{"id":61294,"date":"2024-10-28T15:14:55","date_gmt":"2024-10-28T07:14:55","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61294"},"modified":"2024-10-28T15:14:55","modified_gmt":"2024-10-28T07:14:55","slug":"%e4%b8%80%e6%96%87%e6%89%93%e9%80%9a%e6%89%80%e6%9c%89bam%e6%96%87%e4%bb%b6%e5%88%86%e6%9e%90%ef%bc%8c%e8%bd%bb%e6%9d%be%e7%bb%98%e5%88%b6%e9%ab%98%e7%ab%af%e5%a4%a7%e6%b0%94%e7%9a%84%e6%b5%8b","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61294","title":{"rendered":"\u4e00\u6587\u6253\u901a\u6240\u6709bam\u6587\u4ef6\u5206\u6790\uff0c\u8f7b\u677e\u7ed8\u5236\u9ad8\u7aef\u5927\u6c14\u7684\u6d4b\u5e8f\u6570\u636e\u4fe1\u53f7\u70ed\u56fe\u4e0e\u8c31\u56fe\uff1adeeptools"},"content":{"rendered":"\n

<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Hi\uff01\u5c0f\u5e08\u59b9\u4eca\u5929\u6765\u6559\u5927\u5bb6\u4e00\u4e2a\u63a2\u7d22\u57fa\u56e0\u7ec4\u4fe1\u606f\u7684\u5f97\u529b\u52a9\u624b\u5de5\u5177\uff0c\u5728\u8fd9\u4e2a\u57fa\u56e0\u7ec4\u5b66\u9886\u57df\u98de\u901f\u53d1\u5c55\u7684\u9ec4\u91d1\u65f6\u4ee3\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u5206\u6790\u548c\u6bd4\u8f83\u5927\u91cf\u7684\u9ad8\u901a\u91cf\u6d4b\u5e8f\u6570\u636e\uff0c\u6bd4\u5982ChIP-seq\u3001RNA-seq\u7b49\uff0c\u4ee5\u63ed\u793a\u57fa\u56e0\u8868\u8fbe\u7684\u5965\u79d8\u548c\u8c03\u63a7\u673a\u5236\uff0c\u5728\u6b64\u4e4b\u524d\uff0c\u6211\u4eec\u8fd8\u9700\u8981\u5c06\u539f\u59cb\u6570\u636e\u8fdb\u884c\u8d28\u63a7\uff0c\u624d\u80fd\u987a\u5229\u8fdb\u884c\u4e0b\u6e38\u5206\u6790\uff01\u672c\u6b21\u4ecb\u7ecd\u7684\u8f6f\u4ef6\u64cd\u4f5c\u5360\u7528\u5185\u5b58\u6bd4\u8f83\u5927\uff0c\u5efa\u8bae\u4f7f\u7528\u670d\u52a1\u5668\uff0c\u6b22\u8fce\u8054\u7cfb\u5c0f\u5e08\u59b9\u79df\u8d41\u6027\u4ef7\u6bd4\u5c45\u9ad8\u7684\u670d\u52a1\u5668~<\/strong><\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u5f88\u591a\u670b\u53cb\u5728\u505a\u9879\u76ee\u7684\u65f6\u5019\uff0c\u5f97\u5230\u539f\u59cb\u6570\u636e\u4e4b\u540e\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5148\u4e86\u89e3\u4e00\u4e0b\u6d4b\u5e8f\u6570\u636e\u7684\u6d4b\u5e8f\u6df1\u5ea6\u548c\u8986\u76d6\u7387\u7b49\u60c5\u51b5\uff0c\u624d\u80fd\u786e\u5b9a\u6570\u636e\u662f\u5426\u9700\u8981\u91cd\u65b0\u6d4b\u5e8f\u3002\u8fd9\u4e2a\u65f6\u5019\u5c31\u9700\u8981\u7528\u5230\u8fd9\u4e48\u4e00\u6b3e\u795e\u5668\u4e86\uff0c\u5176\u582a\u79f0\u201c\u6df1\u6d77\u5229\u5668\u201d\u3002Deeptools2 \u8f6f\u4ef6\u5728 2016 \u5e74\u53d1\u8868\u5728 NAR\uff08Nucleic Acids Research\uff09\u4e0a\uff0c16 \u5e74\u63a8\u51fa\u7684 Deeptools2 \u5728\u8ba1\u7b97\u548c\u53ef\u89c6\u5316\u65b9\u9762\u505a\u51fa\u4e86\u5f88\u591a\u66f4\u65b0\u548c\u5347\u7ea7\uff0c\u65e8\u5728\u66f4\u5feb\u901f\u548c\u4fbf\u6377\u7684\u5206\u6790\u548c\u53ef\u89c6\u5316\u9ad8\u901a\u91cf\u6d4b\u5e8f\u6570\u636e\uff0c\u5176\u5f15\u7528\u91cf\u5df2\u8fbe3200\u591a\u6b21\uff0c\u662f\u4e00\u4e2a\u9760\u8c31\u3001\u53ef\u4fe1\u7684\u8d28\u68c0\u597d\u5de5\u5177\u3002\u4eca\u5929\u5c0f\u5e08\u59b9\u7ed9\u5927\u5bb6\u5e26\u6765Deeptools\u5de5\u5177\u7684\u57fa\u672c\u529f\u80fd\u548c\u7528\u9014\uff0c\u4ee5\u53ca\u5982\u4f55\u5b9e\u6218\u5e94\u7528\uff0c\u611f\u5174\u8da3\u7684\u8bdd\u5c31\u4e00\u8d77\u770b\u4e0b\u53bb\u5427\u3002\u5c0f\u5e08\u59b9\u53ef\u662f\u5389\u5bb3\u7684\u751f\u4fe1\u9ad8\u624b\uff0c\u8981\u662f\u540c\u5b66\u4eec\u6709\u81ea\u5df1\u505a\u4e0d\u4e86\u7684\u86cb\u767d\u8d28\u7ed3\u6784\u5206\u6790\uff0c\u6b22\u8fce\u968f\u65f6\u8054\u7cfb\u6211\uff01\uff01<\/strong><\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u4e00\u3001\u4ec0\u4e48\u662fDeeptools<\/strong><\/strong><\/p>\n\n\n\n

