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ChIP-seq data processing and relative and quantitative signal normalization for Saccharomyces cerevisiae

ABSTRACT: 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 Saccharomyces cerevisiae, 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 sans-spike-in method for quantitative ChIP-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.

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE288548 | GEO | 2025/02/02

REPOSITORIES: GEO

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Project description:Acute cellular stress is known to induce a global reduction in protein translation through suppression of cap dependent translation. However, selective translation in response to acute stress has been shown to play important roles in regulating the stress response. An accurate transcriptome-wide profile of acute cellular stress-induced translational changes has been challenging to obtain. Commonly used data normalization methods, such as quantile normalization, operate based on the assumption that any systematic shifts are artifacts introduced from experimental procedures. Consequently, if applied to profiling acute cellular stress-induced protein translation changes, these methods are expected to produce biased estimates. To address this issue, here we designed, generated, and evaluated a panel of 16 oligomers to serve as external standards for ribosome profiling studies. Using Sodium Arsenite treatment-induced oxidative stress in lymphoblastoid cell lines as a model system, we applied spike-in oligomers as external standards based on quantifications of monosomal RNA extracted from each sample. We found our spike-in oligomers to display a linear correlation between the observed and the expected, with small but significant ratio compression at the lower concentration range, and span the expected quantitative range in the observed data, which covers 97 % of the quantitated endogenous genes. We found popular global scaling normalization approaches to introduce both high levels of false positives and false negatives in differential expression analysis. Using the expected fold changes constructed from spike-in external controls, we found in our dataset that TMM normalization produced 87.5% false positives when a P value cutoff of 0.1 is used (i.e. 10% expected false positive rate) and on average produced a systematic shift of fold change by 3.25 fold. These results highlight the consequences of applying global scaling approaches to conditions that clearly violate their key assumptions. As an alternative, we found using spike-in quantifications as control genes in RUVg normalization recapitulated the expected stress induced global reduction of translation, and resulted in little, if any, systematic shifts in spike-in constructed true positives. Finally, using spike-in constructed true positives and true negatives, we explored alternative normalization approaches for acute cellular stress response ribo-seq studies. We found that a simple approach that quantile normalized data from control and treated samples separately, which we termed respective quantile normalization, produced expected results in spike-in quantification, and resulted in little, if any, systematic bias on fold change in endogenous genes. Additionally, we found that under certain parameters, using endogenous control genes for RUVg normalization best recapitulate the expected. Our results clearly demonstrated the utility of our spike-in oligomers, both for constructing expected results as controls and for data normalization. Our exploration of different normalization approaches highlights the issues in applying global scaling normalization when key assumptions are clearly not met. We show that a respective quantile normalization approach or normalization with endogenous control genes are viable options worth considering as a more generalizable approach for stress response ribo-seq studies. This conclusion is likely applicable to other types of studies that involve global shifts in expression profiles between comparison groups of interests.

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ChIP-seq data processing and relative and quantitative signal normalization for Saccharomyces cerevisiae

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