{"id":61413,"date":"2024-10-30T15:34:26","date_gmt":"2024-10-30T07:34:26","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61413"},"modified":"2024-10-30T15:34:26","modified_gmt":"2024-10-30T07:34:26","slug":"%e4%b8%80%e9%94%ae%e8%a7%a3%e9%94%81%e6%9f%93%e8%89%b2%e8%b4%a8%e7%a7%98%e5%af%86%ef%bc%81dinor%e5%8c%85%ef%bc%8c%e5%b8%a6%e4%bd%a0%e8%bd%bb%e6%9d%be%e9%a9%be%e9%a9%adnome-seq%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61413","title":{"rendered":"\u4e00\u952e\u89e3\u9501\u67d3\u8272\u8d28\u79d8\u5bc6\uff01dinoR\u5305\uff0c\u5e26\u4f60\u8f7b\u677e\u9a7e\u9a6dNOMe-seq\u6570\u636e\uff01"},"content":{"rendered":"\n

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

\u4eb2\u7231\u7684\u79d1\u7814\u5c0f\u4f19\u4f34\u4eec\uff0c\u4f60\u4eec\u7684\u5c0f\u5e08\u59b9\u53c8\u6765\u5566\uff01\u4eca\u5929\uff0c\u6211\u8981\u7ed9\u5927\u5bb6\u5e26\u6765\u4e00\u4e2a\u8d85\u7ea7\u68d2\u7684\u751f\u7269\u4fe1\u606f\u5b66\u5de5\u5177\u2014\u2014dinoR\u5305\u3002\u5728\u8fd9\u4e2a\u57fa\u56e0\u7ec4\u5b66\u65e5\u65b0\u6708\u5f02\u7684\u65f6\u4ee3\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u6bd4\u8f83\u4e24\u7ec4NOMe-seq\u5b9e\u9a8c\u4e2d\u6838\u5c0f\u4f53\u7ed3\u5408\/\u53ef\u53ca\u6027\u6a21\u5f0f\u7684\u6f5c\u5728\u5dee\u5f02\u3002\u8fd9\u4e0d\uff0c\u5c0f\u5e08\u59b9\u6700\u8fd1\u5c31\u53d1\u73b0\u4e86\u8fd9\u4e2a\u8d85\u7ed9\u529b\u7684R\u5305\uff0c\u5b83\u4e0d\u4ec5\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u9ad8\u6548\u5730\u5904\u7406\u5927\u91cf\u6570\u636e\uff0c\u8fd8\u80fd\u8ba9\u6211\u4eec\u8f7b\u677e\u5730\u68c0\u6d4b\u51fa\u4e0d\u540c\u6837\u672c\u95f4\u7684\u7ec6\u5fae\u5dee\u522b\u3002<\/p>\n\n\n\n

\u60f3\u8c61\u4e00\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u4e00\u6b21\u6027\u5bf9\u4e0a\u5343\u4e2a\u611f\u5174\u8da3\u7684\u57fa\u56e0\u7ec4\u533a\u57df\uff08ROIs\uff09\u8fdb\u884c\u9ad8\u8986\u76d6\u7387\u7684NOMe-seq\u6570\u636e\u83b7\u53d6\uff0c\u8fd9\u5728\u4ee5\u524d\u53ef\u662f\u60f3\u90fd\u4e0d\u6562\u60f3\u7684\u4e8b\u60c5\u54e6\uff01\u800c\u4e14\uff0c\u8fd9\u4e2a`dinoR`\u5305\u8fd8\u80fd\u8ba9\u6211\u4eec\u4e13\u6ce8\u4e8e\u6bd4\u8f83\u5177\u6709\u76f8\u4f3c\u7279\u5f81\u7684ROIs\uff0c\u6bd4\u5982\u8f6c\u5f55\u56e0\u5b50\u7ed3\u5408\u4f4d\u70b9\uff0c\u5e76\u5bf9\u6837\u672c\u95f4\u7684NOMe-seq\u6a21\u5f0f\u5dee\u5f02\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3002\u8fd9\u7b80\u76f4\u5c31\u662f\u6211\u4eec\u8fd9\u4e9b\u79d1\u7814\u5c0f\u4f19\u4f34\u4eec\u7684\u798f\u97f3\u561b\uff01\u6ce8\u610f\u54e6\uff0c\u8fd9\u4e2a<\/strong>R\u5305\u64cd\u4f5c\u5360\u7528\u5185\u5b58\u6bd4\u8f83\u5927\uff0c\u5efa\u8bae\u4f7f\u7528\u670d\u52a1\u5668<\/strong>\u54e6<\/strong>\uff0c\u6b22\u8fce\u8054\u7cfb\u5c0f\u5e08\u59b9\u79df\u8d41\u6027\u4ef7\u6bd4\u9ad8\u7684\u670d\u52a1\u5668<\/strong>~<\/strong><\/p>\n\n\n\n

