Pipeline parameters

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.

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. If you set the strandedness value to auto the pipeline will sub-sample the input FastQ files to 1 million reads, use Salmon Quant to infer the strandedness automatically and then propagate this information to the remainder of the pipeline. If the strandedness has been inferred or provided incorrectly a warning will be present at the top of the MultiQC report so please be sure to check when looking at the QC for your samples.


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, reverse or auto.

An example samplesheet has been provided with the pipeline.

NB: The group and replicate columns were replaced with a single sample column as of v3.1 of the pipeline. The sample column is essentially a concatenation of the group and replicate columns, however it now also offers more flexibility in instances where replicate information is not required e.g. when sequencing clinical samples. If all values of sample have the same number of underscores, fields defined by these underscore-separated names may be used in the PCA plots produced by the pipeline, to regain the ability to represent different groupings.

Adapter trimming options

Trim Galore! is a wrapper tool around Cutadapt and FastQC to peform quality and adapter trimming on FastQ files. Trim Galore! will automatically detect and trim the appropriate adapter sequence. It is the default trimming tool used by this pipeline, however you can use fastp instead by specifying the --trimmer fastp parameter. fastp is a tool designed to provide fast, all-in-one preprocessing for FastQ files. It has been developed in C++ with multithreading support to achieve higher performance. You can specify additional options for Trim Galore! and fastp via the --extra_trimgalore_args and --extra_fastp_args parameters, respectively.

NB: TrimGalore! will only run using multiple cores if you are able to use more than > 5 and > 6 CPUs for single- and paired-end data, respectively. The total cores available to TrimGalore! will also be capped at 4 (7 and 8 CPUs in total for single- and paired-end data, respectively) because there is no longer a run-time benefit. See release notes and discussion whilst adding this logic to the nf-core/atacseq pipeline.

Alignment options

By default, the pipeline uses STAR (i.e. --aligner star_salmon) to map the raw FastQ reads to the reference genome, project the alignments onto the transcriptome and to perform the downstream BAM-level quantification with Salmon. STAR is fast but requires a lot of memory to run, typically around 38GB for the Human GRCh37 reference genome. Since the RSEM (i.e. --aligner star_rsem) workflow in the pipeline also uses STAR you should use the HISAT2 aligner (i.e. --aligner hisat2) if you have memory limitations.

You also have the option to pseudo-align and quantify your data with Salmon by providing the --pseudo_aligner salmon parameter. Salmon will then be run in addition to the standard alignment workflow defined by --aligner, mainly because it allows you to obtain QC metrics with respect to the genomic alignments. However, you can provide the --skip_alignment parameter if you would like to run Salmon in isolation. By default, the pipeline will use the genome fasta and gtf file to generate the transcripts fasta file, and then to build the Salmon index. You can override these parameters using the --transcript_fasta and --salmon_index parameters, respectively. The library preparation protocol (library type) used by Salmon quantification is inferred by the pipeline based on the information provided in the samplesheet, however, you can override it using the --salmon_quant_libtype parameter. You can find the available options in the Salmon documentation.

When running Salmon in mapping-based mode via --pseudo_aligner salmon the entire genome of the organism is used by default for the decoy-aware transcriptome when creating the indices (see second bulleted option in Salmon documentation).

Two additional parameters --extra_star_align_args and --extra_salmon_quant_args were added in v3.10 of the pipeline that allow you to append any custom parameters to the STAR align and Salmon quant commands, respectively. Note, the --seqBias and --gcBias are not provided to Salmon quant by default so you can provide these via --extra_salmon_quant_args '--seqBias --gcBias' if required.

Quantification options

The current options align with STAR and quantify using either Salmon (--aligner star_salmon) / RSEM (--aligner star_rsem). You also have the option to pseudo-align and quantify your data with Salmon by providing the --pseudo_aligner salmon parameter.

Since v3.0 of the pipeline, featureCounts is no longer used to perform gene/transcript quantification, however it is still used to generate QC metrics based on biotype information available within GFF/GTF genome annotation files. This decision was made primarily because of the limitations of featureCounts to appropriately quantify gene expression data. Please see Zhao et al., 2015 and Soneson et al., 2015.