Deeptools\u5e38\u7528\u4e8e\u4ee5\u4e0b\u4efb\u52a1\uff1a\u7ed8\u5236\u70ed\u56fe\u3001\u7ed8\u5236\u5e73\u5747\u66f2\u7ebf\u3001\u8ba1\u7b97\u57fa\u56e0\u7ec4\u8986\u76d6\u5ea6\u548c\u5bcc\u96c6\u5206\u6790\u7b49\u3002\u5b83\u8fd8\u53ef\u4ee5\u4e0e\u5e38\u89c1\u7684\u57fa\u56e0\u7ec4\u6d4f\u89c8\u5668\uff08\u5982IGV\u548cUCSC Genome Browser\uff09\u96c6\u6210\uff0c\u4f7f\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u67e5\u770b\u548c\u6bd4\u8f83\u4e0d\u540c\u7684\u6d4b\u5e8f\u6570\u636e\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u56fe1 \u5de5\u5177\u529f\u80fd\u793a\u610f\u56fe<\/p>\n\n\n\n

\u4e8c\u3001\u4e0b\u8f7d\u548c\u5b89\u88c5<\/strong><\/strong><\/h1>\n\n\n\n

\u4f5c\u8005\u5728\u5b98\u7f51\u4e2d\uff0c\u5171\u63a8\u8350\u4e864\u79cd\u4e0b\u8f7d\u65b9\u6cd5\uff0c\u8fd9\u91cc\u5c0f\u5e08\u59b9\u7ed9\u5927\u5bb6\u5e26\u67653\u79cd\u6700\u5e38\u89c1\u7684\u4e0b\u8f7d\u65b9\u6cd5\uff1a<\/p>\n\n\n\n

1.<\/strong>Conda\u4e0b\u8f7d<\/strong><\/strong><\/p>\n\n\n\n

\u5b89\u88c5DeepTool\u7684\u63a8\u8350\u65b9\u5f0f<\/strong>\u662f\u901a\u8fc7miniconda\u6216anaconda\uff0c\u524d\u63d0\u662fconda\u914d\u7f6e\u4e86\u56fd\u5185\u955c\u50cf\u6e90\u3002<\/p>\n\n\n\n

$ conda install -c bioconda deeptools<\/strong><\/strong><\/p>\n\n\n\n

2.Pip\u4e0b\u8f7d<\/strong><\/strong><\/p>\n\n\n\n

Deeptools\u662fpython\u5305\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u5de5\u5177\u8fdb\u884c\u4e0b\u8f7d\uff1a<\/p>\n\n\n\n

$ pip install deeptools<\/strong><\/strong><\/p>\n\n\n\n

$ pip install deeptools==3.5.3  # \u53ef\u4ee5\u5b89\u88c5\u6307\u5b9a\u7248\u672c<\/strong><\/strong><\/p>\n\n\n\n

3.Git\u4e0b\u8f7d<\/strong><\/strong><\/p>\n\n\n\n

$ pip install deeptools==3.5.3  # \u53ef\u4ee5\u5b89\u88c5\u6307\u5b9a\u7248\u672c<\/strong><\/strong><\/p>\n\n\n\n

$ git clone https:\/\/github.com\/deeptools\/deepTools.git<\/strong><\/strong><\/p>\n\n\n\n

