The pipeline input can be specified in one of two ways:
--submission_basepath path/to/submission(to use a submission folder directly)--input samplesheet.csv(to use a manually-prepared samplesheet)
Instead of parsing the submission metadata, a CSV samplesheet containing the required information for the pipeline can be provided. The samplesheet provides a bit more flexibility at the cost of manual preparation. For example, starting directly from aligned reads is only possible using a samplesheet.
When running the pipeline from unaligned reads each row in the sample sheet is a "run". Each run consists of either a pair of FASTQ files for paired-end data or a single FASTQ file for single-end data. A "sample" is equivalent to a "lab datum" in the official GRZ submission metadata schema and can contain multiple runs.
Runs from the same sample must contain the same sample ID in the sample column to tie them together.
The runId must be unique to a run within a sample to differentiate them.
Runs in different samples may therefore have the same runId.
In short, each row in the samplesheet must have a unique combination of sample and runId.
Below is an example for the same sample sequenced with 3 runs:
sample,runId,libraryType,sequenceSubtype,genomicStudySubtype,reads1,reads2
CONTROL_REP1,run1,wgs,somatic,tumor+germline,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
CONTROL_REP1,run2,wgs,somatic,tumor+germline,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
CONTROL_REP1,run3,wgs,somatic,tumor+germline,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz
A more complete example samplesheet is available here.
nextflow run main.nf \
-profile conda \
--outdir "path/to/outdir" \
--input "samplesheet.csv" \
--genome "GRCh37" # or "GRCh38"You may optionally provide meanDepthOfCoverage, targetedRegionsAboveMinCoverage, percentBasesAboveQualityThreshold if you would like the pipeline to automatically report if the metrics it calculates deviate more than 5% from the provided metrics.
This is how the GRZs will run the pipeline to determine QC issues.
If you are using the pipeline to obtain your metrics first, then you can omit these columns and no deviation will be calculated.
| Column | Required | Description |
|---|---|---|
sample |
yes | Unique sample identifier. Must be identical for multiple sequencing runs from the same sample and not contain spaces. |
runId |
for unaligned input | Uniquely identifies a run within a sample. Must not contain spaces. |
donorPseudonym |
no | Unique identifier of the donor. |
laneId |
no | Lane ID of run. |
flowcellId |
no | Flowcell ID of run. |
labDataName |
no | Sample name or description. |
libraryType |
yes | panel, wgs, wes, panel_lr, wgs_lr, or wes_lr |
sequenceSubtype |
yes | somatic or germline |
sequencerManufacturer |
no | Sequencing platform manufacturer (e.g. Illumina). |
genomicStudySubtype |
yes | tumor+germline, tumor-only, or germline-only |
reads1 |
for unaligned short read input | Full path to FASTQ file for Illumina short reads pair 1 (R1). Must be gzipped and have the extension ".fastq.gz" or ".fq.gz". |
reads2 |
for unaligned short read input | Full path to FASTQ file for Illumina short reads pair 2 (R2). Must be gzipped and have the extension ".fastq.gz" or ".fq.gz". |
reads_long |
for unaligned long read input | Full path to FASTQ or BAM (PacBio) file for long reads. Must have the extension ".fastq.gz", ".fq.gz", or ".bam". |
aligned_reads |
for aligned input | Full path to aligned reads (BAM file). Can be used as an alternative to FASTQ reads. See starting from aligned reads section below for more information. |
bed_file |
for WES and panel | Target region BED for WES and panels with the extension ".bed.gz" or ".bed". Empty for WGS. |
fastp_json |
no | Corresponding FASTP JSON report for aligned_reads. |
meanDepthOfCoverage |
no | Mean depth of coverage across target regions (panel/WES) or genome-wide (WGS). (also see details) |
targetedRegionsAboveMinCoverage |
no | Fraction (between 0 and 1) of target regions with coverage above the minimum required for the library type. (also see details) |
percentBasesAboveQualityThreshold |
no | Percentage (between 0 and 100) of untrimmed read bases that are above the minimum quality threshold for the library type. (also see details) |
You can use re-use cached reference files with nextflow run main.nf --reference_path "your/reference/path".
