Introduction

nf-core/drop(Detection of RNA Outliers Pipeline) is a bioinformatics pipeline that detects aberrant expression, aberrant splicing, and mono-allelic expression from RNA sequencing data.

• aberrant expression
  1. Compute read count matrix (GenomicAlignments)
  2. Detect expression outliers (OUTRIDER)
• aberrant splicing
  1. Count split reads and non-split reads (GenomicAlignments) and (Subread)
  2. Detect aberrant splicing events (FRASER)
• mono-allelic expression
  1. Compute allelic counts (GATK ASEReadCounter)
  2. Detect aberrant mono-allelically expressed genes (DESeq2)
• Present QC Reports (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.

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

samplesheet.tsv:

RNA_ID	RNA_BAM_FILE	RNA_BAI_FILE	DROP_GROUP	STRAND	DNA_ID	DNA_VCF_FILE	DNA_TBI_FILE	GENOME
HG00103	path/to/HG00103.bam	path/to/HG00103.bam.bai	group1,group2	no	HG00103	path/to/demo_chr21.vcf.gz	path/to/demo_chr21.vcf.gz.tbi	ucsc
HG00106	path/to/HG00106.bam	path/to/HG00106.bam.bai	group1,group2	no	HG00106	path/to/demo_chr21.vcf.gz	path/to/demo_chr21.vcf.gz.tbi	ucsc

Each row requires a unique RNA_ID, a BAM file, DROP_GROUP and STRAND. For MAE additional DNA_ID, DNA_VCF_FILE and GENOME.

Here is an example of a samplesheet. Of note, to detect outliers confidently, a sufficiently large sample size is needed (>30 samples).

Now, you can run the pipeline using:

nextflow run nf-core/drop \
   -profile <docker/singularity/conda/...> \
   --input samplesheet.tsv \
   --outdir <OUTDIR> \
   --genome hg19 \
   --gene_annotation <path/to/gene/annotation/yaml> \
   --ae_run true \
   --as_run true \
   --mae_run true \
   --ucsc_fasta <path/to/fasta>

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. Here is an example of a custom config.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/drop was originally written by Vicente Yepez, Christian Mertes, Michaela Mueller, Daniela Andrade, Leonhard Wachutka from the Gagneur lab at the Department of Informatics and School of Medicine of the Technical University of Munich (TUM) and The German Human Genome-Phenome Archive (GHGA).

The Nextflow DSL2 conversion of the pipeline was lead by Nicolas Vannieuwkerke and Yun Wang.

Main developers:

• Nicolas Vannieuwkerke
• Yun Wang

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

• Ata Jadid Ahari
• Drew Behrens

Contributions and Support

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

For further information or help, don't hesitate to get in touch on the Slack #drop channel (you can join with this invite).

Citations

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

You can cite the nf-core publication as follows:

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.