For similar reasons, quantification will not be performed if using --aligner hisat2 due to the lack of an appropriate option to calculate accurate expression estimates from HISAT2 derived genomic alignments - this may change in future releases (see #822). HISAT2 has been made available for those who have a preference for the alignment, QC and other types of downstream analysis compatible with it’s output.

Unique Molecular Identifiers (UMI)

The pipeline supports Unique Molecular Identifiers to increase the accuracy of the quantification. UMIs are short sequences used to uniquely tag each molecule in a sample library and facilitate the accurate identification of read duplicates. They must be added during library preparation and prior to sequencing, therefore require appropriate arrangements with your sequencing provider.

To take UMIs into consideration during a workflow run, specify the --with_umi parameter. The pipeline currently supports UMIs, which are embedded within a read’s sequence and UMIs, whose sequence is given inside the read’s name. Please consult your kit’s manual and/or contact your sequencing provider regarding the exact specification.

The --umitools_grouping_method parameter affects how similar, but non-identical UMIs are treated. directional, the default setting, is most accurate, but computationally very demanding. Consider percentile or unique if processing many samples.


UMI typeSourcePipeline parameters
In read nameIllumina BCL convert >3.7.5--with_umi --skip_umi_extract --umitools_umi_separator ":"
In sequenceLexogen QuantSeq® 3’ mRNA-Seq V2 FWD + UMI Second Strand Synthesis Module--with_umi --umitools_extract_method "regex" --umitools_bc_pattern "^(?P<umi_1>.{6})(?P<discard_1>.{4}).*"
In sequenceLexogen CORALL® Total RNA-Seq V1
> mind Appendix H regarding optional trimming
--with_umi --umitools_extract_method "regex" --umitools_bc_pattern "^(?P<umi_1>.{12}).*"
Optional: --clip_r2 9 --three_prime_clip_r2 12
In sequenceTakara Bio SMARTer® Stranded Total RNA-Seq Kit v3--with_umi --umitools_extract_method "regex" --umitools_bc_pattern2 "^(?P<umi_1>.{8})(?P<discard_1>.{6}).*"

No warranty for the accuracy or completeness of the parameters is implied

3′ digital gene expression assays

Some bulk RNA-seq library preparation protocols capture only a 3’ tag from each transcript, e.g. 3’Pool-seq, DRUG-seq, BRB-seq or Lexogen’s commercial QuantSeq 3’ mRNA-seq FWD protocol. The following parameters have been validated for QuantSeq 3' mRNA-seq FWD data, and provide useful starting points for other 3’ RNA-seq protocols:

Custom STAR parameters

Lexogen provides an example analysis workflow on their website, which includes the ENCODE standard options for the STAR aligner. In addition, Lexogen also decreases the tolerance for mismatches and clips poly(A) tails. To apply these settings, add the following parameters when running the pipeline:

--extra_star_align_args "--alignIntronMax 1000000 --alignIntronMin 20 --alignMatesGapMax 1000000 --alignSJoverhangMin 8 --outFilterMismatchNmax 999 --outFilterMultimapNmax 20 --outFilterType BySJout --outFilterMismatchNoverLmax 0.1 --clip3pAdapterSeq AAAAAAAA"

Custom Salmon arguments

Salmon’s default quantitation algorithm takes into account transcript length. Because 3’ tag protocols do not capture full transcripts, this feature needs to be deactivated by specifying:

--extra_salmon_quant_args "--noLengthCorrection"

QuantSeq analysis with UMIs

If unique molecular identifiers were used to prepare the library, add the following arguments as well, to extract the UMIs and deduplicated alignments:

--umitools_extract_method regex
--umitools_bc_pattern "^(?P<umi_1>.{6})(?P<discard_1>.{4}).*"

Reference genome files

Please refer to the nf-core website for general usage docs and guidelines regarding reference genomes.

The minimum reference genome requirements for this pipeline 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. custom genomes that are unavailable on AWS iGenomes) so that you can save them somewhere locally. 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.

