ChIP-Rx: Arabidopsis Chromatin Profiling Using Quantitative ChIP-Seq.

Abstract

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is widely used to probe the chromatin landscape of transcription factors, chromatin components, and associated proteins. Conventional ChIP normalization procedures robustly allow estimating differences in local enrichment across genomic regions. Yet, inter-sample comparisons can be biased by technical variability and biological differences. This is notably the case when samples display large differences in the abundance of the target protein or its enrichment at chromatin. For example, epigenome defects are improperly detected or quantified upon large-effect genetic or chemical inhibition of chromatin modifiers. To circumvent these caveats and robustly determine biological variations while minimizing technical variability, ChIP adaptations using an external reference have flourished. Here, we describe a step-by-step protocol employing a reference exogenous chromatin (ChIP-Rx) that allows absolute comparisons of epigenome variations in Arabidopsis samples displaying drastic differences in chromatin mark abundance. In contrast to the originally published ChIP-Rx approach, which assumes that exogenous spike-in references are constant across samples, the method detailed here involves the sequencing of each input sample to account for technical variability in initial reference chromatin contents. We also report a detailed computational workflow with an accompanying Github resource to help in calculating spike-in normalization factors, applying them to normalize epigenome tracks, and performing spike-in normalized inter-sample differential analyses. We propose two ways of computing the spike-in factor: a classically used method based on raw counts and a noise-corrected method using peak detection on the exogenous genome.