1. Read binning in fixed-width genomic windows of 100kb via mosaicatcher
2. Normalization of coverage with respect to a reference sample (included)
3. Strand state detection (included)
4. Haplotype resolution via StrandPhaseR
5. Multi-variate segmentation of cells (mosaicatcher)
6. Bayesian classification of segmentation to find SVs using mosaiClassifier (included)
7. Visualization of results using custom R plots (included)

Installation

Choose one of three ways to install and run this workflow:

1. Install software using Bioconda

   • Installation instructions here
   • Configure Snake.conf.json according to your installtion
   • Add your single-cell data according to the specificaitons given below (Setup)

2. Run Snakemake together with a Singularity image

   • Instructions here
   • Requires Snakemake and Singularity. No further installations required
   • Add your single-cell data according to the specificaitons given below

3. Run a complete example data set via Docker

   • Requires Docker (tested in version 18.09)
   • Includes a whole data set of 96 RPE-1 cells
   • Example shown here

Setup

• Download this pipeline

   git clone https://github.com/friendsofstrandseq/pipeline
   cd pipeline

• Add your single-cell data

  Create a subdirectory bam/sampleName/. Your Strand-seq BAM files of this sample go into two folders:

  • all/for the total set of BAM files
  • selected/ for the subset of successful Strand-seq libraries (possibly hard-linked to all/)

  It is important to follow these rules for single-cell data

  • One BAM file per cell
  • Sorted and indexed
  • Timestamp of index files must be newer than of the BAM files
  • Each BAM file must contain a read group (@RG) with a common sample name (SM), which must match the folder name (sampleName above)

• Adapt the config file

  In Snake.conf.json you can specify

• SNP call set, if available If available, specify SNV calls (VCF) in Snake.config.json. Note that the sample name in the VCF must match the one in the BAM files.

Note: Multiple samples can be run simultaneously. Just create different subfolders below bam/. The same settings from the Snake.config.json config files are applied to all samples.

SNP calls

The pipeline will run simple SNV calling using samtools and bcftools on Strand-seq. If you already have SNV calls, you can avoid that by entering your VCF files into the pipeline. To so, make sure the files are tabix-indexed and specifigy them inside the Snake.config.json file:

"snv_calls"     : {
      "NA12878" : "path/to/snp/calls.vcf.gz"
  },

Installation using Singularity/Docker

Will be updated soon.