Histone Chip-Seq

ChIP-Seq experiments are increasingly used to investigate histone modifications. In contrast to transcription factors, histone marks are of variable length and can span across entire gene bodies. Although the experimental procedures are similar, the resulting data needs to be treated accordingly to take this variability into account. While narrow peaks resulting from Transcription Factor ChIP-Seq can be detected using a fixed window size, broad peak detection has to cope with the additional boundary-problem in the sense that the distance between start and end depends on the regions of the underlying genes.

Some existing approaches [Heinz et al., 2010] first detect narrow peaks using a fixed window size, and then merge close peaks in order to avoid the computational cost of finding regions of variable length. Nevertheless, different histone marks can also exhibit distinct shapes across gene bodies [Li et al., 2007], which renders them amenable to a shape-based detection algorithms.

By using existing annotations, the Histone ChIP-Seq tool is able to classify gene regions according to the peak shape and thereby provides a good practical trade-off between computational complexity and biological sensitivity. The primary application areas are the analysis of ChIP-Seq data for diverse histone-modifications such as (mono-, di-, and tri-) methylation, acetylation, ubiquitination, etc., in combination with a set of annotated gene regions. The tool is well suited to analyze data from organisms with available gene annotations, while finding peaks in intergenic regions can be accomplished with the Transcription Factor ChIP-Seq tool.

To run the Histone ChIP-Seq tool:

Toolbox | Epigenomics Analysis (Image epigenomics) | Histone ChIP-Seq (Image broad_peak_detection_16_n_p)

In the first wizard window, select the mapped ChIP-Seq reads as input data (figure 36.1). Multiple inputs (such as replicate experiments) are accepted, provided that they refer to the same genome. It is also possible to work in batch (see Batch processing).

Image histone-chipseq-step1
Figure 36.1: Selecting input tracks for the Histone ChIP-Seq tool.

In the second step (figure 36.2), the gene track and control data are defined, along with the p-value. This value defines which regions have a significant fit with the peak-shape, and only these are copied to the output track.

Image histone-chipseq-step2
Figure 36.2: Setting peak shape parameters.

The output options are shown in figure 36.3.

Image histone-chipseq-step3
Figure 36.3: Setting up result handling.