变异识别-软件varscan2

罗大黑学生信 2021-07-09

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简介

安装

call 变异

结果过滤

简介

VarScan采用了一种稳健的启发式/统计方法来调用满足读取深度、基本质量、变异等位基因频率和统计显著性所需阈值的变量。

It can be used to detect different types of variation:

Germline variants (SNPs an dindels) in individual samples or pools of samples.
Multi-sample variants (shared or private) in multi-sample datasets (with mpileup).
Somatic mutations, LOH events, and germline variants in tumor-normal pairs.
Somatic copy number alterations (CNAs) in tumor-normal exome data.

官网：http://varscan.sourceforge.net/germline-calling.html

该篇幅阐述尝试用VarScan call Somatic mutations(SNPs an dindels) in individual samples .可行待确定？

安装

直接下载下载jar文件即可使用，地址：https://sourceforge.net/projects/varscan/files/

call 变异

input

VarScan2 的输入文件是由samtools mpileup生成mpileup文件。samtools mpileup其他参数可以根据需求调整，一般默认就好。

step 1 ：Generate a pileup file 
# samtools mpileup -B -f [reference sequence] [BAM file] >myData.pileup
samtools mpileup -B -f Homo_sapiens_assembly19.fasta  S2000332-L1.NRAS.C-T.gencore.sort.bam > S2000332-L1.NRAS.C-T.gencore.sort.pileup

Methods

VarScan2 提供三个命令进行call 变异，包括：mpileup2snp/pileup2snp（单独call snp）,mpileup2indel/pileup2indel (单独call indel) 和mpileup2cns/pileup2cns（同时call snp indel）。执行的命令如下：

java -jar VarScan.v2.4.4.jar mpileup2cns -h
Warning: No p-value threshold provided, so p-values will not be calculated
Min coverage:  8
Min reads2:  2
Min var freq:  0.2
Min avg qual:  15
P-value thresh:  0.01
USAGE: java -jar VarScan.jar mpileup2cns [pileup file] OPTIONS
  mpileup file - The SAMtools mpileup file


  OPTIONS:
  --min-coverage  Minimum read depth at a position to make a call [8]
  --min-reads2  Minimum supporting reads at a position to call variants [2]
  --min-avg-qual  Minimum base quality at a position to count a read [15]
  --min-var-freq  Minimum variant allele frequency threshold [0.01]
  --min-freq-for-hom  Minimum frequency to call homozygote [0.75]
  --p-value  Default p-value threshold for calling variants [99e-02]
  --strand-filter  Ignore variants with >90% support on one strand [1]
  --output-vcf  If set to 1, outputs in VCF format
  --vcf-sample-list  For VCF output, a list of sample names in order, one per line
  --variants  Report only variant (SNP/indel) positions [0]


step 2 : call CNS
    java -jar VarScan.v2.4.4.jar mpileup2cns \
    S2000332-L1.NRAS.C-T.gencore.sort.pileup \   
    --min-var-freq 0.01  \
    --p-value 0.01 \
    --output-vcf 1 \
    --variants > S2000332-L1.NRAS.C-T.snv.vcf

结果过滤

从上所述及官网介绍，varscan2 call 变异的命令是比较简单的，只有包括2步骤：1，将bam 文件转换成pipeup 格式；2，将pipeup 格式作为varscan2 的输入进行变异检测，参数的设置的可以按需求修改，一般默认。

虽然call 变异过程比较简单，但call完VCF文件的大小的会比较大，突变内容很多。而过滤将会成为尤为重要的部分。如何在众多数据筛选出可靠的阳性突变？(在此之前了解vcf 每个字段的意思，vcf 可以在info format 列中获得基因型深度(genotype depth)，位点质量得分，次等位基因频率和基因型总深度(genotype call depth))

1. 根据panel 确实筛选的变异范围

bedtools  intersect -a NRAS.C-T.bed -b S2000332-L1.NRAS.C-T.cns.vcf -wb

2. 通用过滤，即根据

a. 突变点最小平均覆盖深度

b. 支持alt 最小覆盖深度

c. 链平衡（single-strands or bi-strands）

d. 支持alt 碱基的平均质量

e. VAF值，突变频率

Min coverage:  10
Min reads2:  2
Min strands2:  1
Min var freq:  0.2
Min avg qual:  15
P-value thresh:  0.1


USAGE: java -jar VarScan.jar filter [variant file] OPTIONS
  variant file - A file of SNPs or indels


  OPTIONS:
  --min-coverage  Minimum read depth at a position to make a call [10]
  --min-reads2  Minimum supporting reads at a position to call variants [2]
  --min-strands2  Minimum # of strands on which variant observed (1 or 2) [1]
  --min-avg-qual  Minimum average base quality for variant-supporting reads [20]
  --min-var-freq  Minimum variant allele frequency threshold [0.20]
  --p-value  Default p-value threshold for calling variants [1e-01]
  --indel-file  File of indels for filtering nearby SNPs
  --output-file  File to contain variants passing filters

参考：

SNP Filtering Tutorial http://www.ddocent.com/filtering/

https://blog.csdn.net/u012110870/article/details/102804559

https://blog.csdn.net/u012110870/article/details/102804561?utm_medium=distribute.pc_relevant.none-task-blog-baidujs_title-0&spm=1001.2101.3001.4242

https://blog.csdn.net/Gossie/article/details/109320960?utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromMachineLearnPai2%7Edefault-3.control&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromMachineLearnPai2%7Edefault-3.control

软件

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变异识别-软件varscan2

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