Abstract
BackgroundChromatin-immunoprecipitation followed by sequencing (ChIP-seq) is the method of choice for mapping genome-wide binding of chromatin-associated factors. However, broadly applicable methods for between-sample comparisons are lacking.ResultsHere, we introduce SNP-ChIP, a method that leverages small-scale intra-species polymorphisms, mainly SNPs, for quantitative spike-in normalization of ChIP-seq results. Sourcing spike-in material from the same species ensures antibody cross-reactivity and physiological coherence, thereby eliminating two central limitations of traditional spike-in approaches. We show that SNP-ChIP is robust to changes in sequencing depth and spike-in proportions, and reliably identifies changes in overall protein levels, irrespective of changes in binding distribution. Application of SNP-ChIP to test cases from budding yeast meiosis allowed discovery of novel regulators of the chromosomal protein Red1 and quantitative analysis of the DNA-damage associated histone modification γ-H2AX.ConclusionSNP-ChIP is fully compatible with the intra-species diversity of humans and most model organisms and thus offers a general method for normalizing ChIP-seq results.
Highlights
Chromatin-immunoprecipitation followed by sequencing (ChIP-seq) is the method of choice for mapping genome-wide binding of chromatin-associated factors
If there is sufficient genetic diversity to allow a large fraction of sequencing reads to be assigned to the genomes of origin, Single-nucleotide polymorphism (SNP)-Chromatin immunoprecipitation (ChIP) allows the generation of genome-wide target distribution profiles
SNP-ChIP of a rapidly evolving chromosomal protein To test the utility of intra-species spike-ins, we turned to chromatin analyses in yeast
Summary
Chromatin-immunoprecipitation followed by sequencing (ChIP-seq) is the method of choice for mapping genome-wide binding of chromatin-associated factors. For sparsely bound proteins, such as transcription factors, inter-sample normalization can often be achieved using statistical methods [2] or ChIP followed by real-time quantitative PCR (ChIP-qPCR) [5]. These methods, either assume constant global signal or a constant signal at selected genes as basis for normalization, which is difficult to verify, in particular for more broadly distributed factors.
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