$ cd deepTools<\/strong><\/strong><\/p>\n\n\n\n

$ pip install .<\/strong><\/strong><\/p>\n\n\n\n

\u4e09\u3001\u5e38\u89c1\u547d\u4ee4<\/strong><\/strong><\/h1>\n\n\n\n

1.bamCoverage<\/strong><\/strong><\/p>\n\n\n\n

\u73b0\u5728\u5047\u8bbe\u6211\u5f97\u5230\u4e86\u4e00\u4e2abam\u6587\u4ef6\uff0c\u5e94\u8be5\u5982\u4f55\u751f\u6210\u4e00\u4e2a\u53ef\u4ee5\u5728\u57fa\u56e0\u7ec4\u6d4f\u89c8\u5668\uff08\u4f8b\u5982IGV\uff09\u4e2d\u67e5\u770b\u7684\u6587\u4ef6\u5462\uff1f\u5f53\u7136\uff0c\u6211\u4eec\u4e5f\u53ef\u4ee5\u5728\u57fa\u56e0\u7ec4\u6d4f\u89c8\u5668\u4e2d\u67e5\u770bBAM\u6587\u4ef6\uff0c\u4f46\u76f4\u63a5\u628abam\u5bfc\u5165IGV\u770b\u5cf0\u56fe\u592a\u6162\uff0c\u628abam\u8f6c\u4e3abigWig\u683c\u5f0f\u540e\uff0c\u6587\u4ef6\u4f1a\u5c0f\u5f88\u591a\uff0c\u8f7d\u5165\u4e5f\u5f88\u5feb\u4e86\u3002<\/p>\n\n\n\n

$ bamCoverage –bam, -b <your.bam> \\    # \u8981\u5904\u7406\u7684bam\u6587\u4ef6<\/strong><\/strong><\/p>\n\n\n\n

–outFileName, -o <output.bw> \\       # \u8981\u8f93\u51fa\u7684\u6587\u4ef6\u540d<\/strong><\/strong><\/p>\n\n\n\n

–region, -r <chr10> \\                # \u60f3\u8981\u5904\u7406\u7684\u533a\u57df\uff0c\u683c\u5f0f\u4e3achr:start:end<\/strong><\/strong><\/p>\n\n\n\n

–numberOfProcessors, -p <1> \\        # \u7ebf\u7a0b\u6570\uff0c\u9ed8\u8ba4\u4e3a1<\/strong><\/strong><\/p>\n\n\n\n

–verbose, -v \\                       # \u663e\u793a\u5904\u7406\u8fc7\u7a0b<\/strong><\/strong><\/p>\n\n\n\n

–binSize 10                          # bin\u8d8a\u5c0f\uff0c\u5206\u8fa8\u7387\u8d8a\u9ad8\uff0c\u9ed8\u8ba4\u4e3a50<\/strong><\/strong><\/p>\n\n\n\n

\u7136\u540e\u518d\u5c06bigwig\u6587\u4ef6\u5bfc\u5165IGV\uff0c\u8bb0\u5f97\u8981\u5bfc\u5165\u53c2\u8003\u57fa\u56e0\u7ec4\u6587\u4ef6\uff0c\u53ef\u4ee5\u5f97\u5230\u4e00\u4e2a\u5404\u6837\u672c\u603b\u4f53\u8868\u8fbe\u91cf\u60c5\u51b5\uff0c\u8fd8\u53ef\u4ee5\u70b9\u8fdb\u4e0d\u540c\u7684\u67d3\u8272\u4f53\u8fdb\u4e00\u6b65\u67e5\u770b\uff0c\u6548\u679c\u56fe\u5982\u4e0b\u3002\u600e\u4e48\u6837\uff0c\u662f\u4e0d\u662f\u5f88\u76f4\u89c2\u5462\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u8c03\u6574\u8981\u663e\u793a\u7684\u67d3\u8272\u4f53\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u56fe2 bigwig\u6587\u4ef6\u5bfc\u5165IGV<\/p>\n\n\n\n

2.plotCoverage<\/strong><\/strong><\/p>\n\n\n\n

\u8be5\u5de5\u5177\u53ef\u7528\u4e8e\u8bc4\u4f30\u7ed9\u5b9a\u6837\u672c\u7684\u6d4b\u5e8f\u6df1\u5ea6\u3002\u5b83\u91c7\u6837100\u4e07\u4e2abp\uff0c\u8ba1\u7b97\u91cd\u53e0\u8bfb\u53d6\u7684\u6570\u91cf\uff0c\u5e76\u53ef\u4ee5\u62a5\u544a\u4e00\u4e2a\u76f4\u65b9\u56fe\uff0c\u544a\u8bc9\u4f60\u6709\u591a\u5c11\u4e2a\u78b1\u57fa\u88ab\u8986\u76d6\u4e86\u591a\u5c11\u6b21\u3002\u53ef\u4ee5\u63a5\u53d7\u591a\u4e2aBAM\u6587\u4ef6\uff0c\u4f46\u5b83\u4eec\u90fd\u5e94\u8be5\u5bf9\u5e94\u4e8e\u76f8\u540c\u7684\u57fa\u56e0\u7ec4\u7ec4\u88c5\u3002<\/p>\n\n\n\n

