The typical command for running the pipeline is as follows:

nextflow run nf-core/rnavar --input samplesheet.csv --genome GRCh38 -profile docker

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you’re running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/rnavar


It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you’ll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/rnavar releases page and find the latest version number - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1.

This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future.

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 4 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Multiple runs of the same sample

The sample identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:


Full samplesheet

The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 4 columns to match those defined in the table below.

A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 6 samples, where TREATMENT_REP3 has been sequenced twice.

sampleCustom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (_).
fastq_1Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
fastq_2Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
strandednessSample strand-specificity. Must be one of unstranded, forward or reverse.

An example samplesheet has been provided with the pipeline.


Reference genome files

The minimum reference genome requirements are a FASTA and GTF file, all other files required to run the pipeline can be generated from these files. However, it is more storage and compute friendly if you are able to re-use reference genome files as efficiently as possible. It is recommended to use the --save_reference parameter if you are using the pipeline to build new indices (e.g. those unavailable on AWS iGenomes) so that you can save them somewhere locally.

NB: Compressed reference files are also supported by the pipeline i.e. standard files with the .gz extension and indices folders with the tar.gz extension.

The index building step can be quite a time-consuming process and it permits their reuse for future runs of the pipeline to save disk space. You can then either provide the appropriate reference genome files on the command-line via the appropriate parameters (e.g. --star_index '/path/to/STAR/index/') or via a custom config file.

NB: If you are supplying a pre-built genome index file via --star_index, please ensure that the index has been generated with the latest STAR version i.e. v2.7.9a or above. In case if the pipeline found an incompatible index, it will generate a new one using the reference genome which will consume time and memory unnecessarily.

  • If --genome is provided then the FASTA and GTF files (and existing indices) will be automatically obtained from AWS-iGenomes unless these have already been downloaded locally in the path specified by --igenomes_base.
  • If --gff is provided as input then this will be converted to a GTF file, or the latter will be used if both are provided.
  • The --exon_bed parameter file is expected to be exon coordinates with at least three columns i.e., <exon_position_start> <exon_position_end> in the file. The <exon_postion_start> should be 0-based. If this parameter is not provided, the exon coordinates are extracted from the GTF file and generates a bed file by the process GTF2BED.
  • If --star_index is not provided then it will be generated from the reference genome FASTA file using STAR --runmode genomeGenerate command.

NB: In case if you are providing a GTF and/or a BED file, please ensure that the chromosomes and contigs in the files are also present in the genome FASTA (and in the .dict) file. Otherwise GATK BedToIntervalList module is likely to fail if the chromosomes/contigs do not match with the reference genome data.

Recommendation when using very large genomes

When the pipeline is used on very large genomes having chromosome size greater than 512Mb (e.g. Chromosome 1 of Monodelphis domestica has a size of 748055161bp), please make sure that --bam_csi_index parameter is provided in order to use coordinate sorted index (CSI) instead of standard binary alignment index (BAI).

NB: When --bam_csi_index is used, variant filtration step will be disabled as GATK VariantFiltration does not currently support CSI index for the input VCF. It may be incorporated in the future when newer GATK versions support CSI for VCF inputs.

Alignment options

The pipeline uses STAR to map the raw FastQ reads to the reference genome. STAR is fast but requires a lot of memory to run, typically around 38GB for the Human GRCh37 reference genome.

By default, STAR runs in 2-pass mode. For the most sensitive novel junction discovery, it is recommend running STAR in the 2-pass mode. It does not increase the number of detected novel junctions, but allows to detect more splices reads mapping to novel junctions. The basic idea is to run 1st pass of STAR mapping with the usual parameters, then collect the junctions detected in the first pass, and use them as ”annotated” junctions for the 2nd pass mapping. You can turn off this feature by setting --star_twopass false in command line.

Read length is an important parameter therefore it has to be used carefully. The default is set to 150, but it has to be changed according to the input reads. For example, if the input read length is 2x151bp, then you use --read_length 151. The --read_length parameter is used while generating an index as well as in the alignment process. In both processes, the pipeline use (read_length - 1) to the STAR parameter --sjdbOverhang as recommended in STAR documentation.

NB: Read length --read_length is an important parameter, therefore it has to set according to the input read length. If you are supplying a pre-built genome index, please make sure that you have used the same (read_length -1) during the genomeGenerate step.

STAR alignment generates a coordinated-sorted BAM file as output. The coordinate-sorting process can be very memory intensive when the input data is deep sequenced or the genome has many highly expressed loci. When the pipeline runs on memory constrained environment, sorting step may fail due to low memory. In such cases you may adjust the limit parameters such as --star_limitBAMsortRAM, --star_outBAMsortingBinsN and --star_limitOutSJcollapsed to increase the sorting memory and genomic bins. Refer the parameter documentation for the default values and adjust as appropriate based on your memory availability.

