*nf-core/rnavar* is a bioinformatics pipeline for RNA variant calling analysis following GATK4 best practices.

Pipeline summary

  1. Merge re-sequenced FastQ files (cat)
  2. Read QC (FastQC)
  3. Align reads to reference genome (STAR)
  4. Sort and index alignments (SAMtools)
  5. Duplicate read marking (GATK4 MarkDuplicates)
  6. Splits reads that contain Ns in their cigar string (GATK4 SplitNCigarReads)
  7. Estimate and correct systematic bias using base quality score recalibration (GATK4 BaseRecalibrator, GATK4 ApplyBQSR)
  8. Convert a BED file to a Picard Interval List (GATK4 BedToIntervalList)
  9. Scatter one interval-list into many interval-files (GATK4 IntervalListTools)
  10. Call SNPs and indels (GATK4 HaplotypeCaller)
  11. Merge multiple VCF files into one VCF (GATK4 MergeVCFs)
  12. Index the VCF (Tabix)
  13. Filter variant calls based on certain criteria (GATK4 VariantFiltration)
  14. Annotate variants (snpEff, Ensembl VEP)
  15. Present QC for raw read, alignment, gene biotype, sample similarity, and strand-specificity checks (MultiQC, R)

Summary of tools and version used in the pipeline

| Tool | Version |
| ----------- | ------- |
| FastQC | 0.11.9 |
| STAR | 2.7.9a |
| Samtools | 1.15.1 |
| GATK | |
| Tabix | 1.11 |
| SnpEff | 5.0 |
| Ensembl VEP | 104.3 |
| MultiQC | 1.12 |


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.

<!-- TODO nf-core: Describe the minimum required steps to execute the pipeline, e.g. how to prepare samplesheets. Explain what rows and columns represent. For instance (please edit as appropriate): First, prepare a samplesheet with your input data that looks as follows: `samplesheet.csv`: ```csv sample,fastq_1,fastq_2 CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz ``` Each row represents a fastq file (single-end) or a pair of fastq files (paired end). -->

Now, you can run the pipeline using:

nextflow run nf-core/rnavar -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input samplesheet.csv  --outdir <OUTDIR> --genome GRCh38  

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.

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.


nf-core/rnavar was originally written in Nextflow DSL2 for use at the Barntumörbanken, Karolinska Institutet, by Praveen Raj (@praveenraj2018) and Maxime U Garcia (@maxulysse).

The pipeline is primarily maintained by Praveen Raj (@praveenraj2018) from Barntumörbanken, Karolinska Institutet and Maxime U Garcia (@maxulysse) from Seqera Labs

Many thanks to other who have helped out along the way too, including (but not limited to):

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 #rnavar channel (you can join with this invite).


If you use nf-core/rnavar for your analysis, please cite it using the following doi: 10.5281/zenodo.6669637

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