Shulou(Shulou.com)06/01 Report--

In this issue, Xiaobian will bring you about how to use the highest resolution CNV Detection Tools in DECoN. The article is rich in content and analyzes and narrates from a professional perspective. After reading this article, I hope you can gain something.

DECoN is a CNV Detection Tools for exon-based panel sequencing that identifies single exon CNVs

Panel sequencing is widely used in clinical practice. At present, it is mature to detect SNP by panel sequencing data, while CNV detection lacks effective tools. In this context, DECoN came into being, developers in ExomeDepth software on the basis of further modifications, mainly the following two major changes

Added detection of mutations in the first exon region on the chromosome

Added distance between exons to HMM

The performance of the software was evaluated by simulated and real data, and DECoN was surprisingly effective in the simulated dataset, with 100% sensitivity and 99% specificity. The real data were sequenced by Illumina TruSight Cancer Panel, and 24 exon CNVs were finally identified. MLPA technology was used to verify that 23 exon CNVs could be detected, and the false positive rate was 4%. For more detailed evaluation results, please see the description in the article.

The software is also very fast and provides good visualization of results, as shown below

The top line graph shows the distribution of coverage across genes, gray represents the control sample, blue represents the experimental sample; the middle shows the name of the gene; the bottom scatter plot represents the ratio between the observed value and the expected value; the gray area represents the 95% confidence interval; when the ratio deviates significantly from the confidence interval, it is considered that there is copy number variation in the area. The red dot area shown above represents the actual observed value being less than the expected value, indicating that a deletion has occurred.

The source code of the software is stored on github, and the link is as follows

https://github.com/RahmanTeam/DECoN

The specific operation is divided into the following 4 steps, corresponding to 4 R scripts.

1. ReadInBams.R

Read bam file, calculate coverage, usage is as follows

Rscript ReadInBams.R \

--bams bamList.txt \

--bed Target_Regions.bed \

--fasta hg19.fa \

--out DECoNtest

The input file is a list of bam files, a bed file for the target region, a fasta file for the reference genome, and a bam file format as follows

The format of the destination zone bed file is as follows

The output is a file with the suffix RData that stores the coverage information of the sample, which is represented by FPKM values in the software.

2. IdentifyFailures.R

Perform quality control to detect exon areas with excessive coverage, samples with poor correlation, etc., as follows

Rscript IdentifyFailures.R \

--Rdata DECoNtest.RData \

--exons customNumbering.txt \

--mincorr .98 \

--mincov 100 \

--custom TRUE \

--out DECoNtest

The input file is the RData file generated in the first step. In addition, a custom exon number file is required.

customNumbering.txt

The content is as follows

If all samples and exon areas meet the requirements, the command will not output the result. If there are unqualified samples and areas, the operation needs to be carried out after elimination.

3. makeCNVcalls.R

CNV calling is performed as follows

Rscript makeCNVcalls.R \

--Rdata DECoNtest.RData \

--exons customNumbering.txt \

--custom TRUE \

--out DECoNtestCalls \

--plot All \

--plotFolder DECoNTestPlots4. runShiny.R

Build a browser-based interactive results presentation page with R package Shiny, using the following

Rscript runShiny.R \

--Rdata DECoNtestCalls.RData

You can view the coverage distribution map, cnv calling results and other information, as shown below

For CNV detection by panel sequencing, DECoN is recommended for analysis.

The above is how to use the CNV Detection Tools with the highest resolution in DECoN shared by everyone. If you happen to have similar doubts, you may wish to 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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