The pipeline will automatically create the files in the correct structure if they don't exist.
While not recommended, you may also manually populate this folder according to the directory structure below. Please make sure the reference FASTA is in BGZIP format, not just GZIP.
$ tree your/reference/path
your/reference/path
├── GRCh37
│ ├── bwamem2
│ │ ├── GRCh37.0123
│ │ ├── GRCh37.amb
│ │ ├── GRCh37.ann
│ │ ├── GRCh37.bwt.2bit.64
│ │ └── GRCh37.pac
│ ├── GRCh37.fasta.gz
│ ├── GRCh37.fasta.gz.fai
│ ├── GRCh37.fasta.gz.gzi
│ └── GRCh37.fasta.mmi
└── GRCh38
├── bwamem2
│ ├── GRCh38.0123
│ ├── GRCh38.amb
│ ├── GRCh38.ann
│ ├── GRCh38.bwt.2bit.64
│ └── GRCh38.pac
├── GRCh38.fasta.gz
├── GRCh38.fasta.gz.fai
├── GRCh38.fasta.gz.gzi
└── GRCh38.fasta.mmiA final alternative is providing --fasta directly, or preparing a config file like this:
fasta = "your/path/to/reference/GRCh37/genome.fa"Please note that reference files already in --reference_path will always be chosen over the path provided through --fasta.
All of the parameters below default to undefined/null.
| Parameters | Description |
|---|---|
reference_path |
reference cache path |
fasta |
genome FASTA path. Only used when reference path is not set or it is missing in the reference path |
When running the pipeline directly from aligned reads each row in the samplesheet is a sample, instead of a run. This assumes that you have merged the alignments from each run already into a single alignment file. Since raw input files are missing, the user must provide a fastp JSON report. As this will correspond to the alignment file in case of multiple lanes, please take the average. Below is an example for merged fastp JSON reports. Only "before_filtering" statistics are important for the quality control thresholds of the workflow.
{
"summary": {
"before_filtering": {
"total_reads": 1094,
"total_bases": 110494,
"q20_bases": 94654,
"q30_bases": 83570,
"q20_rate": 0.856644,
"q30_rate": 0.756331
}
}
}sample,aligned_reads,fastp_json
CONTROL_REP1,AEG588A1_S1001.bam,AEG588A1_S1_L002.fastp.json
CONTROL_REP2,AEG588A1_S2001.bam,AEG588A1_S2_L002.fastp.json
Find a complete example samplesheet here.
Note:
- running with
--submission_basepathis not possible when using alignments as input. - when using a manually-prepared samplesheet, you need to either provide
--genome, or include a column namedreferencein your samplesheet, with all entries set to either GRCh37 or GRCh38. - If you provide your own BAM files, the reference sequence names must follow the UCSC style (e.g. chr1 instead of 1).
The typical command for running the pipeline is as follows:
nextflow run main.nf --input ./samplesheet.csv --outdir ./results --genome GRCh37 -profile dockerThis 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.If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.
Pipeline settings can be provided in a yaml or json file via -params-file <file>.
:::warning
Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).
:::
The above pipeline run specified with a params file in yaml format:
nextflow run BfArM-MVH/GRZ_QC_Workflow -profile docker -params-file params.yamlwith:
input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
<...>You can also generate such YAML/JSON files via nf-core/launch.
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 BfArM-MVH/GRZ_QC_WorkflowIt 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 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.
To further assist in reproducbility, you can use share and re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.
:::tip If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles. :::
:::note 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, Apptainer, Conda) - see below.
:::info We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported. :::
If you want to use multiple config profiles for various institutional clusters, they are available at nf-core/configs. For more information and to see if your system is available in these configs please see the nf-core/configs documentation and download manually to your conf directory.
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
apptainer- A generic configuration profile to be used with Apptainer
wave- A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow
24.03.0-edgeor later).
- A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow
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, Charliecloud, or Apptainer.
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 documentation for more information.
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.
To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.
In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default this pipeline uses containers and software from the biocontainers or bioconda projects. However in some cases the pipeline specified version maybe out of date.
To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.
A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.
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.
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).
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'