Preprocessing options

Marking duplicate reads is performed using GATK4 MarkDuplicates tool. The tool does not remove duplicate reads by default, however you can set --remove_duplicates true to remove them.

GATK best practices has been followed in this pipeline for RNA analysis, hence it uses GATK modules such as SplitNCigarReads, BaseRecalibrator, ApplyBQSR. The BaseRecalibrator process requires known variants sites VCF. ExAc, gnomAD, or dbSNP resources can be used as known sites of variation.You can supply the VCF and index files using parameters such as --dbsnp, --dbsnp_tbi, --known_indels, --known_indels_tbi.

NB: Base recalibration can be turned off using --skip_baserecalibration true option. This is useful when you are analyzing data from non-model organisms where there is no known variant datasets exist.

GATK SplitNCigarReads is very time consuming step, therefore we made an attempt to break the GTF file into multiple chunks (scatters) using GATK IntervalListTools to run the process independently on each chunk in a parallel way to speed up the analysis. The default number of splits is set to 25, that means the GTF file is split into 25 smaller files and run GATK SplitNCigarReads on each of them in parallel. You can modify the number of splits using parameter --gatk_interval_scatter_count.

Variant calling and filtering

GATK HaplotypeCaller is used for variant calling with default minimum phred-scaled confidence threshold as 20. This value can be changed using paramerter --gatk_hc_call_conf.

The pipeline runs a hard-filtering step on the variants by default. It does not filter out any variants, rather it flags i.e. PASS or other flags such as FS, QD, SnpCluster, etc. in FILTER column of the VCF. The following are the default filter criteria, however it can be changed using the respective parameters.

  • --gatk_vf_cluster_size is set to 3. It is the number of SNPs which make up a cluster.
  • --gatk_vf_window_size is set to 35. The window size (in bases) in which to evaluate clustered SNPs.
  • --gatk_vf_fs_filter is set to 30.0. Filter based on FisherStrand > 30.0. It is the Phred-scaled probability that there is strand bias at the site.
  • --gatk_vf_qd_filter is set to 2.0 meaning filter variants if Quality By Depth filter is < 2.0.

Variant filtering is an optional step. You can skip it using --skip_variantfiltration parameter.

Variant annotation

The annotation of variants is performed using snpEff and VEP. The parameter to use is --annotate_tools snpeff or --annotate_tools vep. You can even run both snpEff and VEP using --annotate_tools merge, in this case the output VCF file will have both snpEff and VEP annotations combined.

You can skip the variant annotation step using --skip_variantannotation parameter or without passing --annotate_tools options.

Annotation cache

Both snpEff and VEP enable usage of cache. If cache is available on the machine where rnavar is run, it is possible to run annotation using cache. You need to specify the cache directory using --snpeff_cache and --vep_cache in the command lines or within configuration files. The cache will only be used when --annotation_cache and cache directories are specified (either in command lines or in a configuration file).


nextflow run nf-core/rnavar --input samplesheet.csv --genome GRCh38 -profile docker --annoate_tools snpEff --snpeff_cache </path/to/snpEff/cache> --annotation_cache
nextflow run nf-core/rnavar --input samplesheet.csv --genome GRCh38 -profile docker --annotate_tools VEP --vep_cache </path/to/VEP/cache> --annotation_cache

Download annotation cache

A Nextflow helper script link has been designed to help downloading snpEff and VEP caches. Such files are meant to be shared between multiple users, so this script is mainly meant for people administrating servers, clusters and advanced users.

nextflow run --snpeff_cache </path/to/snpEff/cache> --snpeff_db <snpEff DB version> --genome <GENOME>
nextflow run --vep_cache </path/to/VEP/cache> --species <species> --vep_cache_version <VEP cache version> --genome <GENOME>

Using VEP CADD plugin

To enable the use of the VEP CADD plugin:

  • Download the CADD files
  • Specify them (either on the command line, like in the example or in a configuration file)
  • use the --cadd_cache flag


nextflow run nf-core/rnavar --input samplesheet.csv --genome GRCh38 -profile docker --annotate_tools VEP VEP --cadd_cache \
    --cadd_indels </path/to/CADD/cache/InDels.tsv.gz> \
    --cadd_indels_tbi </path/to/CADD/cache/InDels.tsv.gz.tbi> \
    --cadd_wg_snvs </path/to/CADD/cache/whole_genome_SNVs.tsv.gz> \
    --cadd_wg_snvs_tbi </path/to/CADD/cache/whole_genome_SNVs.tsv.gz.tbi>

Downloading CADD files

An helper script has been designed to help downloading CADD files. Such files are meant to be share between multiple users, so this script is mainly meant for people administrating servers, clusters and advanced users.

nextflow run --cadd_cache </path/to/CADD/cache> --cadd_version <CADD version> --genome <GENOME>


NB: These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).


Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Conda) - see below. When using Biocontainers, most of these software packaging methods pull Docker containers from e.g FastQC except for Singularity which directly downloads Singularity images via https hosted by the Galaxy project and Conda which downloads and installs software locally from Bioconda.

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended.

  • docker
    • A generic configuration profile to be used with Docker
  • singularity
    • A generic configuration profile to be used with Singularity
  • podman
    • A generic configuration profile to be used with Podman
  • shifter
    • A generic configuration profile to be used with Shifter
  • charliecloud
    • A generic configuration profile to be used with Charliecloud
  • conda
    • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter or Charliecloud.
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters


Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.


Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.


Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

For example, if the nf-core/rnavar pipeline is failing after multiple re-submissions of the STAR_ALIGN process due to an exit code of 137 this would indicate that there is an out of memory issue:

[62/149eb0] NOTE: Process `NFCORE_RNAVAR:RNAVAR:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1)
Caused by:
    Process `NFCORE_RNAVAR:RNAVAR:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137)
Command executed:
    STAR \
        --genomeDir star \
        --readFilesIn WT_REP1_trimmed.fq.gz  \
        --runThreadN 2 \
        --outFileNamePrefix WT_REP1. \
Command exit status:
Command output:
Command error: line 9:  30 Killed    STAR --genomeDir star --readFilesIn WT_REP1_trimmed.fq.gz --runThreadN 2 --outFileNamePrefix WT_REP1. <TRUNCATED>
Work dir:
Tip: you can replicate the issue by changing to the process work dir and entering the command `bash`

To bypass this error you would need to find exactly which resources are set by the STAR_ALIGN process. The quickest way is to search for process STAR_ALIGN in the nf-core/rnavar Github repo. We have standardised the structure of Nextflow DSL2 pipelines such that all module files will be present in the modules/ directory and so based on the search results the file we want is modules/nf-core/software/star/align/ If you click on the link to that file you will notice that there is a label directive at the top of the module that is set to label process_high. The Nextflow label directive allows us to organise workflow processes in separate groups which can be referenced in a configuration file to select and configure subset of processes having similar computing requirements. The default values for the process_high label are set in the pipeline’s base.config which in this case is defined as 72GB. Providing you haven’t set any other standard nf-core parameters to cap the maximum resources used by the pipeline then we can try and bypass the STAR_ALIGN process failure by creating a custom config file that sets at least 72GB of memory, in this case increased to 100GB. The custom config below can then be provided to the pipeline via the -c parameter as highlighted in previous sections.

process {
        memory = 100.GB

NB: We specify the full process name i.e. NFCORE_RNAVAR:RNAVAR:ALIGN_STAR:STAR_ALIGN in the config file because this takes priority over the short name (STAR_ALIGN) and allows existing configuration using the full process name to be correctly overridden.

If you get a warning suggesting that the process selector isn’t recognised check that the process name has been specified correctly.

Updating containers

The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. If for some reason you need to use a different version of a particular tool with the pipeline then you just need to identify the process name and override the Nextflow container definition for that process using the withName declaration. For example, in the nf-core/viralrecon pipeline a tool called Pangolin has been used during the COVID-19 pandemic to assign lineages to SARS-CoV-2 genome sequenced samples. Given that the lineage assignments change quite frequently it doesn’t make sense to re-release the nf-core/viralrecon everytime a new version of Pangolin has been released. However, you can override the default container used by the pipeline by creating a custom config file and passing it as a command-line argument via -c custom.config.

  1. Check the default version used by the pipeline in the module file for Pangolin

  2. Find the latest version of the Biocontainer available on

  3. Create the custom config accordingly:

    • For Docker:

      process {
          withName: PANGOLIN {
              container = ''
    • For Singularity:

      process {
          withName: PANGOLIN {
              container = ''
    • For Conda:

      process {
          withName: PANGOLIN {
              conda = 'bioconda::pangolin=3.0.5'

NB: If you wish to periodically update individual tool-specific results (e.g. Pangolin) generated by the pipeline then you must ensure to keep the work/ directory otherwise the -resume ability of the pipeline will be compromised and it will restart from scratch.


In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'