\u522b\u6025\uff0c\u5c0f\u5e08\u59b9\u8fd9\u5c31\u5e26\u4f60\u4e00\u8d77\u63a2\u7d22`dinoR`\u7684\u795e\u5947\u529f\u80fd\uff0c\u8ba9\u4f60\u7684\u7814\u7a76\u5de5\u4f5c\u66f4\u52a0\u5f97\u5fc3\u5e94\u624b\u3002\u4e0d\u7ba1\u4f60\u662f\u57fa\u56e0\u7ec4\u5b66\u7684\u8001\u624b\uff0c\u8fd8\u662f\u521a\u5165\u95e8\u7684\u65b0\u624b\uff0c\u8fd9\u4e2a\u5de5\u5177\u90fd\u80fd\u8ba9\u4f60\u7684\u7814\u7a76\u4e4b\u8def\u66f4\u52a0\u987a\u7545\u3002\u51c6\u5907\u597d\u4e86\u5417\uff1f\u8ba9\u6211\u4eec\u4e00\u8d77\u5f00\u542f\u8fd9\u573a\u57fa\u56e0\u7ec4\u63a2\u7d22\u4e4b\u65c5\u5427\uff01\u5982\u679c\u4f60\u5728\u64cd\u4f5c\u8fc7\u7a0b\u4e2d\u9047\u5230\u4efb\u4f55\u95ee\u9898\uff0c\u522b\u72b9\u8c6b\uff0c\u8bb0\u5f97\u8054\u7cfb\u5c0f\u5e08\u59b9\u54e6~<\/strong><\/strong><\/p>\n\n\n\n

\u5b89\u88c5\u548c\u52a0\u8f7dR\u5305<\/strong><\/strong><\/p>\n\n\n\n

\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u5b89\u88c5dinoR\u5305\u5e76\u52a0\u8f7d\u6240\u6709\u8981\u7528\u7684\u5305\uff1a<\/p>\n\n\n\n

if (!require(“BiocManager”, quietly = TRUE)) {<\/p>\n\n\n\n

    install.packages(“BiocManager”)}<\/p>\n\n\n\n

BiocManager::install(“dinoR”)<\/p>\n\n\n\n

suppressPackageStartupMessages({<\/p>\n\n\n\n

  library(dinoR)<\/p>\n\n\n\n

  library(ggplot2)<\/p>\n\n\n\n

  library(dplyr)})<\/p>\n\n\n\n

\u52a0\u8f7dNOMe-seq\u6570\u636e<\/strong><\/strong><\/p>\n\n\n\n

\u63a5\u4e0b\u6765\uff0c\u5c0f\u5e08\u59b9\u6765\u7ed9\u4f60\u7b80\u5355\u8bf4\u8bf4\u600e\u4e48\u52a0\u8f7dNOMe-seq\u6570\u636e\u5427\uff01<\/p>\n\n\n\n

\u9996\u5148\uff0c\u6211\u4eec\u8981\u52a0\u8f7d\u7684\u662fADNP\u6572\u9664\u548c\u91ce\u751f\u578b\u5c0f\u9f20\u80da\u80ce\u5e72\u7ec6\u80de\u7684NOMe-seq\u6570\u636e\u3002\u8fd9\u4e9b\u6570\u636e\u662f\u901a\u8fc7\u4e00\u7cfb\u5217\u590d\u6742\u7684\u6b65\u9aa4\u5904\u7406\u5f97\u5230\u7684\uff0c\u5305\u62ec\u4f7f\u7528biscuit\u5de5\u5177\u5c06\u8bfb\u6bb5\u6620\u5c04\u5230\u57fa\u56e0\u7ec4\uff0c\u7528UMI-tools\u53bb\u9664\u91cd\u590d\u7684UMIs\uff0c\u4ee5\u53ca\u7528fetch-NOMe\u5305\u83b7\u53d6\u7532\u57fa\u5316\u4fdd\u62a4\u4fe1\u606f\u3002\u6700\u540e\uff0c\u6211\u4eec\u7528NOMeConverteR\u5305\u628a\u8fd9\u4e9b\u6570\u636e\u8f6c\u6362\u6210\u4e86RangedSummarizedExperiment\u5bf9\u8c61\uff0c\u8fd9\u6837\u5c31\u53ef\u4ee5\u65b9\u4fbf\u5730\u5728R\u4e2d\u8fdb\u884c\u5206\u6790\u4e86\u3002<\/p>\n\n\n\n

