grothlab/crepas

Table of Contents

1. Samplesheet input
2. Reference genome files
3. Running the pipeline
   1. Parameters
   2. Updating the pipeline
   3. Reproducibility
   4. Core Nextflow arguments
   5. Custom configuration
   6. Running in the background
   7. Nextflow memory requirements

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use the --input parameter to specify its location:

--input <path_to_samplesheet_csv>

It has to be a comma-separated (.csv) file with with at least the sample, biological_replicate, exp_type and fastq_1 columns, and a header row as explained below. The columns can be provided in any order:

Example of a library with forward strandedness

The strandedness column in the input samplesheet records which read of a pair corresponds to the original nascent strand: forward means Read 1 aligns to the nascent forward strand and Read 2 to the nascent reverse strand, while reverse means Read1 aligns to the original nascent reverse strand and Read2 to the original nascent forward strand.

The diagram below illustrates the workflow from adapter ligation to read alignment for a library with forward strandedness:

Example samplesheet 1: Multiple biological replicates

This is an example of a samplesheet for a ChIP-seq experiment with one condition and two biological replicates for each antibody:

Example samplesheet 2: Multiple runs of the same library (technical replicates)

Both the sample and biological_replicate identifiers have to be the same when you have sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will perform the alignments in parallel, and subsequently merge them before further analysis.

This is an example of a samplesheet for a ChIP-seq experiment with one condition, two biological replicates for each antibody, and two technical replicates for each biological replicate:

Reference genome files

Genome index, FASTA, GTF/GFF and gene BED files

The minimum reference genome requirements are a FASTA and a 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. --bwa_index '/path/to/bwa/index/') or via a custom config file.

• If --gene_bed is not provided then it will be generated from the GTF file.

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.

Genome blacklist regions

The blacklist bed files where obtained using the commands below:

cd ..
mkdir -p v1.0
cd v1.0
wget -L https://www.encodeproject.org/files/ENCFF001TDO/@@download/ENCFF001TDO.bed.gz && gunzip ENCFF001TDO.bed.gz && mv ENCFF001TDO.bed hg19-blacklist.v1.bed

mkdir -p assets/blacklists/v2.0/
cd assets/blacklists/v2.0/
wget -L https://raw.githubusercontent.com/Boyle-Lab/Blacklist/master/lists/ce10-blacklist.v2.bed.gz && gunzip ce10-blacklist.v2.bed.gz
wget -L https://raw.githubusercontent.com/Boyle-Lab/Blacklist/master/lists/ce11-blacklist.v2.bed.gz && gunzip ce11-blacklist.v2.bed.gz
wget -L https://raw.githubusercontent.com/Boyle-Lab/Blacklist/master/lists/dm3-blacklist.v2.bed.gz && gunzip dm3-blacklist.v2.bed.gz
wget -L https://raw.githubusercontent.com/Boyle-Lab/Blacklist/master/lists/dm6-blacklist.v2.bed.gz && gunzip dm6-blacklist.v2.bed.gz
wget -L https://raw.githubusercontent.com/Boyle-Lab/Blacklist/master/lists/hg19-blacklist.v2.bed.gz && gunzip hg19-blacklist.v2.bed.gz
wget -L https://raw.githubusercontent.com/Boyle-Lab/Blacklist/master/lists/hg38-blacklist.v2.bed.gz && gunzip hg38-blacklist.v2.bed.gz
wget -L https://raw.githubusercontent.com/Boyle-Lab/Blacklist/master/lists/mm10-blacklist.v2.bed.gz && gunzip mm10-blacklist.v2.bed.gz

cd ..
mkdir -p v3.0
cd v3.0
wget -L https://www.encodeproject.org/files/ENCFF356LFX/@@download/ENCFF356LFX.bed.gz && gunzip ENCFF356LFX.bed.gz && mv ENCFF356LFX.bed hg38-blacklist.v3.bed

NB: A detailed description of the different versions of the files can be found here. Also, to to see which blacklist bed files are assigned by default to the respective reference genome check the igenomes.config.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run grothlab/crepas --input ./samplesheet.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 grothlab/crepas -profile docker -params-file params.yaml

with:

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

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

Parameters

Input/output options

Define where the pipeline should find input data and save output data.

Reference genome options

Reference genome related files and options required for the workflow.

UMI extraction and deduplication options

Options to adjust UMI extraction and deduplication criteria.

Adapter trimming options

Options to adjust adapter trimming criteria.

Hard trimming options

Options to adjust hard trimming criteria.

