Introduction

crepas is a bioinformatics pipeline for comprehensive analysis of bulk chromatin sequencing data. It supports multiple experimental techniques, including ChIP-seq, ChOR-seq, ChIP-exo, SCAR-seq, OK-seq, ATAC-seq, CUT&RUN, CUT&Tag and TIP-seq:

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

Pipeline summary

1. Raw read quality control (FastQC)

2. UMI extraction (UMI-tools or umi-transfer)

3. Adapter, quality or hard trimming (Trim Galore!)

4. Alignment to reference genome (BWA MEM, BWA MEM2, Chromap, Bowtie, Bowtie2, STAR, HISAT2, minimap2 or strobealign)

5. Merging of alignments from multiple libraries of the same sample (technical replicates) (picard)

6. Estimation of library complexity (Preseq)

7. UMI-based deduplication (UMI-tools or UMICollapse) or marking of duplicates (picard)

8. BAM filtering (SAMBAMBA) to remove:

   • unmapped or improperly paired reads
   • reads marked as duplicates

9. Splitting BAM by genome (into endogenous and exogenous/spike-in alignments) (SAMBAMBA)

10. Multimapping read allocation (Chromap, MMR or Allo)

11. Shifting of aligned reads (only for ATAC-seq) (deepTools)

12. BAM filtering (SAMBAMBA) to remove:

    • unmapped or improperly paired reads
    • reads with low mapping quality (depending on the aligner used)
    • secondary alignments

13. BAM filtering to remove:

    • reads mapped within blacklisted regions (SAMBAMBA)
    • orphan reads (singletons) (bampe_rm_orphan.py)

14. Collection of alignment quality control metrics (picard)

15. Calculation of strand cross-correlation, peak and ChIP-seq quality measures including NSC and RSC (phantompeakqualtools)

16. BAM downsampling considering sample type (IP or input control) and genome (endogenous or spike-in) (picard)

17. Creation of coverage tracks (with a specified bin size) and applying multiple normalization methods accounting for spike-in (deepTools)

18. Generating gene-body meta-profile from coverage files (deepTools)

19. Counting of reads mapping on transposable elements instances (TElocal) and subfamilies (TEcount)

20. Calculating genome-wide IP enrichment relative to input control (deepTools)

21. ChIP-seq, ChOR-seq and ATAC-seq downstream analyses

• Call megabase domains of enrichment (EDD), annotate (HOMER), and create consensus peakset (BEDTools)

• Call broad/narrow peaks (MACS3), annotate (HOMER), and create consensus peakset (BEDTools)

• Call peaks (Genrich), annotate (HOMER), and create consensus peakset (BEDTools)

• Call diffuse peaks (epic2), annotate (HOMER), and create consensus peakset (BEDTools)

• Extract genome-wide uncertainty-moderated signal from multi-sample datasets (Consenrich), call consensus peaks (ROCCO) and annotate (HOMER)

• Count reads in consensus peaks (featureCounts)

• PCA and clustering (R, DESeq2)

22. SCAR-seq and OK-seq downstream analyses

• Splitting BAM files by forward and reverse strands.

• Computing BEDGRAPH summaries of feature coverage per strand (BEDTools)

• Creating BigWig files from BEDGRAPH files (bedGraphToBigWig)

• Computing the average coverage per window (bigWigAverageOverBed)

• Calculating partition score or replication fork directionality (RFD) (partition_or_rfd_smooth.pl)

• Generating partition files for samples, stranded inputs and input-corrected samples.

• Plotting partition files and scatter-correlation plots against OK-seq if provided (partition_or_rfd_plot.R)

23. ChIP-exo downstream analyses

• Call peaks (MACE), annotate (HOMER), and create consensus peakset (BEDTools)

24. CUT&RUN, CUT&Tag and TIP-seq downstream analyses

• Call peaks (SEACR), annotate (HOMER), and create consensus peakset (BEDTools)

25. Create IGV session file containing coverage tracks and peaks for data visualisation (IGV).

26. Present QC and stats for raw reads, alignments and peak-calling (MultiQC, R)

Quick start

1. Install Nextflow (>=24.10.0)

2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run grothlab/crepas \
     -profile test,<your_profile> \
     --outdir <path_to_output_directory>

   Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (<your_profile> in the example command above). You can chain multiple config profiles in a comma-separated string.

   • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.

4. Start running your own analysis!

   nextflow run grothlab/crepas \
     -profile <your_profile> \
     --input <path_to_your_input_samplesheet_file> \
     --outdir <path_to_output_directory> \
     --genome GRCh38 \
     --fasta <path_to_your_genome_fasta_file>

Usage

Important

See the usage docs for an overview of how the pipeline works, how to run it and a description of all of the different command-line flags and parameters.

Note

See the usage guide for DAN System users for instructions on how to run the pipeline on the DAN System.

Output

Important

See the output docs for an overview of the different results produced by the pipeline and how to interpret them.

Credits

The crepas pipeline was written by Samuel Ruiz-Pérez (@samuelruizperez) at the Groth Lab (@grothlab), Center for Epigenetic Cell Memory (EpiC), Danish Cancer Institute.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch through the pipeline's GitHub Discussions or directly with Samuel Ruiz-Pérez (samper@cancer.dk)

Citations

If you use crepas for your analysis, please cite it as below:

Ruiz-Pérez, S., Du, Q., Biran, A., Groth, A., & Alcaraz, N. (2026). CREPAS: a reproducible nascent chromatin sequencing analysis pipeline for epigenome replication studies. https://doi.org/10.5281/zenodo.20788108

This pipeline uses code developed and maintained by the nf-core initative, and reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

It also uses adapted scripts from nf-core/chipseq.

nf-core/chipseq: nf-core/chipseq v2.1.0 - Platinum Willow Sparrow

Harshil Patel, Jose Espinosa-Carrasco, Chuan Wang, Phil Ewels, nf-core bot, Tiago Chedraoui Silva, Alexander Peltzer, Björn Langer, Sarah Guinchard, Maxime U. Garcia, Drew Behrens, Matthias Hörtenhuber, Adam Talbot, Konrad Rokicki, Robert Syme, Rotholandus, Samuel Ruiz Pérez, Sofia Haglund, Steffen Möller, Warren Winfried “Winni” Kretzschmar, and Kevin Menden.

Zenodo. 2024. doi: 10.5281/zenodo.13899404.

In addition, an extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.