Abstract
BackgroundCell-specific gene expression is controlled by epigenetic modifications and transcription factor binding. While genome-wide maps for these protein-DNA interactions have become widely available, quantitative comparison of the resulting ChIP-Seq data sets remains challenging. Current approaches to detect differentially bound or modified regions are mainly borrowed from RNA-Seq data analysis, thus focusing on total counts of fragments mapped to a region, ignoring any information encoded in the shape of the peaks.ResultsHere, we present MMDiff, a robust, broadly applicable method for detecting differences between sequence count data sets. Based on quantifying shape changes in signal profiles, it overcomes challenges imposed by the highly structured nature of the data and the paucity of replicates.We first use a simulated data set to compare the performance of MMDiff with results obtained by four alternative methods. We demonstrate that MMDiff excels when peak profiles change between samples. We next use MMDiff to re-analyse a recent data set of the histone modification H3K4me3 elucidating the establishment of this prominent epigenomic marker. Our empirical analysis shows that the method yields reproducible results across experiments, and is able to detect functional important changes in histone modifications. To further explore the broader applicability of MMDiff, we apply it to two ENCODE data sets: one investigating the histone modification H3K27ac and one measuring the genome-wide binding of the transcription factor CTCF. In both cases, MMDiff proves to be complementary to count-based methods. In addition, we can show that MMDiff is capable of directly detecting changes of homotypic binding events at neighbouring binding sites. MMDiff is readily available as a Bioconductor package.ConclusionsOur results demonstrate that higher order features of ChIP-Seq peaks carry relevant and often complementary information to total counts, and hence are important in assessing differential histone modifications and transcription factor binding. We have developed a new computational method, MMDiff, that is capable of exploring these features and therefore closes an existing gap in the analysis of ChIP-Seq data sets.
Highlights
Cell-specific gene expression is controlled by epigenetic modifications and transcription factor binding
This study focused on the question of how profiles of this mark are shaped by Cfp1, which is known to be a conserved DNA-binding subunit of the H3K4 histone methyltransferase (HMT) Set1 complex
We find that the majority of promoter regions of 928 genes associated with RNA binding and processing, translation and structural constituents of ribosomes are clustering together showing a substantial decrease of Trimethylation of Lysine on histone 3 (H3K4me3) levels
Summary
Cell-specific gene expression is controlled by epigenetic modifications and transcription factor binding. To understand the dynamics of histone modifications and TF binding and their effects on cell-specific gene regulation it is necessary to quantitatively compare different ChIP-Seq samples This is a surprisingly difficult task as the statistical assessment of differences is hindered by a number of factors: on the one hand, the data is digital, consisting of counts of DNA fragments (reads) mapped onto regions of the genome. In most studies, only a very small number of replicate experiments are performed, making statistical testing an intrinsically difficult task To compound both of these problems, ChIP-Seq produces spatially distributed patterns of binding or histone modifications localised to specific regions of the genome (peaks); this feature, in particular, renders standard differential testing methods unsuited for the comparison of ChIP-Seq data sets
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