Chipseq

<h1> <picture> <source media="(prefers-color-scheme: dark)" srcset="docs/images/nf-core-chipseq_logo_dark.png"> <img alt="nf-core/chipseq" src="docs/images/nf-core-chipseq_logo_light.png"> </picture> </h1> [](https://github.com/nf-core/chipseq/actions?query=workflow%3A%22nf-core+CI%22) [](https://github.com/nf-core/chipseq/actions?query=workflow%3A%22nf-core+linting%22)[](https://nf-co.re/chipseq/results)[](https://doi.org/10.5281/zenodo.3240506)

Introduction

nfcore/chipseq is a bioinformatics analysis pipeline used for Chromatin ImmunoPrecipitation sequencing (ChIP-seq) data.

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. The dataset consists of FoxA1 (transcription factor) and EZH2 (histone,mark) IP experiments from Franco et al. 2015 (GEO: GSE59530, PMID: 25752574) and Popovic et al. 2014 (GEO: GSE57632, PMID: 25188243), respectively. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from running the full-sized tests can be viewed on the nf-core website.

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!

Online videos

A short talk about the history, current status and functionality on offer in this pipeline was given by Jose Espinosa-Carrasco (@joseespinosa) on 26th July 2022 as part of the nf-core/bytesize series.

You can find numerous talks on the nf-core events page from various topics including writing pipelines/modules in Nextflow DSL2, using nf-core tooling, running nf-core pipelines as well as more generic content like contributing to Github. Please check them out!

Pipeline summary

1. Raw read QC (FastQC)
2. Adapter trimming (Trim Galore!)
3. Choice of multiple aligners 1.(BWA) 2.(Chromap) 3.(Bowtie2) 4.(STAR)
4. Mark duplicates (picard)
5. Merge alignments from multiple libraries of the same sample (picard)
   1. Re-mark duplicates (picard)
   2. Filtering to remove:
      • reads mapping to blacklisted regions (SAMtools, BEDTools)
      • reads that are marked as duplicates (SAMtools)
      • reads that are not marked as primary alignments (SAMtools)
      • reads that are unmapped (SAMtools)
      • reads that map to multiple locations (SAMtools)
      • reads containing > 4 mismatches (BAMTools)
      • reads that have an insert size > 2kb (BAMTools; paired-end only)
      • reads that map to different chromosomes (Pysam; paired-end only)
      • reads that arent in FR orientation (Pysam; paired-end only)
      • reads where only one read of the pair fails the above criteria (Pysam; paired-end only)
   3. Alignment-level QC and estimation of library complexity (picard, Preseq)
   4. Create normalised bigWig files scaled to 1 million mapped reads (BEDTools, bedGraphToBigWig)
   5. Generate gene-body meta-profile from bigWig files (deepTools)
   6. Calculate genome-wide IP enrichment relative to control (deepTools)
   7. Calculate strand cross-correlation peak and ChIP-seq quality measures including NSC and RSC (phantompeakqualtools)
   8. Call broad/narrow peaks (MACS3)
   9. Annotate peaks relative to gene features (HOMER)
   10. Create consensus peakset across all samples and create tabular file to aid in the filtering of the data (BEDTools)
   11. Count reads in consensus peaks (featureCounts)
   12. PCA and clustering (R, DESeq2)
6. Create IGV session file containing bigWig tracks, peaks and differential sites for data visualisation (IGV).
7. Present QC for raw read, alignment, peak-calling and differential binding results (MultiQC, R)

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

To run on your data, prepare a tab-separated samplesheet with your input data. Please follow the documentation on samplesheets for more details. An example samplesheet for running the pipeline looks as follows:

sample,fastq_1,fastq_2,replicate,antibody,control,control_replicate
WT_BCATENIN_IP,BLA203A1_S27_L006_R1_001.fastq.gz,,1,BCATENIN,WT_INPUT,1
WT_BCATENIN_IP,BLA203A25_S16_L001_R1_001.fastq.gz,,2,BCATENIN,WT_INPUT,2
WT_BCATENIN_IP,BLA203A25_S16_L002_R1_001.fastq.gz,,2,BCATENIN,WT_INPUT,2
WT_BCATENIN_IP,BLA203A25_S16_L003_R1_001.fastq.gz,,2,BCATENIN,WT_INPUT,2
WT_BCATENIN_IP,BLA203A49_S40_L001_R1_001.fastq.gz,,3,BCATENIN,WT_INPUT,3
WT_INPUT,BLA203A6_S32_L006_R1_001.fastq.gz,,1,,,
WT_INPUT,BLA203A30_S21_L001_R1_001.fastq.gz,,2,,,
WT_INPUT,BLA203A30_S21_L002_R1_001.fastq.gz,,2,,,
WT_INPUT,BLA203A31_S21_L003_R1_001.fastq.gz,,3,,,

Now, you can run the pipeline using:

nextflow run nf-core/chipseq --input samplesheet.csv --outdir <OUTDIR> --genome GRCh37 -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

See [usage docs](https://nf-co.re/