# **ChIP-seq Manual**
## Version 1.1.2
## June 21, 2020
# BICF ChIP-seq Pipeline
|*master*|*dev*|
|:-:|:-:|
|[](https://git.biohpc.swmed.edu/BICF/Astrocyte/chipseq_analysis/commits/master)|[](https://git.biohpc.swmed.edu/BICF/Astrocyte/chipseq_analysis/commits/dev)|
|[](https://git.biohpc.swmed.edu/BICF/Astrocyte/chipseq_analysis/commits/master)|[](https://git.biohpc.swmed.edu/BICF/Astrocyte/chipseq_analysis/commits/dev)|
[](https://www.nextflow.io/)
[](https://astrocyte-test.biohpc.swmed.edu/static/docs/index.html)
[](https://doi.org/10.5281/zenodo.2648844)
## Introduction
BICF ChIP-seq is a bioinformatics best-practice analysis pipeline used for ChIP-seq (chromatin immunoprecipitation sequencing) data analysis at [BICF](http://www.utsouthwestern.edu/labs/bioinformatics/) at [UT Southwestern Department of Bioinformatics](http://www.utsouthwestern.edu/departments/bioinformatics/).
The pipeline uses [Nextflow](https://www.nextflow.io), a bioinformatics workflow tool. It pre-processes raw data from FastQ inputs, aligns the reads and performs extensive quality-control on the results.
This pipeline is primarily used with a SLURM cluster on the [BioHPC Cluster](https://portal.biohpc.swmed.edu/content/). However, the pipeline should be able to run on any system that supports Nextflow.
Additionally, the pipeline is designed to work with [Astrocyte Workflow System](https://astrocyte.biohpc.swmed.edu/static/docs/index.html) using a simple web interface.
Current version of the software and issue reports are at
https://git.biohpc.swmed.edu/BICF/Astrocyte/chipseq_analysis
To download the current version of the software
```bash
$ git clone git@git.biohpc.swmed.edu:BICF/Astrocyte/chipseq_analysis.git
```
## Input files
##### 1) Fastq Files
+ You will need the full path to the files for the Bash Scipt
##### 2) Design File
+ The Design file is a tab-delimited file with 8 columns for Single-End and 9 columns for Paired-End. Letter, numbers, and underlines can be used in the names. However, the names can only begin with a letter. Columns must be as follows:
1. sample_id a short, unique, and concise name used to label output files; will be used as a control_id if it is the control sample
2. experiment_id biosample_treatment_factor; same name given for all replicates of treatment. Will be used for the consensus header.
3. biosample symbol for tissue type or cell line
4. factor symbol for antibody target
5. treatment symbol of treatment applied
6. replicate a number, usually from 1-3 (i.e. 1)
7. control_id sample_id name that is the control for this sample
8. fastq_read1 name of fastq file 1 for SE or PC data
9. fastq_read2 name of fastq file 2 for PE data
+ See [HERE](test_data/design_ENCSR729LGA_PE.txt) for an example design file, paired-end
+ See [HERE](test_data/design_ENCSR238SGC_SE.txt) for an example design file, single-end
##### 3) Bash Script
+ You will need to create a bash script to run the CHIPseq pipeline on [BioHPC](https://portal.biohpc.swmed.edu/content/)
+ This pipeline has been optimized for the correct partition
+ See [HERE](docs/CHIPseq.sh) for an example bash script
+ The parameters that must be specified are:
- --reads '/path/to/files/name.fastq.gz'
- --designFile '/path/to/file/design.txt',
- --genome 'GRCm38', 'GRCh38', or 'GRCh37' (if you need to use another genome contact the [BICF](mailto:BICF@UTSouthwestern.edu))
- --pairedEnd 'true' or 'false' (where 'true' is PE and 'false' is SE; default 'false')
- --skipDiff 'true' or 'false' (where 'true' is skip differential peak and 'false' is do peak differential peak calling; default 'false')
- --skipMotif 'true' or 'false' (where 'true' is skip motif calling and 'false' is do motif calling; default 'false')
- --skipPlotProfile 'true' or 'false' (where 'true' is skip metageneplot for TSS and 'false' is do metageneplot for TSS; default 'false')
- --outDir (optional) path and folder name of the output data, example: /home2/s000000/Desktop/Chipseq_output (if not specified will be under workflow/output/)