\u5728R\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u7b80\u5355\u7684\u4ee3\u7801\u6765\u52a0\u8f7d\u8fd9\u4e9b\u6570\u636e\uff1a<\/p>\n\n\n\n

data(NomeData)<\/p>\n\n\n\n

# NomeData <- readRDS(file = “NomeData.rds”) #\u76f4\u63a5\u8bfb\u53d6\u5c0f\u5e08\u59b9\u51c6\u5907\u597d\u7684\u6570\u636e<\/p>\n\n\n\n

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

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

\u8fd9\u4e2aNomeData\u5bf9\u8c61\u5305\u542b\u4e86219\u4e2a\u611f\u5174\u8da3\u533a\u57df\uff08ROIs\uff09\u7684\u4fe1\u606f\uff0c\u5e76\u4e14\u6bcf\u4e2aROI\u90fd\u5bf9\u5e94\u77404\u4e2a\u6837\u672c\u7684\u6570\u636e\uff1a\u4e24\u4e2aADNP\u6572\u9664\u6837\u672c\u548c\u4e24\u4e2a\u91ce\u751f\u578b\u6837\u672c\u3002\u8fd9\u4e9bROIs\u90fd\u662f\u56f4\u7ed5\u8f6c\u5f55\u56e0\u5b50\u57fa\u5e8f\u4e2d\u5fc3\u7684\uff0c\u5e76\u4e14\u957f\u5ea6\u4e00\u81f4\uff0c\u8fd9\u6837\u6211\u4eec\u5c31\u53ef\u4ee5\u89c2\u5bdf\u5230\u7532\u57fa\u5316\u4fdd\u62a4\u7684\u4e0d\u5bf9\u79f0\u6027\u4e86\u3002<\/p>\n\n\n\n

\u6bcf\u4e2a\u6837\u672c\u7684\u7532\u57fa\u5316\u6570\u636e\u90fd\u5b58\u50a8\u5728reads\u6d4b\u5b9a\u4e2d\uff0c\u8fd9\u662f\u4e00\u4e2a\u5305\u542bGPos\u5bf9\u8c61\u7684\u7a00\u758f\u903b\u8f91\u77e9\u9635\u3002\u8fd9\u4e9b\u6570\u636e\u544a\u8bc9\u6211\u4eec\u6bcf\u4e2a\u57fa\u56e0\u7ec4\u4f4d\u7f6e\u4e0a\u7684\u7532\u57fa\u5316\u4fdd\u62a4\u60c5\u51b5\uff0c\u662f\u88ab\u4fdd\u62a4\u4e86\u8fd8\u662f\u88ab\u7532\u57fa\u5316\u4e86\u3002<\/p>\n\n\n\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u6765\u770b\u770breads\u6d4b\u5b9a\u4e2d\u5305\u542b\u7684GPos\u5bf9\u8c61\uff0c\u5b83\u5728\u4e24\u4e2a\u7a00\u758f\u903b\u8f91\u77e9\u9635\u4e2d\u5305\u542b\u4e86GCH\u7532\u57fa\u5316\u6570\u636e\uff0c\u4e00\u4e2a\u7528\u4e8e\u4fdd\u62a4\u514d\u53d7\u7532\u57fa\u5316\uff0c\u53e6\u4e00\u4e2a\u7528\u4e8e\u7532\u57fa\u5316\u3002<\/p>\n\n\n\n

assays(NomeData)[[“reads”]][1, 1]<\/p>\n\n\n\n

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

\u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u6211\u4eec\u5c31\u80fd\u591f\u52a0\u8f7d\u5e76\u51c6\u5907\u597dNOMe-seq\u6570\u636e\uff0c\u4e3a\u63a5\u4e0b\u6765\u7684\u5206\u6790\u6253\u4e0b\u575a\u5b9e\u7684\u57fa\u7840\u3002\u5982\u679c\u4f60\u5bf9\u8fd9\u4e2a\u8fc7\u7a0b\u6709\u4efb\u4f55\u7591\u95ee\uff0c\u968f\u65f6\u53ef\u4ee5\u8054\u7cfb\u5c0f\u5e08\u59b9\u54e6\uff01<\/p>\n\n\n\n