Alignment options

Options to adjust parameters and filtering criteria for read alignments.

Spike-in options

Options to adjust spike-in splitting criteria.

Multimapper allocation options

Options to adjust multimapper allocation criteria.

Alignment shifting options

Options to adjust alignment shifting criteria.

Filtering options

Options to adjust filtering criteria for BAM files.

Coverage and normalization options

Options to adjust coverage and normalization criteria.

Downsampling options

Options to adjust downsampling criteria.

TE counting options

Options to adjust transposable element (TE) counting criteria.

Peak calling options

Options to adjust peak calling criteria.

Process skipping options

Options to skip various steps within the workflow.

SCAR-seq (partition) and OK-seq (RFD) analysis options

Options to adjust SCAR-seq and OK-seq analysis criteria.

Repli-seq analysis options

Options to adjust Repli-seq replication-timing (RT) track analysis.

High-resolution Repli-seq analysis options

Options to adjust high-resolution (16-fraction) Repli-seq analysis.

Institutional config options

Parameters used to describe centralised config profiles. These should not be edited.

Generic options

Less common options for the pipeline, typically set in a config file.

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 grothlab/crepas

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 grothlab/crepas 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.

• With a release tag from the pipeline's repository:

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.

nextflow run grothlab/crepas \
  -r a6840348453bebe2cb49384f8522bbfb20d7087b

nextflow run grothlab/crepas \
  -r a684034

• With a git branch from the pipeline's repository:

  nextflow run grothlab/crepas \
    -r dev

The version number you use 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.

Sharing and reusing parameter files (-params-file)

To further assist in reproducibility, 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 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). For example, the nextflow head job can be submitted to SLURM by saving the following script:

#!/bin/bash

#SBATCH --job-name=crepas_job        # specify a name for the job
#SBATCH --mail-type=END,FAIL        # mail events (NONE, BEGIN, END, FAIL, ALL)
#SBATCH --mail-user=NONE            # email address to receive the notifications
#SBATCH -c 1                        # number of requested cores for the Nextflow head job
#SBATCH --mem=4gb                   # total requested RAM for the Nextflow head job
#SBATCH --time=2-00:00:00           # max. running time of the pipeline job, format in D-HH:MM:SS
#SBATCH --output=crepas_job.%j.log   # standard output and error log, '%j' gives the job ID

# Create an output directory for the pipeline run if it does not exist
mkdir -p <path_to_output_directory>
cd <path_to_output_directory>

# Run the pipeline
nextflow run grothlab/crepas \
    -r main \
    -profile test_scarseq \
    --outdir <path_to_output_directory>

And then, submitting the job to the queue with:

sbatch crepas_job.sh

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'

Examples

Setting the environment

Before analyzing any kind of data, we need to install or load Nextflow and a container runtime, as explained in the Quick start section of the README.

First, we create a directory structure for our project:

mkdir ${HOME}/projects/project1

Analyzing OK-seq data

Downloading the data

For this example, we will replicate the initiation zone and fork directionality analyses from the following article:

Petryk, N., Dalby, M., Wenger, A., Stromme, C. B., Strandsby, A., Andersson, R., & Groth, A. (2018). MCM2 promotes symmetric inheritance of modified histones during DNA replication. Science, 361(6409), 1389–1392. https://doi.org/10.1126/science.aau0294

First, we need to download the OK-seq data. We will do so using the nf-core/fetchngs pipeline, which works very well to fetch metadata and raw FastQ files from public databases, including the Gene Expression Omnibus (GEO).

We create a subdirectory for nf-core/fetchngs files:

mkdir -p ${HOME}/projects/project1/fetchngs/

Input IDs file

We then create a CSV file with the SRA IDs of the samples we want to download. In this case, we want the OK-seq data from the Petryk et al. 2018 paper, which is available under accession SRR7535256. Therefore, the file CSV should look like this:

SRR7535256

And we save it as project1.fetchngs.input.csv in the fetchngs subdirectory.

SBATCH script

Now, we will create an SBATCH script to submit the job to the cluster queue. You can use the following template.

