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

Quick installation on BIH

git clone --recurse-submodules https://github.com/pweidner/mosaicatcher-pipeline-BIH.git

mamba create -n snakemake -c bioconda -c conda-forge -c defaults -c anaconda snakemake=7.32.0

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. Binning of sequencing reads in genomic windows of 200kb via mosaic
2. Strand state detection
3. [Optional]Normalization of coverage with respect to a reference sample
4. Multi-variate segmentation of cells (mosaic)
5. Haplotype resolution via StrandPhaseR
6. Bayesian classification of segmentation to find SVs using MosaiClassifier
7. Visualization of results using custom R plots

MosaiCatcher snakemake pipeline and visualisations examples
ashleys-qc-pipeline

📘 Documentation

• Quick Start
• Usage
• Parameters & input
• Output
• Configuration file
• Update the pipeline

📆 Roadmap

Technical-related features

• Zenodo automatic download of external files + indexes (1.2.1)
• Multiple samples in the parent folder (1.2.2)
• Automatic testing of BAM SM tag compared to sample folder name (1.2.3)
• On-error/success e-mail (1.3)
• HPC execution (slurm profile for the moment) (1.3)
• Full singularity image with preinstalled conda envs (1.5.1)
• Single BAM folder with side config file (1.6.1)
• (EMBL) GeneCore mode of execution: allow selection and execution directly by specifying genecore run folder (2022-11-02-H372MAFX5 for instance) (1.8.2)
• Version synchronisation between ashleys-qc-pipeline and mosaicatcher-pipeline (1.8.3)
• Report captions update (1.8.5)
• Clustering plot (heatmap) & SV calls plot update (1.8.6)
• ashleys_pipeline_only parameter: using mosaicatcher-pipeline, trigger ashleys-qc-pipeline only and will stop after the generation of the counts, ashleys predictions & plots to allow the user manual reviewing/selection of the cells to be processed (2.2.0)
• Plotting options (enable/disable segmentation back colors)

Bioinformatic-related features

• Self-handling of low-coverage cells (1.6.1)
• Upstream ashleys-qc-pipeline and FASTQ handle (1.6.1)
• Change of reference genome (currently only GRCh38) (1.7.0)
• Ploidy detection at the segment and the chromosome level: used to bypass StrandPhaseR if more than half of a chromosome is haploid (1.7.0)
• inpub_bam_legacy mode (bam/selected folders) (1.8.4)
• Blacklist regions files for T2T & hg19 (1.8.5)
• ArbiGent integration: Strand-Seq based genotyper to study SV containly at least 500bp of uniquely mappable sequence (1.9.0)
• scNOVA integration: Strand-Seq Single-Cell Nucleosome Occupancy and genetic Variation Analysis (1.9.2)
• multistep_normalisation and multistep_normalisation_for_SV_calling parameters to replace GC analysis module (library size normalisation, GC correction, Variance Stabilising Transformation) (2.1.1)
• Strand-Seq processing based on mm10 assembly (2.1.2)
• UCSC ready to use file generation including counts & SV calls (2.1.2)
• blacklist_regions parameter: (2.2.0)
• IGV ready to use XML session generation: (2.2.2)
• Pooled samples

Small issues to fix

• replace input_bam_location by data_location (harmonization with ashleys-qc-pipeline)
• List of commands available through list_commands parameter (1.8.6
• Move pysam / SM tag comparison script to snakemake rule (2.2.0)

🛑 Troubleshooting & Current limitations

• Do not change the structure of your input folder after running the pipeline, first execution will build a config dataframe file (OUTPUT_DIRECTORY/config/config.tsv) that contains the list of cells and the associated paths
• Do not change the list of chromosomes after a first execution (i.e: first execution on chr17, second execution on all chromosomes)

💂‍♂️ Authors (alphabetical order)

• Ashraf Hufash
• Cosenza Marco
• Ebert Peter
• Ghareghani Maryam
• Grimes Karen
• Gros Christina
• Höps Wolfram
• Jeong Hyobin
• Kinanen Venla
• Korbel Jan
• Marschall Tobias
• Meiers Sasha
• Porubsky David
• Rausch Tobias
• Sanders Ashley
• Van Vliet Alex
• Weber Thomas (maintainer and current developer)

📕 References

MosaiCatcher v2 publication: Weber Thomas, Marco Raffaele Cosenza, and Jan Korbel. 2023. ‘MosaiCatcher v2: A Single-Cell Structural Variations Detection and Analysis Reference Framework Based on Strand-Seq’. Bioinformatics 39 (11): btad633. https://doi.org/10.1093/bioinformatics/btad633.

Strand-seq publication: Falconer, E., Hills, M., Naumann, U. et al. DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution. Nat Methods 9, 1107–1112 (2012). https://doi.org/10.1038/nmeth.2206

scTRIP/MosaiCatcher original publication: Sanders, A.D., Meiers, S., Ghareghani, M. et al. Single-cell analysis of structural variations and complex rearrangements with tri-channel processing. Nat Biotechnol 38, 343–354 (2020). https://doi.org/10.1038/s41587-019-0366-x

ArbiGent publication: Porubsky, David, Wolfram Höps, Hufsah Ashraf, PingHsun Hsieh, Bernardo Rodriguez-Martin, Feyza Yilmaz, Jana Ebler, et al. 2022. “Recurrent Inversion Polymorphisms in Humans Associate with Genetic Instability and Genomic Disorders.” Cell 185 (11): 1986-2005.e26. https://doi.org/10.1016/j.cell.2022.04.017.

scNOVA publication: Jeong, Hyobin, Karen Grimes, Kerstin K. Rauwolf, Peter-Martin Bruch, Tobias Rausch, Patrick Hasenfeld, Eva Benito, et al. 2022. “Functional Analysis of Structural Variants in Single Cells Using Strand-Seq.” Nature Biotechnology, November, 1–13. https://doi.org/10.1038/s41587-022-01551-4.