$ plotCoverage -b <sample1.bam sample2.bam> \\  # \u7a7a\u683c\u95f4\u9694\u7684bam\u6587\u4ef6<\/strong><\/strong><\/p>\n\n\n\n

–plotFile, -o <coverage.png> \\              # \u8f93\u51fa\u6587\u4ef6\u540d<\/strong><\/strong><\/p>\n\n\n\n

–labels, -l <label1 label2> \\               # \u5b9a\u4e49\u6837\u672c\u7684\u6807\u7b7e\uff0c\u7a7a\u683c\u95f4\u9694<\/strong><\/strong><\/p>\n\n\n\n

–plotTitle, -T <yourTitle> \\                # \u7ed8\u56fe\u6807\u9898<\/strong><\/strong><\/p>\n\n\n\n

–skipZeros \\                                # \u6392\u96640\u6216nan\u7684\u57fa\u56e0\u7ec4<\/strong><\/strong><\/p>\n\n\n\n

–outRawCounts readCounts.tab\\               # \u5c06\u539f\u59cb\u8ba1\u6570 \uff08coverage\uff09 \u4fdd\u5b58\u5230\u6587\u4ef6<\/strong><\/strong><\/p>\n\n\n\n

–region, -r \\                               # \u60f3\u8981\u5904\u7406\u7684\u533a\u57df\uff0c\u683c\u5f0f\u4e3achr:start:end<\/strong><\/strong><\/p>\n\n\n\n

–numberOfProcessors, -p \\                   # \u7ebf\u7a0b\u6570\uff0c\u9ed8\u8ba4\u4e3a1<\/strong><\/strong><\/p>\n\n\n\n

$ head readCounts.tab<\/strong><\/strong><\/p>\n\n\n\n

#’chr’  ‘start’ ‘end’   ‘sample1.bam’        ‘sample2.bam’        ‘sample3.bam’        ‘sample4.bam’        ‘sample5.bam’        ‘sample6.bam’<\/strong><\/strong><\/p>\n\n\n\n

NC_072815.1     979324  979325  456.0   743.0   645.0   708.0   409.0   389.0<\/strong><\/strong><\/p>\n\n\n\n

NC_072815.1     979336  979337  468.0   743.0   636.0   709.0   408.0   381.0<\/strong><\/strong><\/p>\n\n\n\n

NC_072815.1     979348  979349  487.0   720.0   628.0   716.0   425.0   383.0<\/strong><\/strong><\/p>\n\n\n\n

NC_072815.1     979360  979361  498.0   719.0   622.0   740.0   425.0   375.0<\/strong><\/strong><\/p>\n\n\n\n

NC_072815.1     979372  979373  503.0   712.0   614.0   735.0   444.0   368.0<\/strong><\/strong><\/p>\n\n\n\n

NC_072815.1     979384  979385  512.0   740.0   662.0   758.0   456.0   373.0<\/strong><\/strong><\/p>\n\n\n\n

NC_072815.1     979396  979397  501.0   724.0   665.0   762.0   465.0   370.0<\/strong><\/strong><\/p>\n\n\n\n

NC_072815.1     979408  979409  509.0   730.0   659.0   773.0   456.0   367.0<\/strong><\/strong><\/p>\n\n\n\n

plotCoverage\u53ef\u4ee5\u5f97\u5230\u4e00\u4e2abam\u6587\u4ef6\u7684\u53ef\u89c6\u5316\u56fe\uff0c\u5de6\u56fe\u53ef\u4ee5\u5f97\u5230\u5404\u6837\u672c\u5e73\u5747\u6df1\u5ea6\uff0c\u53f3\u56fe\u53ef\u4ee5\u77e5\u9053\u5404\u6df1\u5ea6\u6240\u5360\u6bd4\u4f8b\uff0c\u4f8b\u5982\u5728\u68d5\u8272\u7ebf\u4ee3\u8868\u7684\u6837\u672c\u4e2d\uff0c400X\u6df1\u5ea6\u7684\u7247\u6bb5\u536020%\u3002\u00a0<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u56fe3 plotCoverage\u5f97\u5230\u5e73\u5747\u6df1\u5ea6\u548c\u6240\u5360\u6bd4\u4f8b<\/h2>\n\n\n\n

