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This pipeline identifies regions of significant protein binding ("peaks") based on an annotated genome. 10 hour minimum ($470 internal, $600 external) per project.

1. Quality Assessment

Quality of data assessed by FastQC; results of quality assessment will be evaluated prior to downstream analysis.

  • Deliverables:
    • reports generated by FastQC
  • Tools used:
    • FastQC: (Andrews 2010) used to generate quality summaries of data:
      • Per base sequence quality report: useful for deciding if trimming necessary.
      • Sequence duplication levels: evaluation of library complexity. Higher levels of sequence duplication may be expected for high coverage RNAseq data.
      • Overrepresented sequences: evaluation of adapter contamination.

2. Mapping

Mapping to genome reference performed using BWA.

  • Deliverables
    • Mapping results, as bam files and mapping statistics.
  • Tools Used:
    • BWA: (Li 2013) primary aligner used to generate read alignments.
    • Samtools: (Li 2009) used to prepare bams and generate mapping statistics.

4. Peak Calling

Counting the number of normalized ChIP-seq reads compared to a background control (Input or mock ChIP) to identify regions of binding enrichment.

  • Deliverables
    • Peak calls as narrowPeak (BED 6+) files, containing p-value, q-value, and fold enrichment scores for each peak.
    • Per-base normalized signal files as bigWigs.
  • Tools Used:
    • MACS2: (Zhang, 2008) used to identify and score peak regions.
    • bedtools (Quinlan, 2010) used for optional blacklist filtering.

5. Significance Threshhold Analysis

Statistical analysis and informed heuristics to determine appropriate significance threshhold(s) for identifying peaks for downstream analysis.

  • Deliverables
    • Summary file outlining peak counts at selected levels (High, Medium, and Low stringency) and master file containing counts over a wide range of q-values and fold enrichment values. Peak count vs q-value and fold enrichment plots.
  • Tools Used:
    • R, in-house scripts and ggplot: used to produce peak count statistics and plots.






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