Nucleosome distribution around promoters

Chip-seq data analysis: from quality check to motif discovery and more

Lausanne, 27 March - 31 March 2017

Data reproduction exercise: Nucleosome distribution around promoters.

Author: Rene Dreos

Introduction

This exercise is based on the following paper:

et al.

The autors analysed varous histone modification and variants distributions around promoters stratified by their initiation pattern.
The metilated histones and the variant H2A.Z were initially published in:

et al.

17512414

Histone H3 was from:

et al.

18329373

and acetylated histones from:

et al.

18552846

Exercise

We wil try to reprocude results shown in Figure 1a,b; Figure 2 and FIgure 3 in (Nozaki et al. 2011).

Figure 1:

Figure 2:

Figure 3:

Have a look at the Figure legends and the Methods section of the corresponding paper. The autors classified promoters based on their initiation pattern into two classes: broad and peak. They then studied the chromatin organisation around the two promoter classes. Figure 1A,B and Figure 2 show similar things: the distribution around the two promoter classes of several histone marks. Figure 3 instead shows the ratio (look in the paper for the exact definition) of the distribution of the two classes for each histone mark.
Once you have found the results you can check if the conclusion reported in the paper holds:

"around broad promoters histones were highly distributed and aligned in a orerly fashon"

Hints and recipes

Note that the data used in this paper is present on the ChIP-Seq server, aligned on hg18 genome assembly.
To reproduce the figures you should:

Instead of classify promoters based on their initiation pattern, classify them based on the presence absence of the TATA-box in the expected location. It has been reported before that TATA-box promoters have a peak initiation pattern whereas non-TATA-box promoters have a broad initiation pattern. To do so you have to use FindM
- Extract TATA-box promoters from EPDnew databse 003 for genome assembly hg18.
- Select a Sequence Range from -33 to -15
- Select "Search mode" as forward and "Sequence Selection mode" as "sequence with motifs".
- Select the TATA-box matrix from "Promoter Motifs"
- Run the job and save the SGA file as "peakPromoters.sga". You shoul get 1922 promoters
- Run again the analysis with the same parameters except "Sequence Selection mode" selectin this time "sequence lacking motif". Run the job, save the SGA file as "broadPromoters.sga". You shoul have 21425 promoters
Now optimise ChIP-seq parameters for each sample you want to correlate to the promoters. You can find these samples in the ChIP-seq server under hg18 genome assembly, ChIP-seq data type and Barski, Schones and Wang series.
- Find centering parameter for each nucleosome sample. Use ChIP-Cor for this task. It can be found looking at the correlation plot between plus and minus strand tags.
You ca check if TATA-box promoters have a peak initiation pattern looking at the CAGE distribution (you can use the DBTSS7 dataset present for hg18 assembly) around them and compare to the non-TATA-box promoters.
Find the histone distribution around promoters. Use ChIP-Cor for this task. Note that promoters are oriented features. Use the centering parameter found in the previous step for centering target feature.
Save the TEXT file with numerical values of the distribution of the histone marks / variants around promoters and, using R, combine them in plots similar to the published figures.