svtorm

SVtorm: Structural Variant for Target Panels Optimized by Recalling & Merging.

https://github.com/jblancoheredia/svtorm

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Repository

SVtorm: Structural Variant for Target Panels Optimized by Recalling & Merging.

Basic Info
  • Host: GitHub
  • Owner: jblancoheredia
  • License: mit
  • Language: Nextflow
  • Default Branch: main
  • Size: 829 KB
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Created 9 months ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Contributing License Citation

README.md

mskcc/cti/svtorm

Nextflow run with conda run with docker run with singularity Launch on Seqera Platform

Introduction

Metro

CTI/SVtorm is a high-performance bioinformatics pipeline tailored to detect Structural Variants from Targeted sequencing data Optimized by Recall and Merge. Concived for ACCESSv2 can be used for different panels. SVtorm delivers sensitive and reliable SV calls from liquid biopsy samples, by integrating and harmonizing multiple cutting-edge callers, SVtorm generates clinically actionable results to support oncologists and inform precision treatment decisions, while also advancing translational research.

Pipeline Steps

  1. SVtorm starts with a couple of BAM files per sample, one for tumour and one for normal.
  2. Collect stats for the input BAM files (Picard)
  3. Calling SVs
  4. Merging Calls (SURVIVOR)
  5. ReCalling (Gridss)
  6. Filtering Calls (SURVIVOR)
  7. SVs Stats (SURVIVOR)
  8. Annotate SVs (iAnnotateSV)
  9. Draw SVs (DrawSV)
  10. Check for expected SVs in Controls (SeraCareCheckUp)
  11. Present QC for raw reads (MultiQC)

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data. [!NOTE] The best practice is to create a dedicated conda environment to install nf-core and Nextflow.

To run SVtorm follow these steps:

First, prepare the structure of the project, the ideal structure would be like follows:

PROJECT/ ├── 01_data/ │ ├── samples.csv │ ├── SAMPLE1_TUMOUR.bam │ ├── SAMPLE1_TUMOUR.bai │ ├── SAMPLE1_NORMAL.bam │ ├── SAMPLE1_NORMAL.bai │ ├── SAMPLE2_TUMOUR.bam │ └── SAMPLE2_TUMOUR.bai ├── 02_code/ │ └── run_SVtorm.sh ├── 03_outs/ ├── 04_logs/ ├── 05_work/ └── 06_cach/

Note: Any other structure is also possible, just adjust the launching script accordingly.

Second, prepare a samplesheet with your input data that looks as follows:

samples.csv:

csv patient,sample,bam,bai,tumour,matched PATIENT1,SAMPLE1,/path/to/normal/bam/file/SAMPLE1_NORMAL.bam,/path/to/normal/bam/file/SAMPLE1_NORMAL.bai,false,true PATIENT1,SAMPLE1,/path/to/tumour/bam/file/SAMPLE1_TUMOUR.bam,/path/to/tumour/bam/file/SAMPLE1_TUMOUR.bai,true,true PATIENT2,SAMPLE2,/path/to/tumour/bam/file/SAMPLE2_TUMOUR.bam,/path/to/tumour/bam/file/SAMPLE2_TUMOUR.bai,true,false Each row corresponds to a sample BAM file and its associated index (BAI). The matched column indicates whether a matched normal is available (true/false), and the tumour column designates whether the sample is a tumour. If no normal is provided, a default putative normal will be automatically used to support somatic variant calling.

Third, now you can run the pipeline using the assets/run_SVtorm.sh script as a template, such script is:

```bash

!/bin/bash

source activate

export NXFLOGFILE="../04logs/nextflow.log" export NXFCACHEDIR="../06cach/nextflow-cache"

nextflow run \ /path/to/SVtorm/main.nf \ --input ../01data/samples.csv \ --outdir ../03outs/ \ --email @mskcc.org \ -profile \ -work-dir ../05work \ --seqlibrary Av2 \ --genome HG19VS \ -resume ```

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

Credits

SVtorm was originally written by Juan Blanco-Heredia at the Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Technology Innovation Lab, Memorial Sloan Kettering Cancer Center.

Main developer:

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

Owner

  • Login: jblancoheredia
  • Kind: user

Citation (CITATIONS.md)

# jblancoheredia/svtorm: Citations

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

- [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

> Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].

- [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)

> Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.

## Software packaging/containerisation tools

- [Anaconda](https://anaconda.com)

  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)

  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

- [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

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