## Pipeline
+ There are 11 steps to the pipeline
1. Check input files
2. Trim adaptors TrimGalore!
3. Aligned trimmed reads with bwa, and sorts/converts to bam with samtools
4. Mark duplicates with Sambamba, and filter reads with samtools
5. Quality metrics with deep tools
6. Calculate cross-correlation using PhantomPeakQualTools
7. Call peaks with MACS
8. Calculate consensus peaks
9. Annotate all peaks using ChipSeeker
10. Calculate Differential Binding Activity with DiffBind (If more than 1 rep in more than 1 experiment)
11. Use MEME-ChIP to find motifs in original peaks
12. Plot enrichment of signal around TSS
See [FLOWCHART](docs/flowchart.pdf)
## Output Files
Folder | File | Description
--- | --- | ---
design | N/A | Inputs used for analysis; can ignore
trimReads | *_trimming_report.txt | report detailing how many reads were trimmed
trimReads | *_trimmed.fq.gz | trimmed fastq files used for analysis
alignReads | *.srt.bam.flagstat.qc | QC metrics from the mapping process
alignReads | *.srt.bam | sorted bam file
filterReads | *.dup.qc | QC metrics of find duplicate reads (sambamba)
filterReads | *.filt.nodup.bam | filtered bam file with duplicate reads removed
filterReads | *.filt.nodup.bam.bai | indexed filtered bam file
filterReads | *.filt.nodup.flagstat.qc | QC metrics of filtered bam file (mapping stats, samtools)
filterReads | *.filt.nodup.pbc.qc | QC metrics of library complexity
convertReads | *.filt.nodup.bedse.gz | bed alignment in BEDPE format
convertReads | *.filt.nodup.tagAlign.gz | bed alignent in BEDPE format, same as bedse unless samples are paired-end
multiqcReport | multiqc_report.html | Quality control report of NRF, PBC1, PBC2, NSC, and RSC. Also contains software versions and references to cite.
experimentQC | coverage.pdf | plot to assess the sequencing depth of a given sample
experimentQC | *_fingerprint.pdf | plot to determine if the antibody-treatment enriched sufficiently
experimentQC | heatmeap_SpearmanCorr.pdf | plot of Spearman correlation between samples
experimentQC | heatmeap_PearsonCorr.pdf | plot of Pearson correlation between samples
experimentQC | sample_mbs.npz | array of multiple BAM summaries
crossReads | *.cc.plot.pdf | Plot of cross-correlation to assess signal-to-noise ratios
crossReads | *.cc.qc | cross-correlation metrics. File [HEADER](docs/xcor_header.txt)
callPeaksMACS | pooled/*pooled.fc_signal.bw | bigwig data file; raw fold enrichment of sample/control
callPeaksMACS | pooled/*pooled_peaks.xls | Excel file of peaks
callPeaksMACS | pooled/*.pvalue_signal.bw | bigwig data file; sample/control signal adjusted for pvalue significance
callPeaksMACS | pooled/*_pooled.narrowPeak | peaks file; see [HERE](https://genome.ucsc.edu/FAQ/FAQformat.html#format12) for ENCODE narrowPeak header format
consensusPeaks | *.rejected.narrowPeak | peaks not supported by multiple testing (replicates and pseudo-replicates)
consensusPeaks | *.replicated.narrowPeak | peaks supported by multiple testing (replicates and pseudo-replicates)
peakAnnotation | *.chipseeker_annotation.tsv | annotated narrowPeaks file
peakAnnotation | *.chipseeker_pie.pdf | pie graph of where narrow annotated peaks occur
peakAnnotation | *.chipseeker_upsetplot.pdf | upsetplot showing the count of overlaps of the genes with different annotated location
motifSearch | *_memechip/index.html | interactive HTML link of MEME output
motifSearch | sorted-*.replicated.narrowPeak | Top 600 peaks sorted by p-value; input for motifSearch
motifSearch | *_memechip/combined.meme | MEME identified motifs
diffPeaks | heatmap.pdf | Use only for replicated samples; heatmap of relationship of peak location and peak intensity
diffPeaks | normcount_peaksets.txt | Use only for replicated samples; peak set values of each sample
diffPeaks | pca.pdf | Use only for replicated samples; PCA of peak location and peak intensity
diffPeaks | *_diffbind.bed | Use only for replicated samples; bed file of peak locations between replicates
diffPeaks | *_diffbind.csv | Use only for replicated samples; CSV file of peaks between replicates
plotProfile | plotProfile.png | Plot profile of the TSS region
plotProfile | computeMatrix.gz | Compute Matrix from deeptools to create custom plots other than plotProfile
## Common Quality Control Metrics
+ These are the list of files that should be reviewed before continuing on with the CHIPseq experiment. If your experiment fails any of these metrics, you should pause and re-evaluate whether the data should remain in the study.
