How do I interpret the Peak Calling QC Report?

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Similar to the Alignment QC Report, following a secondary analysis Peak Calling experiment, a Peak Calling QC Report will be generated. Within this report (found under Peak Calling > QC Report) you will find Peak Calling Stats and Metrics, Top Called Peaks, and Peak Annotation Plots. In the right window pane of the report will be descriptions of each of these categories. 

For evaluating the quality of peak calling, the Fraction of Reads in Peaks (FRiP) score is a crucial metric. This score represents the ratio of unique reads that are associated with statistically significant peaks. These peaks are identified by using an associated IgG negative control as a background representation, which helps to differentiate true signal from background noise. Generally, a high-quality FRiP score is considered to be greater than 0.2, indicating a robust enrichment of reads at identified peak regions.

In addition to the FRiP score, a Top Called Peaks graph and file will be generated in a .csv format. This file provides the precise genomic locations of these significant peaks, including their chromosome, start, and end coordinates. These locations can be visually cross-referenced and examined using genome browsers such as the Integrative Genomics Viewer (IGV) to confirm their presence and evaluate their surrounding genomic context. This visual inspection can help to validate the peak calling results and identify any potential biases or artifacts.

Furthermore, a graphical Peak Annotation will be included in the QC Report. This annotation categorizes each identified peak based on its genomic location. Common genomic categories include promoters, introns, exons, untranslated regions (UTRs), intergenic regions, and transcription start sites (TSS). This annotation provides valuable insights into the biological relevance of the identified peaks, indicating whether they are enriched in gene regulatory regions or other functional elements. Analyzing the distribution of peaks across these categories can help to understand the overall landscape of enriched regions within the genome.