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

CNVnator is a CNV testing software based on Read-Depth analysis strategy to predict CNV by analyzing genome-wide sequencing data. The source code is saved on github at the following URL

Https://github.com/abyzovlab/CNVnator

The installation of this software is quite complicated. I use the docker image installed by others directly here, which is also the convenience of docker. Download the cnvnator image installed by others directly from the source. The code is as follows.

Docker pull diploid/cnvnator

The specific analysis steps are as follows

1. EXTRACTING READ MAPPING FROM BAM/SAM FILES

CNVnator relies on the ROOT software package, which is specially developed for big data's processing, and provides a series of functions such as statistical analysis, visualization, data storage and so on. Based on this system, the running speed can be greatly accelerated, so the original information is stored in the root file to facilitate analysis.

This step stores the information of the alignment in a file with the suffix root, as follows

Cnvnator-root file.root-tree chr1.bam-chrom 1

The-tree parameter specifies the name of the input bam file, the-root file specifies the name of the output root file,-chrom specifies the chromosomes to be analyzed, and all chromosomes are analyzed by default. Here I am testing and only analyze chromosome 1.

2. GENERATING A READ DEPTH HISTOGRAM

This step is to divide the genome into fixed-size windows and count the read depth in each window. The code is as follows

Cnvnator-root file.root-his 1000-chrom 1-d genome_fa_dir

The-his parameter specifies the size of the window in bp, and the-d parameter specifies the directory where the reference genome fasta file is located.

3. CALCULATING STATISTICS

This step is required, and the code is as follows

Cnvnator-root file.root-stat 1000

-stat specifies the size of the window, which is the same as the-his parameter in step 2.

4. Partition

Segmentation is performed in this step, and the code is as follows

Cnvnator-root file.root-partition 1000

-partition specifies the size of the window, which is the same as the-his parameter in step 2.

5. Call CNVs

This step analyzes the CNV of each segment region, as follows

Cnvnator-root file.root-call 1000 > cnv.call.txt

-call specifies the size of the window, which is the same as the value of the-his parameter in step 2. The output is printed to the screen by default, and redirected to the file here.

The contents of the output file are as follows

The first is the type of CNV, including deletion and duplication, the second is the chromosome region of CNV, the third is the length of CNV, the fourth is the normalized read depth, normalized to the range of 0-1, the fifth to the eighth are different values, and the last one is Q0. The corresponding explanations for the last five columns are as follows.

The output of the original cnv call can also be converted to VCF format through the script that comes with the software, as follows

Cnvnator2VCF.pl cnv.call.txt > cnv.vcf

CNVnator is powerful and fast. For more information on usage, please refer to the official documentation.

At this point, the study on "how to use CNVnator for CNV detection" 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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