\u7ed8\u5236<\/strong>meta<\/strong> <\/strong>plots<\/strong><\/strong><\/p>\n\n\n\n

\u5982\u679c\u60f3\u8981\u6bd4\u8f83\u5728REST\u3001CTCF\u6216ADNP\u8fd9\u4e9b\u8f6c\u5f55\u56e0\u5b50\u7ed3\u5408\u4f4d\u70b9\u4e2d\u5fc3\u7684ROIs\u4e4b\u95f4\u7684\u5dee\u5f02\uff0c\u53ef\u4ee5\u751f\u6210\u4e00\u79cd\u53eb\u505a\u201cmeta plots\u201d\u7684\u56fe\uff0c\u8fd9\u79cd\u56fe\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u89c2\u5bdf\u4e0d\u540c\u6837\u672c\u95f4\u5728\u8fd9\u4e9b\u7279\u5b9a\u533a\u57df\u7684\u7532\u57fa\u5316\u4fdd\u62a4\u6a21\u5f0f\u3002<\/p>\n\n\n\n

\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u6765\u81ea\u91ce\u751f\u578b\u5c0f\u9f20ES\u7ec6\u80de\u7684\u4e24\u4e2a\u6837\u672c\uff0c\u4ee5\u53ca\u6765\u81eaADNP\u6572\u9664\u5c0f\u9f20ES\u7ec6\u80de\u7684\u4e24\u4e2a\u6837\u672c\u3002\u5982\u679c\u67d0\u4e2aROI-\u6837\u672c\u7ec4\u5408\u7684\u8bfb\u6570\u5c11\u4e8e10\u4e2a\uff0c\u6211\u4eec\u4f1a\u5c06\u5176\u6392\u9664\u5728\u5916\u3002<\/p>\n\n\n\n

\u4e0b\u9762\u662f\u751f\u6210\u8fd9\u4e9b\u56fe\u7684R\u4ee3\u7801\uff1a<\/p>\n\n\n\n

avePlotData <- metaPlots(NomeData = NomeData, nr = 10, ROIgroup = “motif”)<\/p>\n\n\n\n

# \u7ed8\u5236\u5e73\u5747\u56fe<\/p>\n\n\n\n

ggplot(avePlotData, aes(x = position, y = protection)) +<\/p>\n\n\n\n

    geom_point(alpha = 0.5) +<\/p>\n\n\n\n

    geom_line(aes(x = position, y = loess), col = “darkblue”, lwd = 2) +<\/p>\n\n\n\n

    theme_classic() +<\/p>\n\n\n\n

    facet_grid(rows = vars(type), cols = vars(sample), scales = “free”) +<\/p>\n\n\n\n

    ylim(c(0, 100)) +<\/p>\n\n\n\n

    geom_hline(<\/p>\n\n\n\n

        yintercept = c(10, 20, 30, 40, 50, 60, 70, 80, 90),<\/p>\n\n\n\n

        alpha = 0.5, color = “grey”, linetype = “dashed”<\/p>\n\n\n\n

    )<\/p>\n\n\n\n

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

\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u751f\u6210\u4e00\u4e2a\u5305\u542b\u6240\u6709\u6837\u672c\u548cROIs\u7684\u56fe\uff0c\u5176\u4e2d\u6bcf\u4e2a\u70b9\u4ee3\u8868\u4e00\u4e2a\u4f4d\u7f6e\u7684\u4fdd\u62a4\u6c34\u5e73\uff0c\u800c\u84dd\u8272\u7684\u7ebf\u5219\u8868\u793a\u901a\u8fc7loess\u5e73\u6ed1\u5904\u7406\u540e\u7684\u8d8b\u52bf\u3002\u6211\u4eec\u8fd8\u5728\u56fe\u4e2d\u6dfb\u52a0\u4e86\u6c34\u5e73\u7684\u865a\u7ebf\uff0c\u4ee5\u4fbf\u4e8e\u89c2\u5bdf\u4fdd\u62a4\u6c34\u5e73\u7684\u53d8\u5316\u3002<\/p>\n\n\n\n

