cnvnator安装及使用方法简介

414,782

1、安装gcc

推荐yum安装，自己寻找依存关系，版本当在4.8以上。

2、安装root

a、下载root_v6.03.04.source.tar.gz

yum install git make gcc-c++ gcc binutils libX11-devel libXpm-devel libXft-devel libXext-devel
 yum install gcc-gfortran openssl-devel pcre-devel mesa-libGL-devel glew-devel ftgl-devel mysql-devel fftw-devel cfitsio-devel graphviz-devel avahi-compat-libdns_sd-devel libldap-dev python-devel libxml2-devel gsl-static

b、cd root-6.03.04

./configure
 make
 source bin/thisroot.sh
 root
 报错：*** DISPLAY not set, setting it to 192.168.1.xx:0.0
 执行：export DISPLAY=:0.0

3、安装CNVnator

下载CNVnator_v0.3.zip
 unzip CNVnator_v0.3.zip
 cd ./CNVnator_v0.3/src/samtools
 #编译samtools之前一定要确定安装 ncurses*
 yum -y install ncurses*
 make
 cd ..
 vi Makefile
 ##修改里面改为ROOTFLAGS = -m64 -std=c++11 -pthread
 ##将-lCint去掉
 make

4、使用方法

/soft/CNVnator_v0.3/src/cnvnator -genome ./hg37.fa -root 2.root -chrom 1 2 3 -tree aln-pe.sam
 /soft/CNVnator_v0.3/src/cnvnator -genome ./hg37.fa -root 2.root -chrom 1 2 3 -d ../hg37/ -his 100
 /soft/CNVnator_v0.3/src/cnvnator -genome ./hg37.fa -root 2.root -chrom 1 2 3 -stat 100
 /soft/CNVnator_v0.3/src/cnvnator -genome ./hg37.fa -root 2.root -chrom 1 2 3 -partition 100
 /soft/CNVnator_v0.3/src/cnvnator -genome ./hg37.fa -root 2.root -chrom 1 2 3 -call 100 > cnvout.txt

评论  4  访客  4
    • 请输入您的QQ号 0
      1F
      回复

      您好！请问 bin_size 参数选择100 是经验值吗？ 对于全基因组和外显子组数据，bin_size是不是需要做不同的取值处理

        • mengfanrui
          B1
          回复

          @ 请输入您的QQ号 bin size的选择不是经验值，bin size的选择有两个标准：
          1. mean to sigma的比率大概在4-5之间, 计算mean to sigma的命令为：
          cnvnator -root my.root -eval
          2. 跟测序深度有关系：
          given the same data quality and read length, we observed that the optimal bin size, and thus breakpoint resolution accuracy, scales roughly inversely with the coverage, resulting in ~100-bp bins for 20–30X coverage; ~500-bp bins for 4–6X coverage, and ~30-bp bins for ~100X coverage.
          至于具体的bin size的选择，就要根据这两条去权衡。

            • 可爱雨过晴空 0
              B2
              回复

              @ mengfanrui 非常感谢您的解答！ 我再请教您一个问题啊，就是我发现好像CNVnator不能处理case-control的sample， 那么对于这种有normal-tumor的成对样本，您觉得用什么做CNV分析比较好呢？

          • mengfanrui
            2F
            回复

            **How to select the bin size?
            1. the ratio of mean to sigam is roughly 4-5;
            -eval returns values of mean and sigma once histograms are calculated
            eg:
            cnvnator -root my.root -his 1000 -d dir_with_chrom_fas
            cnvnator -root my.root -eval 1000
            2. Specifically, given the same data quality and read length, we observed that the optimal bin size, and thus breakpoint resolution accuracy, scales roughly inversely with the coverage, resulting in ~100-bp bins for 20–30X coverage; ~500-bp bins for 4–6X coverage, and ~30-bp bins for ~100X coverage.
            **How to filter the results from cnvnator?
            1. q0<0.5
            2. the first e-value(it is like the blast e-value) is small
            **批量获得genotype 和 Rd value
            awk '{print $2}END{print "exit"}' out.cnv | cnvnator -root out.root -genotype 100
            **-1 for q0 means it couldn’t be calculated. The likely reason is that no reads were mapped in that region.

          发表评论

          匿名网友