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What this article shares with you is about how PennCNV uses SNP chip to detect CNV. The editor thinks it is very practical, so I share it with you to learn. I hope you can get something after reading this article.

To detect CNV through SNP chip, there are many corresponding analysis software, PennCNV is one of them, this software uses hidden Markov model to detect CNV, the official website is as follows

Http://penncnv.openbioinformatics.org/en/latest/

The pipeline for data analysis is as follows

First of all, we need to prepare signal intensity file, which is used to record the two statistics of LRR and BAF for each locus. There are two formats. The first format is as follows.

\ t separated 6 columns, the first column is the name of SNP, the second column is the name of the chromosome where the snp locus is located, the third column is the chromosome location where the snp locus is located, the fourth column is the typing result of the locus, the fifth column is the statistical value of LRR, the sixth column is the statistical value of BAF. For cnv calling, the most important are the values of the three columns Name, LRR and BAF, so the file can also have a second format, as shown below

Secondly, we need the HMM and PFB files corresponding to the chip platform, and some of these two files are included in the installation directory of the software. PFB is the abbreviation of population frequency of B allel, which is essentially the MAF of each SNP locus. It also provides annotation information about the location of chromosomes, as shown below.

For the non-SNP probe integrated on the SNP chip, the PFB value is 2. It is important to note that only the sites recorded in this file will be used for CNV calling analysis, and when we need to filter sites, we only need to filter in this file. The HMM file is included in the software and provides the transfer probability between different copy numbers. Once the input file is ready, you can analyze it. The common analysis steps are as follows

1. CNV calling

For independent samples, the command to detect CNV is as follows

Detect_cnv.pl\

-test\

-hmm lib/hh650.hmm\

-pfb lib/hh650.hg18.pfb\

Sample.txt\

-log sample.log\

-out sample.rawcnv

The output is as follows

The first is the chromosome region of CNV, the second is the number of SNP sites contained in the CNV region, the third is the length of the CNV region, and the cn in the fourth column represents the copy number of the CNV region, followed by the input file corresponding to the sample, the starting and ending snp name, and scoring values.

two。 Filter CNV

Common screening strategies are as follows

Filter according to statistical indicators such as length and the number of sites included

Filter out CNV located in special regions, such as centromeres, telomeres, etc., according to chromosome regions.

The command to filter by length is as follows

Filter_cnv.pl\

-numsnp 10\

-length 50k\

Sampleall.rawcnv

Filter out CNV regions that contain less than 10 snp bits and whose length is less than 50kb.

3. Notes on CNV

The following comments are usually made to the CNV area

Overlap gene

The nearest genes upstream and downstream

Functional components of overlap

The command is as follows

Scan_region.pl\

Sampleall.rawcnv\

Hg19_refGene.txt\

-refgene\

-reflink hg19_refLink.txt\

> sampleall.cnv.hg194. Visualization of CNV

To visualize the LRR and BAF values of SNP sites in the CNV area, the command is as follows

Visualize_cnv.pl\

-format plot\

-signal sample.txt\

Sampleall.rawcnv

The results are as follows

The software also has many advanced uses, such as CNV detection of family samples, association analysis of case/control and so on.

This is how PennCNV uses SNP chips to detect CNV. The editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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