\u4ece\u56fe\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u89c2\u5bdf\u5230REST\u548cCTCF\u7ed3\u5408\u4f4d\u70b9\u5468\u56f4\u7684NOMe\u8db3\u8ff9\u6ca1\u6709\u53d8\u5316\uff0c\u4f46\u5728ADNP\u7ed3\u5408\u4f4d\u70b9\u5468\u56f4\uff0c\u91ce\u751f\u578b\u548cADNP\u6572\u9664\u7ec6\u80de\u4e4b\u95f4\u5b58\u5728\u660e\u663e\u7684\u5dee\u5f02\u3002\u8fd9\u610f\u5473\u7740ADNP\u7684\u7f3a\u5931\u53ef\u80fd\u4f1a\u5f71\u54cd\u8fd9\u4e9b\u4f4d\u70b9\u7684\u6838\u5c0f\u4f53\u4fdd\u62a4\u6a21\u5f0f\u3002<\/p>\n\n\n\n

\u4e94\u79cd\u67d3\u8272\u8d28\u6a21\u5f0f\u7684\u7247\u6bb5\u8ba1\u6570<\/strong><\/strong><\/p>\n\n\n\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u6765\u804a\u804a\u5982\u4f55\u786e\u5b9a\u4e94\u79cd\u67d3\u8272\u8d28\u6a21\u5f0f\u7684\u7247\u6bb5\u8ba1\u6570\uff1a\u8f6c\u5f55\u56e0\u5b50\u7ed3\u5408\u4f4d\u70b9\uff08TF\uff09\u3001\u5f00\u653e\u67d3\u8272\u8d28\u3001\u4e0a\u6e38\u6838\u5c0f\u4f53\uff08upNuc\uff09\u3001\u4e0b\u6e38\u6838\u5c0f\u4f53\uff08downNuc\uff09\u548c\u666e\u901a\u6838\u5c0f\u4f53\uff08Nuc\uff09\u3002\u8fd9\u4e9b\u6a21\u5f0f\u6709\u52a9\u4e8e\u6211\u4eec\u7406\u89e3\u57fa\u56e0\u7ec4\u4e2d\u4e0d\u540c\u533a\u57df\u7684\u529f\u80fd\u72b6\u6001\u3002<\/p>\n\n\n\n

\u9996\u5148\uff0c\u6211\u4eec\u91c7\u7528\u4e86S\u00f6nmezer\u7b49\u4eba\u57282021\u5e74\u7684\u7814\u7a76\u65b9\u6cd5\uff0c\u5e76\u7a0d\u4f5c\u4fee\u6539\u3002\u6211\u4eec\u6839\u636e\u56f4\u7ed5\u57fa\u5e8f\u4e2d\u5fc3\u7684\u4e09\u4e2a\u7a97\u53e3\uff08-50:-25\u3001-8:8\u300125:50\uff09\u6765\u5206\u7c7b\u6bcf\u4e2a\u7247\u6bb5\uff0c\u8fd9\u4e9b\u7a97\u53e3\u5bf9\u5e94\u4e8e\u63d0\u4f9b\u7684ROIs\u7684\u4e2d\u5fc3\u533a\u57df\u3002<\/p>\n\n\n\n

NomeData <- footprintCalc(NomeData)<\/p>\n\n\n\n

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

\u6267\u884c\u8fd9\u4e2a\u51fd\u6570\u540e\uff0cNomeData\u5bf9\u8c61\u4e2d\u4f1a\u65b0\u589e\u4e00\u4e2a\u540d\u4e3afootprints\u7684\u6d4b\u5b9a\uff0c\u8fd9\u4e2a\u6d4b\u5b9a\u4f1a\u5c06\u6bcf\u4e2a\u7247\u6bb5\u5f52\u7c7b\u4e3a\u4e0a\u8ff0\u4e94\u79cd\u8db3\u8ff9\u6a21\u5f0f\u4e4b\u4e00\u3002<\/p>\n\n\n\n