3.computeMatrix\uff1a\u805a\u7c7b\u5206\u6790<\/strong><\/strong><\/p>\n\n\n\n

computeMatrix\u4e3b\u8981\u7528\u4e8e\u8ba1\u7b97\u57fa\u56e0\u7ec4\u533a\u57df\u7684\u5f97\u5206\uff0c\u5e76\u751f\u6210\u4e00\u4e2a\u4e2d\u95f4\u6587\u4ef6\uff0c\u8be5\u6587\u4ef6\u53ef\u4ee5\u4e0eplotHeatmap\u548cplotProfile\u5de5\u5177\u4e00\u8d77\u4f7f\u7528\u8fdb\u884c\u53ef\u89c6\u5316\u3002computeMatrix\u6709\u4e24\u79cd\u4e3b\u8981\u7684\u4f7f\u7528\u6a21\u5f0f\uff0c\u539f\u7406\u5982\u4e0b\u56fe\uff1a<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u56fe4 computeMatrix\u805a\u7c7b\u5206\u6790\u539f\u7406\u793a\u610f\u56fe<\/p>\n\n\n\n

\u2460reference-point\u6a21\u5f0f\uff1a\u8ba1\u7b97\u76f8\u5bf9\u4e8e\u67d0\u4e2a\u70b9\uff08\u4f8b\u5982\u57fa\u56e0\u7684\u8f6c\u5f55\u8d77\u59cb\u4f4d\u70b9TSS\u3001\u7ec8\u6b62\u4f4d\u70b9TES\u6216\u533a\u57df\u4e2d\u5fc3\uff09\u7684\u4fe1\u53f7\u5206\u5e03\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7-referencePoint\u53c2\u6570\u6307\u5b9a\u53c2\u8003\u70b9\uff0c\u4ee5\u53ca\u901a\u8fc7b\u548ca\u53c2\u6570\u5b9a\u4e49\u611f\u5174\u8da3\u533a\u57df\u7684\u4e0a\u6e38\u548c\u4e0b\u6e38\u957f\u5ea6\u3002<\/p>\n\n\n\n

$ computeMatrix scale-regions -S H3K27Me3-input.bigWig \\ <\/strong><\/strong><\/p>\n\n\n\n

H3K4Me1-Input.bigWig \\ <\/strong><\/strong><\/p>\n\n\n\n

H3K4Me3-Input.bigWig \\ <\/strong><\/strong><\/p>\n\n\n\n

-R genes19.bed genesX.bed \\ <\/strong><\/strong><\/p>\n\n\n\n

–beforeRegionStartLength 3000 \\ <\/strong><\/strong><\/p>\n\n\n\n

–regionBodyLength 5000 \\ <\/strong><\/strong><\/p>\n\n\n\n

–afterRegionStartLength 3000 <\/strong><\/strong><\/p>\n\n\n\n

–skipZeros -o matrix_1.mat.gz <\/strong><\/strong><\/p>\n\n\n\n

$ plotHeatmap -m matrix_2.mat.gz \\ <\/strong><\/strong><\/p>\n\n\n\n

-out ExampleHeatmap2.png \\<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u56fe5 reference-point\u7528\u4e8e\u8ba1\u7b97\u76f8\u5bf9\u4e8e\u70b9\uff08\u53c2\u8003\u70b9\uff09\u7684\u4fe1\u53f7\u5206\u5e03<\/p>\n\n\n\n

\u2461scale-regions\u6a21\u5f0f\uff1a\u8ba1\u7b97\u4e00\u7ec4\u533a\u57df\u4e0a\u7684\u4fe1\u53f7\uff0c\u8fd9\u4e9b\u533a\u57df\u53ef\u4ee5\u88ab\u7f29\u653e\u5230\u76f8\u540c\u7684\u5927\u5c0f\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7b\uff08beforeRegionStartLength\uff09\u3001a\uff08afterRegionStartLength\uff09\u548cm\uff08regionBodyLength\uff09\u53c2\u6570\u63a7\u5236\u533a\u57df\u957f\u5ea6\u3002<\/p>\n\n\n\n

$ computeMatrix scale-regions -S H3K27Me3-input.bigWig \\ <\/strong><\/strong><\/p>\n\n\n\n

H3K4Me1-Input.bigWig \\ <\/strong><\/strong><\/p>\n\n\n\n

H3K4Me3-Input.bigWig \\ <\/strong><\/strong><\/p>\n\n\n\n

-R genes19.bed genesX.bed \\ <\/strong><\/strong><\/p>\n\n\n\n

–beforeRegionStartLength 3000 \\ <\/strong><\/strong><\/p>\n\n\n\n

–regionBodyLength 5000 \\ <\/strong><\/strong><\/p>\n\n\n\n

–afterRegionStartLength 3000 <\/strong><\/strong><\/p>\n\n\n\n

–skipZeros -o matrix.mat.gz <\/strong><\/strong><\/p>\n\n\n\n

$ plotProfile -m matrix.mat.gz \\ <\/strong><\/strong><\/p>\n\n\n\n

-out ExampleProfile1.png \\ <\/strong><\/strong><\/p>\n\n\n\n

–numPlotsPerRow 2 \\ <\/strong><\/strong><\/p>\n\n\n\n

–plotTitle “Test data profile”<\/strong><\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u56fe6 scale-regions\u7528\u4e8e\u5728\u4e00\u7ec4\u533a\u57df(\u6bd4\u4f8b\u5c3a-\u533a\u57df)\u4e0a\u8ba1\u7b97\u4fe1\u53f7\uff0c\u5176\u4e2d\u6240\u6709\u533a\u57df\u90fd\u7f29\u653e\u5230\u76f8\u540c\u7684\u5927\u5c0f<\/p>\n\n\n\n

