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This article mainly introduces "how to use deeptools to view reads distribution characteristics". In daily operation, I believe many people have doubts about how to use deeptools to view reads distribution characteristics. The editor consulted all kinds of data and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts about "how to use deeptools to view reads distribution characteristics". Next, please follow the editor to study!

In the analysis of chip_seq data, we usually explore the distribution of peak region on the genome to see if there is a rule in its distribution, such as whether there is enrichment near the transcriptional start site or the transcriptional termination site. We can use deeptools as a tool to achieve this.

First of all, through the computeMatrix command, you can calculate the distribution on the genome region, which can be divided into the following two modes

Scale-regions

Reference-point

The first mode represents an interval, including the start and end positions, and the second mode represents a certain site, such as the transcriptional initiation site. The official website gives a good explanation for the difference between the two modes, as shown below

All regions are divided into equal intervals called bin, and then the sequencing depth of all sites in each bin is calculated, which is represented by the average sequencing depth of all sites by default. After the intermediate result file is calculated by this command, you can use the following two commands for visualization

PlotProfile

PlotHeatmap

Here is a practical example, starting with the bam file, to get the final visualization result

1. Convert bam files to bigwig files

With the bamCoverage command, you can convert a bam file to a bigwig file, using the following

BamCoverage-b input.bam-o input.bw2. Run computeMatrix

This command has two modes, scale-regions and reference-points, and the second is shown here as an example, using the following

ComputeMatrix reference-points\

-S inpnut.bw\

-R hg19.bed\

-- binSize 10\

-- skipZeros\

-a 3000\

-b 3000\

-o matrix.gz\

-- outFileNameMatrix matrix.tab

In the output tab file, each line represents a transcript, which is the same as the number of transcripts in the input bed file, each column represents the average sequencing depth in the bin interval, and the number of columns is related to the length of the interval and bin_sizz. In the above example, select the upstream and downstream 3kb intervals, the bin size is 10bp, so there is a total of 3000X2/10, that is, 600 intervals.

In visualization, the more the number of intervals, the line chart drawn will be relatively smooth, so you can properly adjust the size of the bin to make the drawing more beautiful.

3. PlotProfile

The usage is as follows

PlotProfile-m matrix.gz\

-out profile.pdf

The resulting results are as follows

4. PlotHeatmap

The usage is as follows

PlotHeatmap-m matrix.gz-o heatmap.pdf

The result is divided into two parts, the first part is the same as the result of plotProfile, and the second part is a heat map, which is shown below.

Is to generate the contents of the tab file to draw a heat map, the above shows the basic usage, in addition, there are a lot of parameters can be adjusted to draw a more beautiful picture.

At this point, the study on "how to use deeptools to view reads distribution characteristics" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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