Usage

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 7 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Grouping and Replicates

The sample_id identifiers must be identical for all data files that belong to the same biological sample. The pipeline groups all files sharing the same sample_id and processes them together to build a unified segmentation model for that sample.

If you have multiple files for the exact same histone mark within a single sample, the pipeline treats them as biological or technical replicates. You must assign each of these files a unique integer in the replicate column.

NOTE: Replicates for methylation data (.bed or .bed.gz files) are not currently supported by the pipeline. Methylation data should only have one entry per sample_id.

Below is an example of a samplesheet where a sample has two replicates for the H3K4me3 mark, alongside single entries for another histone mark and methylation data:

sample_id,replicate,epigenetic_mark,file_name,modality,paired_end,distribution
SAMPLE_A,1,H3K4me3,./data/rep1_H3K4me3.bam,ChIP-seq,true,NBI
SAMPLE_A,2,H3K4me3,./data/rep2_H3K4me3.bam,ChIP-seq,true,NBI
SAMPLE_A,1,H3K27ac,./data/rep1_H3K27ac.bam,ChIP-seq,true,NBI
SAMPLE_A,1,WGBS,./data/sampleA_methyl.bed.gz,WGBS,true,BI

Full samplesheet

The pipeline uses a 7-column structured format to process and group epigenetic data.
File types are inferred automatically:

• Histone -> .bam, .bam.gz
• Methylation -> .bed, .bed.gz

The input incase of Methylation (.bed | .bed.gz) must be a BED9+ file with chromosome (col 1), start position (col 2), strand (col 6), coverage (col 10), and percent methylation (col 11).

A complete samplesheet file consisting of multiple samples, including replicates for specific histone marks and paired methylation data, may look like the one below. This example shows two samples (CONTROL and TREATMENT), where CONTROL has two replicates for the H3K4me3 mark.

sample_id,replicate,epigenetic_mark,file_name,modality,paired_end,distribution
CONTROL,1,H3K4me3,./data/control_rep1_H3K4me3.bam,ChIP-seq,true,NBI
CONTROL,2,H3K4me3,./data/control_rep2_H3K4me3.bam,ChIP-seq,true,NBI
CONTROL,1,H3K27ac,./data/control_H3K27ac.bam,ChIP-seq,true,NBI
CONTROL,1,WGBS,./data/control_methyl.bed.gz,WGBS,true,BI
TREATMENT,1,H3K4me3,./data/treatment_H3K4me3.bam,ChIP-seq,true,NBI
TREATMENT,1,H3K27ac,./data/treatment_H3K27ac.bam,ChIP-seq,true,NBI
TREATMENT,1,WGBS,./data/treatment_methyl.bed.gz,WGBS,true,BI

Supported Distributions

An example samplesheet has been provided with the pipeline.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/epigenomesegmentation --input ./samplesheet.csv --outdir ./results --genome hg38 -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>.

Segmentation

The pipeline routes inputs by file extension: BAM files are used for histone counts, while BED/BED.GZ files are used for methylation or coverage-marker counts. When both modalities are supplied, the default workflow combines them for segmentation; histone-only samples are segmented using histone data. For methylation/coverage-only segmentation, use --dna.

Pipeline modes

By default, the pipeline runs topology modeling (LDM). --dna, --fitting, --jointrain, and --counts change the workflow path; use only compatible options together. --duration selects the model type and can be combined with compatible workflow options such as --jointrain. --methcounts and --histonecounts are inputs for precomputed counts, not modes.

Count generation only

Use --counts to run the BAM/BED count-generation steps and stop before model training and segmentation:

nextflow run nf-core/epigenomesegmentation \
  --input samplesheet.csv \
  --outdir results \
  --genome hg38 \
  --counts \
  -profile docker

For BAM inputs, the pipeline generates histone count matrices. For BED/BED.GZ inputs, it generates binned methylation/coverage count files. Outputs are published under Counts/<sample>_Histone/ and Counts/<sample>_Methylation/, respectively. This mode uses raw BAM/BED inputs; use --histonecounts and/or --methcounts to supply precomputed counts for a segmentation run instead. --counts does not generate segmentations or model reports.

Methylation or CoverageMarker Mode

--dna

With this flag, BAM processing is skipped and the pipeline performs methylation/coverage-only segmentation from BED inputs. The workflow generates and bins the BED-based counts before training and decoding the model.

Duration Mode

--duration

Selects the duration-modeling (DM) HMM, which models segment duration. Without this flag, the default segmentation workflow uses topology modeling (LDM).

Fitting Mode

--fitting

This mode fits candidate distributions to the count data rather than running the usual segmentation workflow. Specify the comma-separated candidates with --distributions, for example --distributions 'NBI,SI,BNB'. The workflow uses the fitting results to produce an updated samplesheet with the best-fitting distribution.

Jointrain Mode

--jointrain

This opt-in mode trains a shared model from the combined counts of the input samples for each requested state, then decodes each sample separately. It can make state labels comparable across samples; each sample still receives its own segmentation. It is disabled by default.

Note: --jointrain can be combined with --duration. Do not combine it with --dna, --fitting, or --counts; those select different workflow paths.

Using precomputed counts

--methcounts <path to count matrix>  --histonecounts <path to count matrix>

Use these options when count matrices have already been generated. Supply --histonecounts for histone counts and --methcounts for methylation/coverage counts; supplying both enables a combined run. The pipeline still uses --input to obtain sample and assay metadata and to determine how inputs are grouped. For methylation-only segmentation, also set --dna.

Important limitation: Each option is currently resolved as one file path (or a glob whose first match is used) and paired with metadata from the samplesheet. The workflow does not currently document or guarantee automatic per-sample matching of multiple count files. Confirm the expected count-file layout and sample association before relying on custom count inputs for multiple samples.

Exploring Multiple States

--states 8,10,12

The --states parameter defines the number of chromatin states for the segmentation model. You can provide a single integer or a comma-separated list (e.g., 8,10,12) to run multiple state configurations. The pipeline creates a model configuration for each requested state.

Parameter Estimation Chromosome

--chr_parameter_estimation 12

The --chr_parameter_estimation parameter defines which chromosome should be used for the initial parameter estimation step before full model training. By default, it uses chromosome 12. You can provide an integer (e.g., 12, 22), or a string identifier if you are using specific custom reference genomes or pilot data (e.g., pilot_hg38).

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

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

with:

input: './samplesheet.csv'
outdir: './results/'
genome: 'hg38'
<...>

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/epigenomesegmentation

Reproducibility

It is a good idea to specify the 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/epigenomesegmentation 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.

To further assist in reproducibility, you can use share and reuse 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 check if your system is supported, 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 environment.

• 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 pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (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 customise process resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps 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.

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'