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Today, I will talk to you about how to carry out the actual combat of MACS2 peak calling. Many people may not know much about it. In order to let everyone know more, Xiaobian summarized the following contents for everyone. I hope everyone can gain something according to this article.

MACS is one of the most popular peak calling software, originally designed for chip data on transcription factors, and in the latest version, it also adds adaptations for histone modifications. The latest version is v2.0, the official website is as follows

https://github.com/taoliu/MACS

The following subcommands are available in version 2.0

callpeak

bdgpeakcall

bdgbroadcall

bdgcmp

bdgopt

cmbreps

bdgdiff

filterdup

predictd

pileup

randsample

refinepeak

Each subcommand and corresponding function are described as follows

The following is a brief introduction to macs2's most classic use scenario peak calling, the basic usage is as follows

macs2 callpeak \

-t ip.bam \

-c input.bam \

--outdir out_dir \

-n chip \

-g hs

The-t parameter specifies the sample to be processed by the antibody, and-c specifies the input sample. It is worth mentioning that macs supports multiple input file formats, in addition to the bam format used in the above code, SAM/BED format is also supported.

--outdir specifies the directory of the output result, -n parameter specifies the prefix of the output file name, -g parameter specifies the effective size of the genome, in the NGS data, the coverage of sequencing reads on the genome is not 100%, and the alignment information of some repeated regions is unreliable, the remaining regions that can be utilized usually only account for 70% to 90% of the entire genome region, and the size of this region is the effective size. For common species, the program has a built-in effective size. All we need to do is specify the acronym for the species.

For other species, you need to specify the size of the valid genome yourself, in bp.

The output file is as follows

chip_model.r

chip_peaks.narrowPeak

chip_peaks.xls

chip_summits.bed

model.r is an executable R script that generates a PDF output file using the following code

Rscript chip_model.r

The first page shows the distribution of positive and negative strand sequencing in peak neighborhood interval, which is used to evaluate the parameter value of d, as follows

The second page is the result of cross-correlation analysis, which is illustrated as follows

The file with suffix xls is the output result of peak, and the content is as follows

#The first line is comment information, showing the specific command and parameter settings of the software call for easy verification; the other lines record the interval information of peak, where the starting position is counted from 1.

The file with the suffix narrowpeak is a BED file with the following contents

The first four columns represent the peak interval and name. Note that the starting position in bed format starts counting from 0. The value in the fifth column is int(-10 $> log10qvalue). The sixth column is all., The seventh column is fold_enrichment, the eighth column is-log10 (pvalue), the ninth column is-log10 (qvalue), and the tenth column is the center of peak, i.e. the distance between summit and the start position of peak, corresponding to abs_summit - start.

The file with the suffix bed is the peak center, i.e. the bed file corresponding to summit. The content is as follows

The last column is-log10 (qvalue).

After reading the above content, do you have any further understanding of how to carry out MACS2 peak calling? If you still want to know more knowledge or related content, please pay attention to the industry information channel, thank you for your support.

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