# sgRNA设计 使用`sgRNAcas9`软件包进行基于参考基因组的sgRNA 设计：针对每条给定的query 序列首先搜索`NGG`的PAM结构，找到对应的motif后，在全基因组范围内评估`on-target`和`off-target`值。如果有多条质量比较好的`on-target`sgRNA，后面还可以根据基因注释文件筛选那些距离5'端更近的sgRNA。 > [软件包地址](http://www.biootools.com/software.html#tab1) ![CRISPRcase9](https://s1.ax1x.com/2020/04/16/JEVP3T.png) ## 主要流程 1. 提取基因的cDNA序列 2. 使用软件包中`sgRNAcas9.pl`脚本，进行全基因组范围搜索靶位点 3. 根据参考基因组的注释文件，对靶向位点进行注释（看是否靶向exon区域） ## 1.全基因组搜索sgRNA ```bash perl sgRNAcas9_3.0.5.pl -i genes_end.fasta -x 20 -l 40 -m 60 -g Ghirsutum_genome.fasta -o b -t s -v l -n 5 -s -3 -e 33 ``` ```bash -i Input file -x Length of sgRNA[20] -l The minimum value of GC content [20] -m The maximum value of GC content [80] -g The reference genome sequence -o Searching CRISPR target sites using DNA strands based option(s/a/b) [s, sense strand searching mode] [a, anti-sense strand searching mode] [b, both strand searching mode] -t Type of sgRNA searching mode(s/p) [s, single-gRNA searching mode] [p, paired-gRNA searching mode] -v Operation system(w/l/u/m/a) [w, for windows-32, 64] [l, for linux-64] [u, for linux-32] [m, for MacOSX-64] [a, for MacOSX-32] -n Maximum number of mismatches [5] -s The minimum value of sgRNA offset [-2] 错配罚分 -e The maximum value of sgRNA offset [32] -p Output path ``` ## 2.根据参考文件对sgRNA进行注释 ### 2.1提取评分等级为一下的sgRNA ID信息 * Best * repeat\_sites\_or\_bad * low\_risk ```bash #M表示错配碱基数 0M 1M 2M 3M rank 2 0 0 0 repeat_sites_or_bad 1 0 0 5 low_risk 1 0 0 3 Best ``` > 例如repeat等级中0M靶向的位置有2个，我们要看看它靶向的位置是否是同一个基因，进行sgRNA评价 ### 2.2合并所有的sgRNA信息 `cat A.Sort_OT_byID/* >all_genen_OT.txt` 靶标序列评价 使用自带的脚本***ot2gtf\_v2.pl\\***，对得到的sgRNA靶标进行评价，主要是看它是否靶向目标基因的外显子区域，或者存在靶向其他基因exon区域（脱靶情况） ```bash perl ../Usefull_Script/ot2gtf_v2.pl -i Low_OT.text -g ../Gh_gene.gtf -o Low_OT_gtf_out.text #也可以将上一步所有的OT文件合并之后，在运行脚本 ``` ### 2.3去除脱靶的sgRNA awk的原理: 1. 靶标基因与sgRNA的序列信息一致赋权值 0 2. sgRNA靶向同源基因和自己本身赋权值 0 3. 靶标序列脱靶赋权值 1 4. 最后将同一个sgRNA靶标的权值相加，为0则表示没有脱靶；否则脱靶舍弃 ```bash #awk脚本 -F "\t" '{ a=substr($1,1,15);a1=substr($1,6,1);a2=substr($1,7,2);b1=substr($5,6,1);b2=substr($5,7,2) }{ if(a==$8)print $0"\t"0; else if(a1=="A"&&b1=="D"){ if(a2==b2||a2=="02"&&b2==03||a2=="03"&&b2=="02"){ print $0"\t"0;}else print $0"\t"1;} else if(a1=="D"&&b1=="A"){ if(a2==b2||a2=="02"&&b2=="03"||a2=="03"&&b2=="02"){ print $0"\t"0;}else print $0"\t"1;} else print $0"\t"1;}' Best_Repeat_Low_OT_gtf >2222222 ``` 得到的没有脱靶的sgRNA编号 ![没有脱靶sgRNA](https://zpliu1126.github.io/Blog/img/sgRNAcase/otsgRNAcase.png) ## 3.sgRNA筛选 经过上一步筛选后的sgRNA文件，我们需要根据以下几个指标筛选比较理想的靶标序列 1. 靶标序列尽量靠近5’端 2. 同一个基因找两个靶标序列，尽量让这两段序列间隔在100bp左右 自定义python脚本 ```python usage: -h|--help print help information -g|--gff= gff file path way -s|--sgRNA= sgRNA file path way -l|--genelength= length of gene -r|--sequence= sgRNA sequence path way -o|--outfile= output file path way ``` > [脚本获取](https://github.com/zpliu1126/Bioinformatic/blob/33379dd39a948ff6e463a9c9f31ca5fb837ac998/sgRNAcas9/comparisonsgRNA.py) ## 4.参考 1. [tramisutes](https://tiramisutes.github.io/2017/01/13/CRISPR-Designer.html) 2. [sgRNAcase9](http://www.biootools.com/software.html#tab1) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://zpliu.gitbook.io/booknote/crisp-case9/sgrna-she-ji.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.