#!/bin/bash

#SBATCH --job-name=FETCHNGS            # specify a name for the job
#SBATCH --mail-type=END,FAIL           # mail events (NONE, BEGIN, END, FAIL, ALL)
#SBATCH --mail-user=NONE               # email address to receive the notifications
#SBATCH -c 1                           # number of requested cores for the Nextflow head job
#SBATCH --mem=4gb                      # total requested RAM for the Nextflow head job
#SBATCH --time=2-00:00:00              # max. running time of the pipeline job, format in D-HH:MM:SS
#SBATCH --output=fetchngs_job.%j.log   # standard output and error log, '%j' gives the job ID

# Load the required modules
module load java/21.0.7 nextflow/25.10.4 singularity/3.8.7

# Create an output directory for the pipeline's output
mkdir -p ${HOME}/projects/project1/fetchngs/output/
cd ${HOME}/projects/project1/fetchngs/output/

# Run the pipeline
nextflow run nf-core/fetchngs \
    -r 1.11.0 \
    -profile ku_sund_danhead_mod \
    --input ${HOME}/projects/project1/fetchngs/project1.fetchngs.input.csv \
    --outdir ${HOME}/projects/project1/fetchngs/output/

Then place the script in your project directory, e.g., as project1.fetchngs.sbatch.sh.

Your folder structure should so far look something like this:

projects/
└── project1/
    └── fetchngs/
        ├── project1.fetchngs.input.csv
        └── project1.fetchngs.sbatch.sh

Submitting the job

We can then submit the job to the queue with:

cd ${HOME}/projects/project1/fetchngs/
sbatch project1.fetchngs.sbatch.sh

You can then monitor the job status using squeue or by looking at the fetchngs_job.%j.log file in the fetchngs directory. You can also check the .nextflow.log file in the fetchngs/output/ directory for more detailed logs.

When this job is finished, you should have the raw FastQ file SRX4403092_SRR7535256.fastq.gz downloaded in the fetchngs/output/fastq/ subdirectory.

Running crepas

Now that we have the data, we can run the crepas pipeline to analyze it. We will create another subdirectory for the crepas files:

mkdir -p ${HOME}/projects/project1/crepas/

Input samplesheet CSV

Then, we need to create a samplesheet CSV file for the crepas pipeline. This file should contain information about the samples we want to analyze. For this example, we will create a file called project1.crepas.samplesheet.csv in the crepas subdirectory with the following content:

sample,fastq_1,biological_replicate,exp_type,strandedness
SRR7535256,<PATH>/projects/project1/fetchngs/output/fastq/SRX4403092_SRR7535256.fastq.gz,1,OK-seq,forward

Parameter YAML file

Next, since the crepas pipeline has many more parameters, we will compile them into a parameter YAML file for easier management. The file should contain:

input: OK_seq_SRR7535256.crepas.samplesheet.csv
outdir: output_mm10_bowtie2
bowtie2_index: /reference_files/GRCm38/bowtie2_2.5.4_index
fasta: /reference_files/GRCm38/GRCm38.primary_assembly.genome.fa.gz
gtf: /reference_files/GRCm38/gencode.vM25.primary_assembly.annotation.gtf.gz
blacklist: /reference_files/GRCm38/GRCm38.ENCODE_ENCFF999QPV.Kundaje.unified_Excludable.bed.gz
aligner: bowtie2
genome: GRCm38
seq_platform: ILLUMINA
read_length: 75
fragment_size: 200
partition_fragment_size: 200
skip_macs3: true
multiqc_title: OK_seq_SRR7535256
trim_full_length: false
trim_minimum_length: 10
trim_maximum_length: 70
trim_nextseq: 0
trim_stringency: 1
trim_error_rate: 0.1
trim_q_cutoff: 20
skip_flT3: true
skip_flTbl: false
save_partition_intermeds: true
skip_plot_profile: true
skip_plot_fingerprint: true
skip_spp: true

Save this file as project1.crepas.params.yaml in the crepas/ subdirectory.

SBATCH script

Now, we will create an SBATCH script to submit the crepas job to the cluster queue. You can use the following template.

Submitting the job

Analyzing ChIP-seq data

For this example, we will analyze a subset of the ChIP-seq data from the following article:

Flury, V., Reverón-Gómez, N., Alcaraz, N., Stewart-Morgan, K. R., Wenger, A., Klose, R. J., & Groth, A. (2023). Recycling of modified H2A-H2B provides short-term memory of chromatin states. Cell, 186(5), 1050-1065.e19. https://doi.org/10.1016/j.cell.2023.01.007

Downloading the data

Now we need to download the ChIP-seq data. We will do so using the nf-core/fetchngs pipeline, which works very well to fetch metadata and raw FastQ files from public databases.

Running crepas

Relevant parameters

WIP

Analyzing ChOR-seq data

WIP

Analyzing SCAR-seq data

WIP

Analyzing ATAC-seq data

WIP

Analyzing ChIP-exo data

WIP

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