1. multiqcReport/multiqc_report.html: follow the ChiP-seq standards [HERE](https://www.encodeproject.org/chip-seq/);
2. experimentQC/*_fingerprint.pdf: make sure the plots information is correct for your antibody/input. See [HERE](https://deeptools.readthedocs.io/en/develop/content/tools/plotFingerprint.html) for more details.
3. crossReads/*cc.plot.pdf: make sure your sample data has the correct signal intensity and location. See [HERE](hhttps://ccg.epfl.ch//var/sib_april15/cases/landt12/strand_correlation.html) for more details.
4. crossReads/*.cc.qc: Column 9 (NSC) should be > 1.1 for experiment and < 1.1 for input. Column 10 (RSC) should be > 0.8 for experiment and < 0.8 for input. See [HERE](https://genome.ucsc.edu/encode/qualityMetrics.html) for more details.
5. experimentQC/coverage.pdf, experimentQC/heatmeap_SpearmanCorr.pdf, experimentQC/heatmeap_PearsonCorr.pdf: See [HERE](https://deeptools.readthedocs.io/en/develop/content/list_of_tools.html) for more details.
## Common Errors
If you find an error, please let the [BICF](mailto:BICF@UTSouthwestern.edu) know and we will add it here.
## Citation
Please cite individual programs and versions used [HERE](docs/references.md), and the pipeline doi:[10.5281/zenodo.2648844](https://doi.org/10.5281/zenodo.2648844). Please cite in publications: Pipeline was developed by BICF from funding provided by Cancer Prevention and Research Institute of Texas (RP150596).
## Programs and Versions
+ python/3.6.1-2-anaconda [website](https://www.anaconda.com/download/#linux) [citation](docs/references.md)
+ trimgalore/0.4.1 [website](https://github.com/FelixKrueger/TrimGalore) [citation](docs/references.md)
+ cutadapt/1.9.1 [website](https://cutadapt.readthedocs.io/en/stable/index.html) [citation](docs/references.md)
+ bwa/intel/0.7.12 [website](http://bio-bwa.sourceforge.net/) [citation](docs/references.md)
+ samtools/1.6 [website](http://samtools.sourceforge.net/) [citation](docs/references.md)
+ sambamba/0.6.6 [website](http://lomereiter.github.io/sambamba/) [citation](docs/references.md)
+ bedtools/2.26.0 [website](https://bedtools.readthedocs.io/en/latest/) [citation](docs/references.md)
+ deeptools/2.5.0.1 [website](https://deeptools.readthedocs.io/en/develop/) [citation](docs/references.md)
+ phantompeakqualtools/1.2 [website](https://github.com/kundajelab/phantompeakqualtools) [citation](docs/references.md)
+ macs/2.1.0-20151222 [website](http://liulab.dfci.harvard.edu/MACS/) [citation](docs/references.md)
+ UCSC_userApps/v317 [website](https://genome.ucsc.edu/util.html) [citation](docs/references.md)
+ R/3.4.1 [website](https://www.r-project.org/) [citation](docs/references.md)
+ SPP/1.14
+ meme/4.11.1-gcc-openmpi [website](http://meme-suite.org/doc/install.html?man_type=web) [citation](docs/references.md)
+ ChIPseeker [website](https://bioconductor.org/packages/release/bioc/html/ChIPseeker.html) [citation](docs/references.md)
+ DiffBind [website](https://bioconductor.org/packages/release/bioc/html/DiffBind.html) [citation](docs/references.md)
## Credits
This example worklow is derived from original scripts kindly contributed by the Bioinformatic Core Facility ([BICF](https://www.utsouthwestern.edu/labs/bioinformatics/)), in the [Department of Bioinformatics](https://www.utsouthwestern.edu/departments/bioinformatics/).