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bioinformatics and randomness

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Comparison of ChIP-seq data

In my last post, we looked at how normalization can be used for analyzing high throughput sequencing data. I am interested now in how to identify differences across conditions.

To compare the ChIP-seq data from different experiments, we looked at the yeast data from [3]. To start, the reads overlapping each genome position are binned into windows (spacing=10) using wiggle files in MACS [1]. Then MACS identifies peaks in the data, identifying potential transcription factor binding sites, and we found overlapping and non-overlapping (unique) peaks using bedtools [2].

When we compared biological replicate experiments of S96 yeast strain, we see that most of the differences (unique peaks) are near y=x showing that they are not significantly different from each other (Figure 1)

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Figure 1. Comparison of S96 replicates shows high correlation (rho~0.9) and the peaks that are marked unique are clustered around the line of equality. The log2 plot does not show large differences either.

We can also use this same technique to show how the two different strains compare by using the same procedure. In this case, we compared S96 and HS959 strains and found noticeable differences in the peaks that are called unique. The raw data shows large differences between the peaks that are called unique in each dataset. Looking at the log-transform shows that most of these positions have fold-change differences greater than 2.

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Figure 2. Comparison of the S96 and HS959 genomes. (Left) The raw read scores shows that the unique peaks have significantly different groupings between each strain (Right) Log-transform shows large fold-change differences exist amongst the peaks called unique

 

The comparison of the raw read scores is convincing that some of the peaks are differentiated but it is also clear from comparing some experiments that the read depth is not equal which results in the “banana shape” (Figure 2). We explored several options for normalizing the data including background subtraction, scaling, background subtraction and scaling, and normalized difference (NormDiff) [3]. I will include these in a new post…

To process the data, plyr (R package) was used to fit a neat design pattern called split-apply-combine. In this case I split data across factors (chromosome) and apply functions (selections), and then results are combine automatically back into one table. This cut some of the fat off of my old overweight code!

[1] Zhang et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol (2008) vol. 9 (9) pp. R137

[2] Quinlan AR and Hall IM, 2010. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 26, 6, pp. 841–842.

[3] Zheng et al. 2010 Genetic analysis of variation in transcription factor binding in yeast

additionally R packages plyr, stringr, and RColorBrewer were used to analyze this data