4.<\/strong>multiBigwigSummary<\/strong><\/strong><\/p>\n\n\n\n

multiBigwigSummary\u7528\u4e8e\u8ba1\u7b97\u4e00\u7ec4bigWig\u6587\u4ef6\u5728\u57fa\u56e0\u7ec4\u4e2d\u7684\u6307\u5b9a\u533a\u57df\u6216\u6574\u4e2a\u57fa\u56e0\u7ec4\u4e0a\u7684\u8986\u76d6\u5ea6\u5e73\u5747\u503c\uff0c\u8fd9\u4e2a\u5de5\u5177\u6709\u4e24\u79cd\u6a21\u5f0f\uff1abins\u548cBED-file\u3002\u8ba1\u7b972\u4e2a\u4ee5\u4e0a\u7684bam\u7684\u57fa\u56e0\u7ec4\u533a\u57df\u7684read\u8986\u76d6\u5ea6\u3002\u53ef\u4ee5\u4f7f\u7528\u201cbins\u201d\u6a21\u5f0f\u5bf9\u6574\u4e2a\u57fa\u56e0\u7ec4\u8fdb\u884c\u5206\u6790\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528BED\u6587\u4ef6\u6a21\u5f0f\u8ba1\u7b97\u7279\u5b9a\u533a\u57df\u7684reads\u8986\u76d6\u5ea6\u3002<\/p>\n\n\n\n

plotCorrelation\u6839\u636e\u57fa\u56e0\u7ec4\u533a\u57df\u5185\u7684read\u8986\u76d6\u7387\uff08\u6216\u5176\u4ed6\u5206\u6570\uff09\uff0c\u8ba1\u7b97\u4e24\u4e2a\u6216\u591a\u4e2a\u6587\u4ef6\u4e4b\u95f4\u7684\u603b\u4f53\u76f8\u4f3c\u5ea6\uff0c\u5fc5\u987b\u4f7f\u7528multiBamSummary\u6216multiBigwigSummary\u8ba1\u7b97\u3002Pearson\u662f\u9002\u7528\u4e8e\u7b26\u5408\u6b63\u6001\u5206\u5e03\u7684\u6570\u636e\u7684\u4e00\u79cd\u9002\u5f53\u7684\u6d4b\u91cf\u65b9\u6cd5\uff0c\u800cSpearman\u5219\u4e0d\u505a\u51fa\u8fd9\u79cd\u5047\u8bbe\uff0c\u901a\u5e38\u4e0d\u53d7\u5f02\u5e38\u503c\u7684\u5f71\u54cd\uff0c\u4f46\u4e5f\u56e0\u6b64\u53d8\u5f97\u4e0d\u592a\u654f\u611f\u3002<\/strong><\/strong><\/p>\n\n\n\n

# bins\u6a21\u5f0f<\/strong><\/strong><\/p>\n\n\n\n

$ multiBigwigSummary bins \\<\/strong><\/strong><\/p>\n\n\n\n

-b testFiles\/H3K4Me1.bigWig testFiles\/H3K4Me3.bigWig testFiles\/H3K27Me3.bigWig testFiles\/Input.bigWig \\<\/strong><\/strong><\/p>\n\n\n\n

–labels H3K4me1 H3K4me3 H3K27me3 input \\<\/strong><\/strong><\/p>\n\n\n\n

-out scores_per_bin.npz –outRawCounts scores_per_bin.tab<\/strong><\/strong><\/p>\n\n\n\n

$ head scores_per_bin.tab<\/strong><\/strong><\/p>\n\n\n\n

    #’chr’  ‘start’ ‘end’   ‘H3K4me1’       ‘H3K4me3’       ‘H3K27me3’      ‘input’<\/strong><\/strong><\/p>\n\n\n\n

    19      0       10000   0.0     0.0     0.0     0.0<\/strong><\/strong><\/p>\n\n\n\n

    19      10000   20000   0.0     0.0     0.0     0.0<\/strong><\/strong><\/p>\n\n\n\n

    19      20000   30000   0.0     0.0     0.0     0.0<\/strong><\/strong><\/p>\n\n\n\n

