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ICGC-FeatureCounts Usage

General Nextflow info

Nextflow handles job submissions on SLURM or other environments, and supervises running the jobs. Thus the Nextflow process must run until the pipeline is finished. We recommend that you put the process running in the background through screen / tmux or similar tool. Alternatively you can run nextflow within a cluster job submitted your job scheduler.

It is recommended to limit the Nextflow Java virtual machines memory. We recommend adding the following line to your environment (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/ICGC-FeatureCounts --manifest 'your-input-manifest' --accesstoken 'your-access-token-for-icgc-data' --gtf 'path-to-gtf-file' -profile standard,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 ICGC-FeatureCounts

Reproducibility

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 ICGC-FeatureCounts 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.

Main Arguments

-profile

Use this parameter to choose a configuration profile. Each profile is designed for a different compute environment - follow the links below to see instructions for running on that system. Available profiles are:

  • docker
    • A generic configuration profile to be used with Docker
    • Runs using the local executor and pulls software from dockerhub: icgc-featurecounts
  • awsbatch
    • A configuration to utilize the pipeline on AWSBatch with Docker
    • Please specify params.awsqueue, params.awsregion, params.workDir and params.outdir accordingly
  • standard
    • The default profile, used if -profile is not specified at all. Runs locally and expects all software to be installed and available on the PATH.
    • This profile is mainly designed to be used as a starting point for other configurations and is inherited by most of the other profiles.
  • none
    • No configuration at all. Useful if you want to build your own config from scratch and want to avoid loading in the default base config profile (not recommended).

--manifest

Use this to specify the location of your ICGC AWS Manifest file. It should be a tsv file containing the columns object_id and file_name For example:

--manifest 'path/to/data/manifest.tsv'

Please note the following requirements:

  1. The path must be enclosed in quotes

--gtf

The pipeline requires a GTF file as input for your data analysis. You may use iGenomes S3 URLs for an appropriate GTF file For example:

--gtf 's3://ngi-igenomes/igenomes/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.gtf'

--accesstoken

The pipeline requires you to specify your ICGC AWS Accesstoken to be specified. This token is used to generate programmatically the required pre-authenticated AWS S3 URLs for the requested samples. For example:

--accesstoken 'abccaa7-ab30437a-49230a'

AWS Batch specific configuration parameters

Note that you need to specify at least the JobQueue as the AWS Region is already prefilled with a default value. Furthermore, make sure to define params.workDir and params.outdir for storing temporary files and your results on S3.

--awsregion

Specify the required AWS Region to run AWSBatch on. Default is us-east-1 (as ICGC data is solely available there for now), but in future this might help running on different AWS regions.

--awsregion 'us-east-1'

--awsqueue

Specify the required AWSBatch JobQueue you created when setting up the pipeline here.

--awsqueue 'MyFancyProjectJobQueue'

Job Resources

Automatic resubmission

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 an error code of 143 (exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped.

Custom resource requests

Wherever process-specific requirements are set in the pipeline, the default value can be changed by creating a custom config file. See the files in conf for examples.

Other command line parameters

--outdir

The output directory where the results will be saved.

-resume

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.

NB: Single hyphen (core Nextflow option)

-c

Specify the path to a specific config file (this is a core NextFlow command).

NB: Single hyphen (core Nextflow option)

Note - you can use this to override defaults. For example, you can specify a config file using -c that contains the following:

process.$multiqc.module = []

--max_memory

Use to set a top-limit for the default memory requirement for each process. Should be a string in the format integer-unit. eg. `--max_memory '8.GB'``

--max_time

Use to set a top-limit for the default time requirement for each process. Should be a string in the format integer-unit. eg. --max_time '2.h'

--max_cpus

Use to set a top-limit for the default CPU requirement for each process. Should be a string in the format integer-unit. eg. --max_cpus 1

### --multiqc_config If you would like to supply a custom config file to MultiQC, you can specify a path with --multiqc_config. This is used instead of the config file specific to the pipeline.