  • 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.
  • If --gene_bed is not provided then it will be generated from the GTF file.
  • If --additional_fasta is provided then the features in this file (e.g. ERCC spike-ins) will be automatically concatenated onto both the reference FASTA file as well as the GTF annotation before building the appropriate indices.

When using --aligner star_rsem, both the STAR and RSEM indices should be present in the path specified by --rsem_index (see #568).

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.

As of v3.7 of the pipeline, if you are using a genome downloaded from AWS iGenomes and using --aligner star_salmon (default) the version of STAR to use for the alignment will be auto-detected (see #808).

If you are using GENCODE reference genome files please specify the --gencode parameter because the format of these files is slightly different to ENSEMBL genome files:

  • The --gtf_group_features_type parameter will automatically be set to gene_type as opposed to gene_biotype, respectively.
  • If you are running Salmon, the --gencode flag will also be passed to the index building step to overcome parsing issues resulting from the transcript IDs in GENCODE fasta files being separated by vertical pipes (|) instead of spaces (see this issue).

Prokaryotic genome annotations

This pipeline uses featureCounts to generate QC metrics based on biotype information available within GFF/GTF genome annotation files. The format of these annotation files can vary significantly depending on the source of the annotation and the type of organism. The default settings in the pipeline are tailored towards Ensembl GTF annotations available for eukaryotic genomes. Prokaryotic genome annotations tend to be distributed in GFF format which are structured differently in terms of the feature naming conventions. There are a number of ways you can tune the behaviour of the pipeline to cater for differences/absence of biotype information:

  • Use --skip_biotype_qc to bypass this step altogether in case biotype information is of no interest or isn’t present in your annotation file.
  • Use --skip_rseqc since features like splice junctions, transcription start (TSS) and ending sites (TES) are less prevalent and therefore, less informative in prokaryotes compared to eukaryotes.
  • Use --featurecounts_feature_type transcript instead of --featurecounts_feature_type transcript exon (default) since entries for the latter may not contain a --featurecounts_group_type gene_biotype entry in the last column of the annotation. You should make sure that the value defined by --featurecounts_feature_type ideally contain corresponding entries for featurecounts_group_type.
  • Use --featurecounts_feature_type 'CDS' --featurecounts_group_type 'product' to identify the number of hypothetical proteins. However, the featureCounts QC will no longer reflect the biotype information from your RNA.

Please get in touch with us on the #rnaseq channel in the nf-core Slack workspace if you are having problems or need any advice.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/rnaseq --input samplesheet.csv --outdir <OUTDIR> --genome GRCh37 -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/rnaseq


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/rnaseq releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

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. For example, at the bottom of the MultiQC reports.

Core Nextflow arguments

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.

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 https://github.com/nf-core/configs 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, since it can lead to different results on different machines dependent on the computer enviroment.

  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters
  • 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.


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.

Custom configuration

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/rnaseq 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_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1)
Caused by:
    Process `NFCORE_RNASEQ:RNASEQ: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:
    .command.sh: 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 .command.run`

For beginners

A first step to bypass this error, you could try to increase the amount of CPUs, memory, and time for the whole pipeline. Therefor you can try to increase the resource for the parameters --max_cpus, --max_memory, and --max_time. Based on the error above, you have to increase the amount of memory. Therefore you can go to the parameter documentation of rnaseq and scroll down to the show hidden parameter button to get the default value for --max_memory. In this case 128GB, you than can try to run your pipeline again with --max_memory 200GB -resume to skip all process, that were already calculated. If you can not increase the resource of the complete pipeline, you can try to adapt the resource for a single process as mentioned below.

Advanced option on process level

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/rnaseq 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/star/align/main.nf. 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_RNASEQ:RNASEQ: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 (advanced users)

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 Quay.io

  3. Create the custom config accordingly:

    • For Docker:

      process {
          withName: PANGOLIN {
              container = 'quay.io/biocontainers/pangolin:3.0.5--pyhdfd78af_0'
    • For Singularity:

      process {
          withName: PANGOLIN {
              container = 'https://depot.galaxyproject.org/singularity/pangolin:3.0.5--pyhdfd78af_0'
    • 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.

Azure Resource Requests

To be used with the azurebatch profile by specifying the -profile azurebatch. We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.

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'