nf-core/tfactivity

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Introduction

The following inputs can be processed by the pipeline:

• Chromatin accessibility data (at least one of the following)
  • Peaks in BED3-compatible format (e.g. broadPeak), parameter --input
  • BAM files, parameter --input_bam
• Gene expression data (all of the following)
  • Raw count matrix (e.g. from nf-core/rnaseq), parameter --counts
  • Design matrix assigning conditions and optionally batch information to the samples provided in the count matrix, parameter --counts_design

The conditions in the peak/BAM samplesheets need to match the conditions in the design matrix.

Peaks samplesheet

The samplesheet for peak files can look as follows:

sample,condition,assay,peak_file
condition1_H3K27ac_1,condition1,H3K27ac,condition1_H3K27ac_1.broadPeak
condition1_H3K27ac_2,condition1,H3K27ac,condition1_H3K27ac_2.broadPeak
condition1_H3K4me3,condition1,H3K4me3,condition1_H3K4me3.broadPeak
condition2_H3K27ac,condition2,H3K27ac,condition2_H3K27ac.broadPeak
condition3_H3K27ac,condition3,H3K27ac,condition3_H3K27ac.broadPeak
condition3_H3K4me3,condition3,H3K4me3,condition3_H3K4me3.broadPeak

There are some optional columns which can be added to the samplesheet to configure the footprinting:

sample,condition,assay,peak_file,footprinting,include_original,max_peak_gap
condition1_H3K27ac_1,condition1,H3K27ac,condition1_H3K27ac_1.broadPeak,true,true,500
condition1_H3K27ac_2,condition1,H3K27ac,condition1_H3K27ac_2.broadPeak,true,true,500
condition1_H3K4me3,condition1,H3K4me3,condition1_H3K4me3.broadPeak,true,true,500
condition2_H3K27ac,condition2,H3K27ac,condition2_H3K27ac.broadPeak,true,true,500
condition3_H3K27ac,condition3,H3K27ac,condition3_H3K27ac.broadPeak,true,true,500
condition3_H3K4me3,condition3,H3K4me3,condition3_H3K4me3.broadPeak,true,true,500
condition1_ATAC-seq,condition1,ATAC-seq,condition1_ATAC-seq.broadPeak,false,,

• footprinting: Whether to perform footprinting analysis on the peaks. If enabled, the regions between close peaks will be scanned for transcription factor affinity. This is recommended for Histone modification ChIP-seq data, but not for ATAC-Seq and DNase-Seq data. Default: true
• include_original: Whether to include the original peaks in the footprinting analysis. Default: true
• max_peak_gap: Maximum number of base pairs between two peaks to be considered as a single region for footprinting analysis. Default: 500

BAM samplesheet

The samplesheet for BAM files can look as follows:

sample,condition,assay,signal,control
condition1_H3K27ac_1,condition1,H3K27ac,condition1_H3K27ac_1.bam,condition1_control.bam
condition1_H3K27ac_2,condition1,H3K27ac,condition1_H3K27ac_2.bam,condition1_control.bam
condition1_H3K4me3,condition1,H3K4me3,condition1_H3K4me3.bam,condition1_control.bam
condition2_H3K27ac,condition2,H3K27ac,condition2_H3K27ac.bam,condition2_control.bam
condition3_H3K27ac,condition3,H3K27ac,condition3_H3K27ac.bam,condition3_control.bam
condition3_H3K4me3,condition3,H3K4me3,condition3_H3K4me3.bam,condition3_control.bam

The first three columns are the same as in the peak file samplesheet. The signal column should contain the path to the signal BAM file. The control column should contain the path to the control BAM file.

These files are used to predict enhancer regions in the following way:

• Train chromHMM on the signal and control BAM files
• Identify states that are enriched for either H3K27ac or H3K4me3
• Extract the regions of these states
• Merge close regions to enhancer regions using the ROSE algorithm

The resulting enhancer regions are then used as if they were peak files provided in the peak samplesheet. However, footprinting analysis is not performed on these regions.

Gene expression data

Gene expression data can be provided in two ways. In both ways, it should be raw counts per gene ID across samples.

1. A single count matrix with gene IDs as rows and samples as columns. This matrix should be provided with the --counts parameter. The --counts_design parameter is used to match samples in the count matrix to conditions (and optionally batches).
2. A gene list file and one count file per sample. In this case, provide the gene list file with the --counts parameter and use the counts_file column in --counts_design to specify the count files. The files will be merged into a single count matrix (as in the first option) before further processing.

Single count matrix

The count matrix (--counts) should look like this:

gene_id,sample1,sample2,sample3
ENSG00000000001,10,20,30
ENSG00000000002,5,10,15
ENSG00000000003,2,4,6

The design matrix (--counts_design) should look like this:

sample,condition
sample1,condition1
sample2,condition1
sample3,condition2

Gene list and multiple count files

The gene list file (--counts) should look like this:

ENSG00000000001
ENSG00000000002
ENSG00000000003

The design matrix (--counts_design) should look like this:

sample,condition,counts_file
sample1,condition1,sample1_counts.txt
sample2,condition1,sample2_counts.txt
sample3,condition2,sample3_counts.txt

In this case, the count files should look like this:

10
20
30

Batch effect correction

Optionally, you can specify a column batch in the design matrix to correct for batch effects. The batch effect correction is performed using DESeq2. This is possible for both the single count matrix and the gene list with multiple count files.

sample,condition,batch
sample1,condition1,batch1
sample2,condition1,batch2
sample3,condition2,batch2

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/tfactivity --input ./samplesheet.csv --counts ./count_matrix.csv --counts_design ./counts_design.csv --outdir ./results --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.

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>.

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/tfactivity -profile docker -params-file params.yaml

with params.yaml containing:

input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

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/tfactivity

Reproducibility

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/tfactivity 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.

Core Nextflow arguments

-profile

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.

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
• 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-edge or later).
• 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.

-resume

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.

-c

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.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

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 nf-core pipelines use 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.

Custom Tool Arguments

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.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

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'