+ 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
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)
consensusPeaks | unique_experiments.csv | design file; can ignore
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
...
...
@@ -119,10 +118,11 @@ diffPeaks | *_diffbind.csv | Use only for replicated samples; CSV file of peaks
## 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.filterReads/*.filt.nodup.pbc.qc: follow the ChiP-seq standards [HERE](https://www.encodeproject.org/chip-seq/); NRF>0.9, PBC1>0.9, and PBC2>10
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/*.filt.nodup.tagAlign.15.tagAlign.gz.cc.plot.pdf: make sure your sample data has the correct signal intensity and location. See [HERE](https://hbctraining.github.io/Intro-to-ChIPseq/lessons/06_QC_cross_correlation.html) for more details.
4. crossReads/*.filt.nodup.tagAlign.15.tagAlign.gz.cc.qc: Column 9 (NSC) should be > 1.1 for experiment and <1.1forinput.Column10(RSC)shouldbe> 0.8 for experiment and < 0.8 for input. See [HERE](https://hbctraining.github.io/Intro-to-ChIPseq/lessons/06_QC_cross_correlation.html) for more details.
3. crossReads/*cc.plot.pdf: make sure your sample data has the correct signal intensity and location. See [HERE](https://hbctraining.github.io/Intro-to-ChIPseq/lessons/06_QC_cross_correlation.html) for more details.
4. crossReads/*.cc.qc: Column 9 (NSC) should be > 1.1 for experiment and <1.1forinput.Column10(RSC)shouldbe> 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
...
...
@@ -140,15 +140,19 @@ If you find an error, please let the [BICF](mailto:BICF@UTSouthwestern.edu) know
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/).
Please cite in publications: Pipeline was developed by BICF from funding provided by Cancer Prevention and Research Institute of Texas (RP150596).
## Citation
Please cite individual programs and versions used [HERE](docs/references.txt). Also, please look out for our pipeline to be published in the future [HERE](https://zenodo.org/).
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.
### Pipeline Steps
Report issues to the Bioinformatic Core Facility [BICF](mailto:BICF@UTSouthwestern.edu)
1) Trim adaptors TrimGalore!
2) Align with BWA
3) Filter reads with Sambamba S
4) Quality control with DeepTools
5) Calculate Cross-correlation using SPP and PhantomPeakQualTools
6) Signal profiling using MACS2
7) Call consenus peaks
8) Annotate all peaks using ChipSeeker
9) Use MEME-ChIP to find motifs in original peaks
10) Find differential expressed peaks using DiffBind (If more than 1 experiment)
### Pipeline Steps
+ 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
## Workflow Parameters
reads - Choose all ChIP-seq fastq files for analysis.
pairedEnd - Choose True/False if data is paired-end
design - Choose the file with the experiment design information. TSV format
1. One or more input FASTQ files from a ChIP-seq expereiment and a design file with the link bewetwen the same file name and sample id (required) - Choose all ChIP-seq fastq files for analysis.
2. In single-end sequencing, the sequencer reads a fragment from only one end to the other, generating the sequence of base pairs. In paired-end reading it starts at one read, finishes this direction at the specified read length, and then starts another round of reading from the opposite end of the fragment. (Paired-end: True, Single-end: False) (required)
3. A design file listing sample id, fastq files, corresponding control id and additional information about the sample.
genome - Choose a genomic reference (genome).
4. Reference species and genome used for alignment and subsequent analysis. (required)
5. Run differential peak analysis (required). Must have at least 2 replicates per experiment and at least 2 experiments.
6. Run motif calling (required). Top 600 peaks sorted by p-value.
7. Ensure configuraton for astrocyte. (required; always true)
## Design file
The following columns are necessary, must be named as in template. An design file template can be downloaded [HERE](https://git.biohpc.swmed.edu/bchen4/chipseq_analysis/raw/master/docs/design_example.csv)
SampleID
The id of the sample. This will be the header in output files, please make sure it is concise
Tissue
Tissue of the sample
Factor
Factor of the experiment
Condition
This is the group that will be used for pairwise differential expression analysis
Replicate
Replicate id
Peaks
The file name of the peak file for this sample
bamReads
The file name of the IP BAM for this sample
bamControl
The file name of the control BAM for this sample
ContorlID
The id of the control sample
PeakCaller
The peak caller used
+ 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
## 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
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)
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
## 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](https://hbctraining.github.io/Intro-to-ChIPseq/lessons/06_QC_cross_correlation.html) for more details.
4. crossReads/*.cc.qc: Column 9 (NSC) should be > 1.1 for experiment and <1.1forinput.Column10(RSC)shouldbe> 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.
### Credits
This example worklow is derived from original scripts kindly contributed by the Bioinformatic Core Facility (BICF), Department of Bioinformatics
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/).
Please cite in publications: Pipeline was developed by BICF from funding provided by Cancer Prevention and Research Institute of Texas (RP150596).
