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.
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.

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.

Direct download of public repository data

NB: This is an experimental feature but should work beautifully when it does! :)

The pipeline has been set-up to automatically download and process the raw FastQ files from public repositories. Identifiers can be provided in a file, one-per-line via the --public_data_ids parameter. Currently, the following identifiers are supported:


If SRR/ERR run ids are provided then these will be resolved back to their appropriate SRX/ERX ids to be able to merge multiple runs from the same experiment. This is conceptually the same as merging multiple libraries sequenced from the same sample.

The final sample information for all identifiers is obtained from the ENA which provides direct download links for FastQ files as well as their associated md5 sums. If download links exist, the files will be downloaded in parallel by FTP otherwise they will NOT be downloaded. This is intentional because the tools such as parallel-fastq-dump, fasterq-dump, prefetch etc require pre-existing configuration files in the users home directory which makes automation tricky across different platforms and containerisation.

As a bonus, the pipeline will also generate a valid samplesheet with paths to the downloaded data that can be used with the --input parameter, however, it is highly recommended that you double-check that all of the identifiers you defined using --public_data_ids are represented in the samplesheet. Also, public databases don’t reliably hold information such as strandedness information so you may need to amend these entries too. All of the sample metadata obtained from the ENA has been appended as additional columns to help you manually curate the samplesheet before you run the pipeline.

If you have a GEO accession (found in the data availability section of published papers) you can directly download a text file containing the appropriate SRA ids to pass to the pipeline:

  • Search for your GEO accession on GEO
  • Click SRA Run Selector at the bottom of the GEO accession page
  • Select the desired samples in the SRA Run Selector and then download the Accession List

This downloads a text file called SRR_Acc_List.txt which can be directly provided to the pipeline e.g. --public_data_ids SRR_Acc_List.txt.

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.

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. 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.

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).

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/rnaseq --input samplesheet.csv --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
results         # Finished results (configurable, see below)
.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’s 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 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.

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. 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 used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously.

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 `RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1)
Error executing process > 'RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)'
Caused by:
    Process `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: 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/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/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 {
    withName: STAR_ALIGN {
        memory = 100.GB

NB: We specify just the process name i.e. STAR_ALIGN in the config file and not the full task name string that is printed to screen in the error message or on the terminal whilst the pipeline is running i.e. RNASEQ:ALIGN_STAR:STAR_ALIGN. You may get a warning suggesting that the process selector isn’t recognised but you can ignore that if the process name has been specified correctly. This is something that needs to be fixed upstream in core Nextflow.

Tool-specific options

For the ultimate flexibility, we have implemented and are using Nextflow DSL2 modules in a way where it is possible for both developers and users to change tool-specific command-line arguments (e.g. providing an additional command-line argument to the STAR_ALIGN process) as well as publishing options (e.g. saving files produced by the STAR_ALIGN process that aren’t saved by default by the pipeline). In the majority of instances, as a user you won’t have to change the default options set by the pipeline developer(s), however, there may be edge cases where creating a simple custom config file can improve the behaviour of the pipeline if for example it is failing due to a weird error that requires setting a tool-specific parameter to deal with smaller / larger genomes.

The command-line arguments passed to STAR in the STAR_ALIGN module are a combination of:

  • Mandatory arguments or those that need to be evaluated within the scope of the module, as supplied in the script section of the module file.

  • An options.args string of non-mandatory parameters that is set to be empty by default in the module but can be overwritten when including the module in the sub-workflow / workflow context via the addParams Nextflow option.

The nf-core/rnaseq pipeline has a sub-workflow (see terminology) specifically to align reads with STAR and to sort, index and generate some basic stats on the resulting BAM files using SAMtools. At the top of this file we import the STAR_ALIGN module via the Nextflow include keyword and by default the options passed to the module via the addParams option are set as an empty Groovy map here; this in turn means options.args will be set to empty by default in the module file too. This is an intentional design choice and allows us to implement well-written sub-workflows composed of a chain of tools that by default run with the bare minimum parameter set for any given tool in order to make it much easier to share across pipelines and to provide the flexibility for users and developers to customise any non-mandatory arguments.

When including the sub-workflow above in the main pipeline workflow we use the same include statement, however, we now have the ability to overwrite options for each of the tools in the sub-workflow including the align_options variable that will be used specifically to overwrite the optional arguments passed to the STAR_ALIGN module. In this case, the options to be provided to STAR_ALIGN have been assigned sensible defaults by the developer(s) in the pipeline’s modules.config and can be accessed and customised in the workflow context too before eventually passing them to the sub-workflow as a Groovy map called star_align_options. These options will then be propagated from workflow -> sub-workflow -> module.

As mentioned at the beginning of this section it may also be necessary for users to overwrite the options passed to modules to be able to customise specific aspects of the way in which a particular tool is executed by the pipeline. Given that all of the default module options are stored in the pipeline’s modules.config as a params variable it is also possible to overwrite any of these options via a custom config file.

Say for example we want to append an additional, non-mandatory parameter (i.e. --outFilterMismatchNmax 16) to the arguments passed to the STAR_ALIGN module. Firstly, we need to copy across the default args specified in the modules.config and create a custom config file that is a composite of the default args as well as the additional options you would like to provide. This is very important because Nextflow will overwrite the default value of args that you provide via the custom config.

As you will see in the example below, we have:

  • appended --outFilterMismatchNmax 16 to the default args used by the module.
  • changed the default publish_dir value to where the files will eventually be published in the main results directory.
  • appended 'bam':'' to the default value of publish_files so that the BAM files generated by the process will also be saved in the top-level results directory for the module. Note: 'out':'log' means any file/directory ending in out will now be saved in a separate directory called my_star_directory/log/.
params {
    modules {
    'star_align' {
        args          = "--quantMode TranscriptomeSAM --twopassMode Basic --outSAMtype BAM Unsorted --readFilesCommand zcat --runRNGseed 0 --outFilterMultimapNmax 20 --alignSJDBoverhangMin 1 --outSAMattributes NH HI AS NM MD --quantTranscriptomeBan Singleend --outFilterMismatchNmax 16"
        publish_dir   = "my_star_directory"
        publish_files = ['out':'log', 'tab':'log', 'bam':'']


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'