Shulou(Shulou.com)06/02 Report--

In this issue, Xiaobian will bring you about the principle of CNVnator. The article is rich in content and analyzes and narrates it from a professional perspective. After reading this article, I hope you can gain something.

1. alignment reference genome

To calculate the sequencing depth, we first need to align the sequenced reads to the reference genome. One of the most critical steps is how to align the reads of multiple regions of the genome. When a read is aligned to multiple locations on the genome, it is completely impossible to distinguish which region it belongs to from the perspective of data analysis, because the homology of these regions is very high. For such reads, there are two processing strategies, the first is to directly eliminate, keep the unque-mapping reads; the second is to randomly select one of the positions as the true alignment position of the reads, cnvnator algorithm is the second strategy.

2. Build RD signal

After alignment, the genome can be divided into windows of equal length, and the sequencing depth within each window can be calculated. What needs to be noted here is that the original sequencing depth can be corrected by using gc content. PCR amplification of different GC content sequences has bias, so RD signal in the calculation window needs to correct this systematic error, cnvnator correction formula is as follows

global represents the average value of the original RD signal in all bin windows, gc represents the average value of the original RD signal in all bin windows with the same GC content as the current bin, and the ratio of the two is used as a coefficient to correct the original RD signal.

3. Mean-shift clustering

Mean-shift is a clustering algorithm that uses the corrected RD signal value to cluster adjacent bins. In theory, bins clustered into one class have the same cnv copy number. The diagram is shown below.

It should be noted that only bins with close chromosome positions are clustered here, which is not equivalent to segmentation in CNV analysis.

4. segmentation

The above clustering signal only has significance in part of chromosomes. When it is placed in the whole genome to identify CNV, it must be realized by segmentation algorithm. cnvnator adopts its own unique algorithm. There is a key parameter called bandwidth. Different values will affect the division of CNV regions. The figure is as follows.

The larger the value, the CNV signal of small fragments will be masked, and the smaller the value, the smaller the value, the higher the false positive rate of CNV detection.

5. signal merging

According to the difference with the adjacent segment RD signal, the original divided segments are merged.

6. cnv calling

For different segments, predict the corresponding copy number.

When CNVinator software is used for analysis, the choice of bin and bandwidth parameters has a great influence on the results. Through this software can detect various lengths of cnv, and the accuracy of typing is very high, is a recommended cnv detection software.

The above is what the principle of CNVnator shared by Xiaobian is. If there is a similar doubt, please refer to the above analysis for understanding. If you want to know more about it, please pay attention to the industry information channel.

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