ChIP-seq Data Processing and Relative and Quantitative Signal Normalization for <em>Saccharomyces cerevisiae</em>

Chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) is a widely used technique for genome-wide analyses of protein-DNA interactions. This protocol provides a guide to ChIP-seq data processing in <i>Saccharomyces cerevisiae</i>, with a focus on signal normalization to address data biases and enable meaningful comparisons within and between samples. Designed for researchers with minimal bioinformatics experience, it includes practical overviews and refers to scripting examples for key tasks, such as configuring computational environments, trimming and aligning reads, processing alignments, and visualizing signals. This protocol employs the <b>s</b>ans-spike-<b>i</b>n method for <b>q</b>uantitative <b>ChIP</b>-seq (siQ-ChIP) and normalized coverage for absolute and relative comparisons of ChIP-seq data, respectively. While spike-in normalization, which is semiquantitative, is addressed for context, siQ-ChIP and normalized coverage are recommended as mathematically rigorous and reliable alternatives. Key features • ChIP-seq data processing workflow for Linux and macOS integrating data acquisition, trimming, alignment, processing, and multiple forms of signal computation, with a focus on reproducibility. • ChIP-seq signal generation using siQ-ChIP to quantify absolute IP efficiency-providing a rigorous alternative to spike-in normalization-and normalized coverage for relative comparisons. • Broad applicability demonstrated with <i>Saccharomyces cerevisiae</i> (experimental) and <i>Schizosaccharomyces pombe</i> (spike-in) data but suitable for ChIP-seq in any species. • In-depth notes and troubleshooting guide users through setup challenges and key concepts in basic bioinformatics, data processing, and signal computation. Graphical overview Flowchart depicting ChIP-seq data processing steps covered in this protocol.