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Holly Ruess authored
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Astrocyte ATAC-seq analysis Workflow Package

pipeline Status Coverage Report Nextflow Astrocyte

Introduction

BICF ATAC-seq is a bioinformatics best-practice analysis pipeline used for ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) data analysis at BICF at UT Southwestern Department of Bioinformatics.

The pipeline uses Nextflow, 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. 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 using a simple web interface.

Current version of the software and issue reports are at https://git.biohpc.swmed.edu/BICF/Astrocyte/atacseq_analysis

To download the current (working not tagged) version of the software

$ git clone git@git.biohpc.swmed.edu:BICF/Astrocyte/atacseq_analysis.git

Input files

1) Fastq Files
  • You will need the full path to the files for the Bash Scipt

Design file

  • The Design file is a tab-delimited file with 4 columns for Single-End and 5 columns for Paired-End. Letter, numbers, and underlines can be used in the names. However, the names must begin with a letter. Columns must be as follows:

    1. sample_id - The id of the sample. This will be the header in output files, please make sure it is concise
    2. experiment_id - Same name given for all replicates of treatment. Will be used for the consensus header.
    3. replicate - Replicate number
    4. fastq_read1 - Name of fastq file 1 for SE or PE data
    5. fastq_read2 - Name of fastq file 2 for PE data
  • See HERE for an example design file, paired-end

  • See HERE for an example design file, single-end

Pipeline

  • There are 9 steps to the pipeline
    1. Check input files
    2. Trim adaptors with TrimGalore!
    3. Map reads with BWA, filter with SamTools, and sort with Sambamba
    4. Mark duplicates with Sambamba, Filter reads with SamTools, calculate percentage of reads in mitochondria, and calculate library complexity with SamTools and bedtools
    5. Calculate cross-correlation using PhantomPeakQualTools
    6. Call peaks with MACS2 from overlaps of pooled replicates
    7. Call consensus peaks and optional removal of blacklist peaks
    8. QC metrics
    9. MultiQC report

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 *.flagstat.qc QC metrics from the mapping process
alignReads *.bam sorted bam file
filterReads *.dedup.qc QC metrics of find duplicate reads (sambamba)
filterReads *.dedup.bam filtered bam file with duplicate reads removed
filterReads *.dedup.bam.bai indexed filtered bam file
filterReads *.dedup.flagstat.qc QC metrics of filtered bam file (mapping stats, samtools)
filterReads *.dedup.pbc.qc QC metrics of library complexity
filterReads *.pctmito.tsv QC percentage of reads in mitochondria
convertReads *.filt.nodup.bedse.gz bed alignment in BEDPE format
convertReads *.tagAlign.gz bed alignent in BEDPE or BEDSE format
crossReads *.cc.plot.pdf Plot of cross-correlation to assess signal-to-noise ratios
crossReads *.cc.qc cross-correlation metrics. File HEADER
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 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 *.replicated_noblacklist.narrowPeak peaks supported by multiple testing (replicates and pseudo-replicates) with blacklist regions removed
experimentQC coverage.pdf plot to assess the sequencing depth of a given sample
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
experimentQC *.FRiPscore.tsv File containing FRiP score
experimentQC *.TSSenrichment.tsv File containing TSS enrichment
experimentQC *_large_tss-enrich.pdf TSS Enrichment heatmap and metagene plot
experimentQC *_tss-enrich.pdf TSS Enrichment metagene plot
experimentQC *.fragment_length_linear.pdf Paired-end only, fragment/insert size densities, linear
experimentQC *.fragment_length_linear.pdf Paired-end only, log10 fragment/insert size densities
experimentQC *.fragment_length_count.txt Paired-end only, count and fragment length, raw data
multiqcReport multiqc_report.html Quality control report of percent mitochondria, NRF, PBC1, PBC2, NSC, and RSC. Also contains software versions and references to cite.

Common Quality Control Metrics

  • These are the list of files that should be reviewed before continuing on with the ATAC-seq 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 ATAC-seq standards HERE;
    2. crossReads/*cc.plot.pdf: make sure your sample data has the correct signal intensity and location. See HERE for more details.
    3. filterReads/sample/*.pbc.qc: column 6 (NRF) > 0.9, column 7 (PBC1) > 0.9, and column 8 (PBC2) >3.
    4. experimentQC/coverage.pdf, experimentQC/heatmeap_SpearmanCorr.pdf, experimentQC/heatmeap_PearsonCorr.pdf: See HERE for more details.
    5. experimentQC/: Common Quality controls for ATAC-seq: FRiP score, TSS enrichment, Fragment/Insert length densities (paired-end only)

Common Errors

If you find an error, please let the BICF know and we will add it here.

Citation

Please cite individual programs and versions used HERE, and the pipeline doi: coming soon. Please cite in publications: Pipeline was developed by BICF from funding provided by Cancer Prevention and Research Institute of Texas (RP150596).

Credits

This example worklow is derived from original scripts kindly contributed by the Bioinformatic Core Facility (BICF), in the Department of Bioinformatics.