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This article focuses on "how to use HOMER for peak calling". Interested friends may wish to have a look at it. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how to use HOMER for peak calling.

HOMER is a software for motif prediction, in addition, the software also integrates many other functions, which can be used to analyze data such as chip_seq,RNA_seq,Hi-C. This article mainly introduces how to do peak calling through HOMER.

In HOMER, peak calling is carried out through the command findPeaks, which has the following modes, corresponding to the identification of different types of peak

Factor

This model is used to identify DNA and protein binding sites, mainly to identify transcription factor binding sites, and the predicted length of peak is a fixed value.

Histone

This pattern is used to identify areas where histone modification occurs, and the peak length recognized by this pattern is not exactly the same and is a variable value.

Super

This pattern is used to identify super enhancers.

Groseq

This model is used to analyze chain-specific GRO_seq data.

Tss

This pattern is used to analyze 5'RNA_seq/CAGE/5'GRO_seq in order to identify promoter/TSS regions.

Dnase

This model is used to analyze DNase_seq data in order to identify DNase enzyme hypersensitive sites.

MC

This pattern is used to identify DNA methylation regions.

For the peak calling of chip_seq, the commonly used modes are factor, histone and super. The specific usage is as follows, which is divided into two steps.

1. MakeTagDirectory

After comparing the genome to get the bam file, first use the command makeTagDirectory to generate a folder, using the following usage

MakeTagDirectory out_dir align.bam

The output directory file is as follows

├── chr1.tags.tsv

├── chr2.tags.tsv

├── chr3.tags.tsv

...

├── chrY.tags.tsv

├── tagAutocorrelation.txt

├── tagCountDistribution.txt

├── tagInfo.txt

└── tagLengthDistribution.txt

By default, the alignment of each chromosome is stored in a tags.tsv file, in addition to several files that start with tag and contain some simple statistics.

TagCountDistribution.txt contains the distribution information of the sequencing depth, the first is the value of the sequencing depth, and the second is the proportion of the corresponding reads. Based on the first 10 lines of this file, the visualization in R is as follows

For chip samples, the higher the proportion of unique mapping reads, the better, so we can see that the proportion of sequencing depth 1 is the highest.

TagLengthDistribution.txt contains the length distribution information of reads, the first column is the length, the second column is the proportion of the corresponding reads, which is visualized in R as follows

You can have an intuitive understanding of the length distribution of the inserted clip.

TagAutocorrelation.txt is used to evaluate the correlation of sequencing depth distribution on the positive and negative chain of sequencing data. Visualization in R is as follows

The distance between positive and negative peaks is the length of insertion skewness.

2. FindPeaks

After establishing tagdirectory for input and IP samples respectively, you can peak calling. The usage is as follows

FindPeaks ip_tagdir/-I input_tagdir-style histone-o homer.peak.txt

The output is similar to that of macs2 and is divided into two parts, with a comment line starting with # at the beginning and end of the file, and some of the information is as follows

The line corresponding to peak is as follows

At this point, I believe you have a deeper understanding of "how to use HOMER for peak calling". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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