    19      30000   40000   0.0     0.0     0.0     0.0<\/strong><\/strong><\/p>\n\n\n\n

    19      40000   50000   0.0     0.0     0.0     0.0<\/strong><\/strong><\/p>\n\n\n\n

    19      50000   60000   0.0221538461538 0.0     0.00482142857143        0.0522717391304<\/strong><\/strong><\/p>\n\n\n\n

    19      60000   70000   4.27391282051   1.625   0.634116071429  1.29124347826<\/strong><\/strong><\/p>\n\n\n\n

    19      70000   80000   13.0891675214   24.65   1.8180625       2.80073695652<\/strong><\/strong><\/p>\n\n\n\n

    19      80000   90000   1.74591965812   0.29    4.35576785714   0.92987826087<\/strong><\/strong><\/p>\n\n\n\n

$ plotCorrelation -in matrix.gz -c spearman -p heatmap -o plot.png<\/strong><\/strong><\/p>\n\n\n\n

# BED-file\u6a21\u5f0f<\/strong><\/strong><\/p>\n\n\n\n

$ multiBigwigSummary BED-file \\<\/strong><\/strong><\/p>\n\n\n\n

–bwfiles testFiles\/*bigWig \\<\/strong><\/strong><\/p>\n\n\n\n

–BED testFiles\/genes.bed \\<\/strong><\/strong><\/p>\n\n\n\n

–labels H3K27me3 H3K4me1 H3K4me3 HeK9me3 input \\<\/strong><\/strong><\/p>\n\n\n\n

-out scores_per_transcript.npz –outRawCounts scores_per_transcript.tab<\/strong><\/strong><\/p>\n\n\n\n

$ head scores_per_transcript.tab<\/strong><\/strong><\/p>\n\n\n\n

 #’chr’     ‘start’ ‘end’   ‘H3K27me3’      ‘H3K4me1’       ‘H3K4me3’       ‘HeK9me3’       ‘input’<\/strong><\/strong><\/p>\n\n\n\n

19  60104   70951   0.663422099099  4.37103606574   14.9609108509   0.596631607217  1.34274297191<\/strong><\/strong><\/p>\n\n\n\n

19  60950   70966   0.714223982699  4.54650763906   16.2336261981   0.62173674295   1.41719308888<\/strong><\/strong><\/p>\n\n\n\n

19  62114   70944   0.747578769617  4.84009060023   18.2951302378   0.648723472352  1.51324474371<\/strong><\/strong><\/p>\n\n\n\n

19  63820   70951   0.781816722009  5.30500631048   22.5579862572   0.682862029229  1.55490104062<\/strong><\/strong><\/p>\n\n\n\n

19  65057   66382   0.528301886792  5.45886792453   0.523018867925  0.555471698113  1.97056603774<\/strong><\/strong><\/p>\n\n\n\n

19  65821   66416   0.411764705882  3.0     0.636974789916  0.168067226891  1.67226890756<\/strong><\/strong><\/p>\n\n\n\n

19  65821   70945   0.844600775761  4.79176424668   31.1346604215   0.693073728066  1.47911787666<\/strong><\/strong><\/p>\n\n\n\n

19  66319   66492   0.774566473988  1.59537572254   0.0     0.0     0.578034682081<\/strong><\/strong><\/p>\n\n\n\n

19  66345   71535   0.877430197151  5.49036608863   43.978805395    0.746026011561  1.43545279383<\/strong><\/strong><\/p>\n\n\n\n

multiBigwigSummary\u9009\u9879\u53ef\u4ee5\u5bf9\u591a\u4e2abw\u6587\u4ef6\u8fdb\u884c\u6574\u5408\uff0c\u7136\u540e\u7ed3\u5408plotCorrelation\u548cplotPCA\u53ef\u4ee5\u5b9e\u73b0\u6837\u672c\u76f8\u5173\u6027\u5206\u6790\u3002\u6b64\u5916\uff0cmultiBamSummary\u8fd8\u53ef\u4ee5\u751f\u6210bedGraph\u6587\u4ef6\uff0c\u4ee5\u4fbf\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u3002<\/p>\n\n\n\n

$ deepTools2.0\/bin\/plotCorrelation \\<\/strong><\/strong><\/p>\n\n\n\n

-in scores_per_transcript.npz \\<\/strong><\/strong><\/p>\n\n\n\n

–corMethod pearson –skipZeros \\<\/strong><\/strong><\/p>\n\n\n\n

–plotTitle “Pearson Correlation of Average Scores Per Transcript” \\<\/strong><\/strong><\/p>\n\n\n\n

–whatToPlot scatterplot \\<\/strong><\/strong><\/p>\n\n\n\n

-o scatterplot_PearsonCorr_bigwigScores.png   \\<\/strong><\/strong><\/p>\n\n\n\n

