Introduction

mskcc/arrakis is a bioinformatics pipeline that performs reallignment and Base Quality Score Recalibration on bams.

1. Reallignment (ABRA)
2. Base Quality Score Recalibration (GATK_BQSR)
3. PrintReads (GATK_PrintReads)
4. Generate Metrics (PICARD_CollectMultipleMetrics)

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.

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

samplesheet.csv:

pairId,tumorBam,normalBam,assay,normalType,bedFile
foo_sample,foo_tumor.rg.md.bam,foo_normal.rg.md.bam,IMPACT505,MATCHED,foo_tumor.foo_normal.fci.bed
bar_sample,bar_tumor.rg.md.bam,bar_normal.rg.md.bam,IMPACT505,MATCHED,bar_tumor.bar_normal.fci.bed

Now, you can run the pipeline using:

nextflow run mskcc/arrakis \
   -profile <docker/singularity/.../institute>,test_juno \
   --input samplesheet.csv \
   --outdir <OUTDIR>

Note

You must include test_juno as your profile for the workflow to run properly

The test_juno parameter file does not need to be modified if you are using grch_37 and do no want to modify the known_sites used by BQSR.

Modifying the config

• If you would like to add more known sites you can modify the config file here
• If you would like to use a diffrent genome you need to first add your genome files here and then add your genome key here

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.

Output structure

The output of the pipeline will be organized by the pairId provided. For the example above the output will look like:

├── bar_sample
│   ├── bar_normal.rg.md.abra.bai
│   ├── bar_normal.rg.md.abra.bam
│   ├── bar_normal.rg.md.abra.bam.bai
│   ├── bar_normal.rg.md.abra.printreads.bai
│   ├── bar_normal.rg.md.abra.printreads.bam
│   ├── bar_normal.rg.md.abra.printreads.qmetrics.quality_by_cycle.pdf
│   ├── bar_normal.rg.md.abra.printreads.qmetrics.quality_by_cycle_metrics
│   ├── bar_sample.recal.matrix
│   ├── bar_tumor.rg.md.abra.bai
│   ├── bar_tumor.rg.md.abra.bam
│   ├── bar_tumor.rg.md.abra.bam.bai
│   ├── bar_tumor.rg.md.abra.printreads.bai
│   ├── bar_tumor.rg.md.abra.printreads.bam
│   ├── bar_tumor.rg.md.abra.printreads.qmetrics.quality_by_cycle.pdf
│   ├── bar_tumor.rg.md.abra.printreads.qmetrics.quality_by_cycle_metrics
│   └── versions.yml
├── foo_sample
│   ├── foo_normal.rg.md.abra.bai
│   ├── foo_normal.rg.md.abra.bam
│   ├── foo_normal.rg.md.abra.bam.bai
│   ├── foo_normal.rg.md.abra.printreads.bai
│   ├── foo_normal.rg.md.abra.printreads.bam
│   ├── foo_normal.rg.md.abra.printreads.qmetrics.quality_by_cycle.pdf
│   ├── foo_normal.rg.md.abra.printreads.qmetrics.quality_by_cycle_metrics
│   ├── foo_sample.recal.matrix
│   ├── foo_tumor.rg.md.abra.bai
│   ├── foo_tumor.rg.md.abra.bam
│   ├── foo_tumor.rg.md.abra.bam.bai
│   ├── foo_tumor.rg.md.abra.printreads.bai
│   ├── foo_tumor.rg.md.abra.printreads.bam
│   ├── foo_tumor.rg.md.abra.printreads.qmetrics.quality_by_cycle.pdf
│   ├── foo_tumor.rg.md.abra.printreads.qmetrics.quality_by_cycle_metrics
│   └── versions.yml
└── pipeline_info
    ├── execution_report_2024-03-08_12-44-35.html
    ├── execution_timeline_2024-03-08_12-44-35.html
    ├── execution_trace_2024-03-08_12-44-35.txt
    ├── nf_core_pipeline_software_mqc_versions.yml
    ├── params_2024-03-08_12-44-37.json
    └── pipeline_dag_2024-03-08_12-44-35.html

Credits

mskcc/arrakis was originally written by Nikhil Kumar (@nikhil).

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

• Lisle E Mose, Charles M Perou, Joel S Parker, Improved indel detection in DNA and RNA via realignment with ABRA2, Bioinformatics, Volume 35, Issue 17, September 2019, Pages 2966–2973, https://doi.org/10.1093/bioinformatics/btz033
• “Picard Toolkit.” 2019. Broad Institute, GitHub Repository. https://broadinstitute.github.io/picard/; Broad Institute
• Van der Auwera, G. A., Carneiro, M. O., Hartl, C., Poplin, R., Del Angel, G., Levy-Moonshine, A., Jordan, T., Shakir, K., Roazen, D., Thibault, J., Banks, E., Garimella, K. V., Altshuler, D., Gabriel, S., & DePristo, M. A. (2013). From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Current protocols in bioinformatics, 43(1110), 11.10.1–11.10.33. https://doi.org/10.1002/0471250953.bi1110s43

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.