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This article mainly introduces "how to use python Genome Tracks to visualize hi-c data". In daily operation, I believe many people have doubts about how to use python Genome Tracks to visualize hi-c data. Xiaobian consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful for you to answer the doubts of "how to use python Genome Tracks to visualize hi-c data". Next, please follow the editor to study!

Visualization is a very important part of data analysis. For the visualization of NGS analysis data, various genome browsers are most commonly used, such as UCSC, GBrowse and other web-based genome browsers, as well as localized graphical interface software such as igvtools. For Hi-C data, the web-based WashU Epigenome Browser genome browser and localized juicebox software were also introduced in the previous article.

Proficient in the use of one of the software can meet most of the needs, but as a geek of letter analysis, I always feel that I still need a command-line tool to improve efficiency. Both python and R have very powerful visualization capabilities. Today, we introduce a software based on python, pyGenomeTracks, an authentic command line tool, which has the same presentation form as Genome browser.

Https://github.com/deeptools/pyGenomeTracks

The software supports visualization of the following information

Bigwig

Bed

Bedgraph

Links

Hi-C matrices

The visual effect of the software is as follows

It is presented in the same way as the genome browser, with each layer as a track. The software uses the form of a configuration file to configure the file information that needs to be displayed. Each file to be displayed and the corresponding parameters are written under a tag, as follows.

1. Bigwig

2. Bedgraph

3. Hic

After that, there are tags such as x-axis and spacer, which correspond to the x-axis and the space between the two tracks, respectively. The following is below

[spacer]

[x-axis]

Where = top

Once you have edited the configuration file, you can run it, using the following

PyGenomeTracks\

-- tracks tracks.ini\

-region chr2:10000000-11000000\

-- outFileName output.pdf

The tracks parameter specifies the name of the configuration file, the region parameter specifies the region of the genome to be visualized, and the outFileName parameter specifies the name of the output file. In order to achieve an aesthetic effect, there are many parameters that need to be adjusted. Please refer to the official documentation and examples for more details.

The effect of an hi-c data visualization is as follows

Through this software, hi-c data can be displayed efficiently.

At this point, the study on "how to use python Genome Tracks to visualize hi-c data" 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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