–outFileCorMatrix PearsonCorr_bigwigScores.tab<\/strong><\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u56fe7 \u4f7f\u7528 multiBigwigSummary \u8ba1\u7b97\u6bcf\u4e2a\u8f6c\u5f55\u672c\u7684\u5e73\u5747\u5f97\u5206\uff0c\u5e76\u5305\u62ec\u6bcf\u4e2a\u6bd4\u8f83\u7684 Pearson \u76f8\u5173\u7cfb\u6570\uff0c\u7136\u540e\u5bf9\u5176\u8fdb\u884c\u6210\u5bf9\u6563\u70b9\u56fe<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u56fe8 \u751f\u6210\u70ed\u56fe\uff0c\u5176\u4e2d\u6210\u5bf9\u76f8\u5173\u7cfb\u6570\u7528\u4e0d\u540c\u7684\u989c\u8272\u5f3a\u5ea6\u63cf\u7ed8\uff0c\u5e76\u4f7f\u7528\u5206\u5c42\u805a\u7c7b\u8fdb\u884c\u805a\u7c7b\u3002<\/p>\n\n\n\n

\u4ee5\u4e0a\u4ee3\u7801\u5c31\u662f\u5bf9Deeptools\u7684\u4ecb\u7ecd\u4e0e\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5\uff0c\u5b66\u4f1a\u8fd9\u4e9b\u65b9\u6cd5\u540e\uff0c\u4f60\u53ef\u4ee5\u5b66\u4e60\u5982\u4f55\u4f7f\u7528Deeptools\u8fdb\u884cbam\u6587\u4ef6\u5206\u6790\uff0c\u5305\u62ec\u8f6c\u6362\u4e3abigwig\u6587\u4ef6\u3001\u83b7\u53d6\u6d4b\u5e8f\u8986\u76d6\u7387\uff0c\u4ee5\u53ca\u5bf9\u57fa\u56e0\u533a\u57df\u8fdb\u884c\u7edf\u8ba1\u548c\u4f5c\u56fe\u7b49\u3002\u5f53\u7136\uff0c\u5927\u5bb6\u4e5f\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u548c\u7814\u7a76\u76ee\u6807\u4fee\u6539\u548c\u6269\u5c55\u8fd9\u4e9b\u4ee3\u7801\u54e6\uff01\uff01\u600e\u4e48\u6837\uff0c\u4f60\u5b66\u4f1a\u4e86\u561b\uff01\uff1f<\/p>\n\n\n\n

\u5b66\u5b8c\u4e86\u64cd\u4f5c\u6d41\u7a0b\u662f\u4e0d\u662f\u60f3\u9a6c\u4e0a\u64cd\u4f5c\u4e00\u756a\uff0c\u5c0f\u5e08\u59b9\u4e5f\u5728\u8d44\u6599\u4e2d\u5b58\u653e\u4e86\u6d4b\u8bd5\u6570\u636e\u3002\u52a0\u5e08\u59b9\u5fae\u4fe1\u83b7\u53d6\u8d44\u6599\u54e6\uff5e\u5927\u5bb6\u53ef\u4ee5\u5b66\u4e60\u5e76\u6d4b\u8bd5\u4e00\u4e0b\uff0c\u8ba9\u77e5\u8bc6\u66f4\u52a0\u7262\u56fa\u3002<\/p>\n\n\n\n

\u540c\u5b66\u4eec\u5982\u679c\u89c9\u5f97\u81ea\u5df1\u5199\u4ee3\u7801\u9ebb\u70e6\uff0c\u53ef\u4ee5\u4f53\u9a8c\u4e00\u4e0b\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u5c0f\u5de5\u5177\uff0c\u53ea\u9700\u8f93\u5165\u6570\u636e\uff0c\u5373\u53ef\u8f7b\u677e\u751f\u6210\u6240\u9700\u56fe\u8868\u3002\u7acb\u5373\u8bbf\u95ee\u4e91\u751f\u4fe1\uff08http:\/\/www.biocloudservice.com\/home.html\uff09\uff0c\u5f00\u542f\u4fbf\u6377\u7684\u751f\u4fe1\u4e4b\u65c5\uff01<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

Hi\uff01\u5c0f\u5e08\u59b9\u4eca\u5929\u6765\u6559\u5927\u5bb6\u4e00\u4e2a\u63a2\u7d22\u57fa\u56e0\u7ec4\u4fe1\u606f\u7684\u5f97\u529b\u52a9\u624b\u5de5\u5177\uff0c\u5728\u8fd9\u4e2a\u57fa\u56e0\u7ec4\u5b66\u9886\u57df\u98de\u901f\u53d1\u5c55\u7684\u9ec4\u91d1\u65f6\u4ee3\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u5206 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61294"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=61294"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61294\/revisions"}],"predecessor-version":[{"id":61307,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61294\/revisions\/61307"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=61294"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=61294"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=61294"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}