<ol>
<li>For sharing, you can test the proportion of overlapping peaks using <code>bedtools intersect</code> or the <code>find.overlap</code> function in <code>GenomicRanges</code> packages in R.</li>
<li>What makes more sense is perhaps to merge all the peaks together using <code>bedtools merge</code>, and then count the number of reads/fragments in each peak for each sample using <code>featureCounts</code>. The output of this is a matrix, which is very similar to gene expression results. Then you can do some differential tests on this data.</li>
</ol>

CC BY-SA 4.0
2020-06-16T21:30:14.707Z
2020-06-16T21:30:14.707Z
Phoenix Mu
<p><a href="https://bioinformatics.stackexchange.com/questions/13108/how-to-make-venn-diagram-for-the-macs-peak-calling-output-of-two-data-sets/13407#13407">In a similar question</a>, I recommended that directly comparing called peaks can be somewhat misleading. This topic is addressed in the documentation of the <a href="https://bioconductor.org/packages/release/bioc/html/DiffBind.html" rel="nofollow noreferrer"><code>DiffBind</code> R package on Bioconductor</a>, in the &quot;Comparison of occupancy and affinity based analyses&quot; section.</p>
<p>But if you're just looking for genome annotation software, the <a href="https://bioconductor.org/packages/release/bioc/html/annotatr.html" rel="nofollow noreferrer"><code>annotatr</code></a> package on Bioconductor is pretty useful</p>

CC BY-SA 4.0
2020-06-22T19:41:30.067Z
2020-06-22T19:41:30.067Z
James Hawley
<p>It depends on how many samples you have.
If you only compare one sample to the second, you can pool and call peaks and then directly calculate RPKM ratios for each peak.
It you have more samples, you may want to use IDR to score differential peaks.
You may also use packages such as Fseq or HOMER to treat one group as ChIP experiment while the other group as control to call peaks.
Alternatively, you could pool all the samples to call peaks, then calculate read counts in each peak for each sample, and then use DESeq2 or other differential tools to calculate significantly different peaks’ significance and effect size.</p>

CC BY-SA 4.0
2021-03-20T00:28:52.787Z
2021-03-20T00:28:52.787Z
Code42
<p>I have several sets of chip-seq data. I called the peaks using Macs2. I am pretty new to the field and I will appreciate any help. I wanted to annotate the peaks and see which peaks are shared between them and which ones are different. What is the best way to do this? which package should I use?</p>

<p>Many thanks</p>

<sequence-annotation><sequence-analysis><chip-seq><peak-calling><macs2>
differential analysis of chip-seq data
# differential analysis of chip-seq data

(Asked by: username_0 on 2020-06-15)

I have several sets of chip-seq data. I called the peaks using Macs2. I am pretty new to the field and I will appreciate any help. I wanted to annotate the peaks and see which peaks are shared between them and which ones are different. What is the best way to do this? which package should I use?

Many thanks

**Tags:** sequence-annotation, sequence-analysis, chip-seq, peak-calling, macs2

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## 3 answers

### Answer by username_1 on 2020-06-16. Score: 1

1. For sharing, you can test the proportion of overlapping peaks using `bedtools intersect` or the `find.overlap` function in `GenomicRanges` packages in R.
2. What makes more sense is perhaps to merge all the peaks together using `bedtools merge`, and then count the number of reads/fragments in each peak for each sample using `featureCounts`. The output of this is a matrix, which is very similar to gene expression results. Then you can do some differential tests on this data.


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### Answer by username_2 on 2020-06-22. Score: 0

[In a similar question](https://bioinformatics.stackexchange.com/questions/13108/how-to-make-venn-diagram-for-the-macs-peak-calling-output-of-two-data-sets/13407#13407), I recommended that directly comparing called peaks can be somewhat misleading. This topic is addressed in the documentation of the [`DiffBind` R package on Bioconductor](https://bioconductor.org/packages/release/bioc/html/DiffBind.html), in the "Comparison of occupancy and affinity based analyses" section.

But if you're just looking for genome annotation software, the [`annotatr`](https://bioconductor.org/packages/release/bioc/html/annotatr.html) package on Bioconductor is pretty useful


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### Answer by username_3 on 2021-03-20. Score: 0

It depends on how many samples you have.
If you only compare one sample to the second, you can pool and call peaks and then directly calculate RPKM ratios for each peak.
It you have more samples, you may want to use IDR to score differential peaks.
You may also use packages such as Fseq or HOMER to treat one group as ChIP experiment while the other group as control to call peaks.
Alternatively, you could pool all the samples to call peaks, then calculate read counts in each peak for each sample, and then use DESeq2 or other differential tools to calculate significantly different peaks’ significance and effect size.


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