assays(NomeData)[[“footprints”]][[1, 1]][1:10]<\/p>\n\n\n\n

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

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u7edf\u8ba1\u6bcf\u4e2a\u6837\u672c-ROI\u7ec4\u5408\u4e2d\u652f\u6301\u6bcf\u79cd\u8db3\u8ff9\u7c7b\u522b\u7684\u7247\u6bb5\u6570\u91cf\u3002\u8fd9\u4f1a\u5728NomeData\u4e2d\u4e3a\u6bcf\u79cd\u8db3\u8ff9\u7c7b\u522b\u65b0\u589e\u4e00\u4e2a\u6d4b\u5b9a\uff0c\u5e76\u4e14\u8fd8\u4f1a\u65b0\u589e\u4e00\u4e2a\u5305\u542b\u6240\u6709\u8db3\u8ff9\u7c7b\u522b\u5408\u5e76\u8ba1\u6570\u7684\u6d4b\u5b9a\uff08\u79f0\u4e3a\u201call\u201d\uff09\u3002<\/p>\n\n\n\n

NomeData <- footprintQuant(NomeData)<\/p>\n\n\n\n

assays(NomeData)[7:12]<\/p>\n\n\n\n

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

\u8bf7\u6ce8\u610f\uff0c\u5982\u679c\u4e00\u4e2a\u7247\u6bb5\u5728\u6240\u6709\u4e09\u4e2a\u7528\u4e8e\u5206\u7c7b\u7684\u7a97\u53e3\u4e2d\u90fd\u6ca1\u6709\u7532\u57fa\u5316\u4fdd\u62a4\u6570\u636e\uff0c\u90a3\u4e48\u8fd9\u4e2a\u7247\u6bb5\u5c06\u4e0d\u4f1a\u88ab\u4f7f\u7528\u3002\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u6211\u4eec\u5f97\u5230\u7684\u6570\u636e\u662f\u51c6\u786e\u548c\u53ef\u9760\u7684\u3002<\/p>\n\n\n\n

\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u8fd9\u4e9b\u6570\u636e\u6765\u6bd4\u8f83ADNP\u6572\u9664\u548c\u91ce\u751f\u578b\u6837\u672c\u4e4b\u95f4\u8db3\u8ff9\u7684\u4e30\u5ea6\u5dee\u5f02\u3002\u8fd9\u6709\u52a9\u4e8e\u6211\u4eec\u7406\u89e3ADNP\u57fa\u56e0\u7684\u53d8\u5316\u5982\u4f55\u5f71\u54cd\u67d3\u8272\u8d28\u7684\u7ed3\u6784\u548c\u529f\u80fd\u3002<\/p>\n\n\n\n

\u6bd4\u8f83ADNP\u6572\u9664\u4e0e\u91ce\u751f\u578b\u6837\u672c\u7684NOMe-seq\u8db3\u8ff9\u4e30\u5ea6\u5dee\u5f02<\/strong><\/p>\n\n\n\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8981\u4f7f\u7528edgeR\u5305\u6765\u5206\u6790ADNP\u6572\u9664\uff08KO\uff09\u548c\u91ce\u751f\u578b\uff08WT\uff09\u6837\u672c\u4e4b\u95f4\u6bcf\u79cd\u8db3\u8ff9\u7c7b\u578b\u7684\u7247\u6bb5\u8ba1\u6570\u4e0e\u603b\u7247\u6bb5\u8ba1\u6570\u7684\u5dee\u5f02\u3002\u6211\u4eec\u4f1a\u8ba1\u7b97\u603b\u7247\u6bb5\u8ba1\u6570\u7684TMM\uff08Trimmed Mean of M-values\uff09\u6807\u51c6\u5316\u5e93\u5927\u5c0f\u3002<\/p>\n\n\n\n

\u9996\u5148\uff0c\u6211\u4eec\u4f7f\u7528`diNOMeTest`\u51fd\u6570\u6765\u6bd4\u8f83ADNP\u6572\u9664\u548c\u91ce\u751f\u578b\u6837\u672c\u4e4b\u95f4\u7684\u5dee\u5f02\uff1a<\/p>\n\n\n\n

res <- diNOMeTest(NomeData,<\/p>\n\n\n\n

    WTsamples = c(“WT_1”, “WT_2”),<\/p>\n\n\n\n

    KOsamples = c(“AdnpKO_1”, “AdnpKO_2”))<\/p>\n\n\n\n

\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u8f93\u51fa\u4e00\u4e2a\u5305\u542b\u5dee\u5f02\u5206\u6790\u7ed3\u679c\u7684\u6570\u636e\u6846\uff0c\u663e\u793a\u4e86\u6bcf\u79cd\u8db3\u8ff9\u7c7b\u578b\u5728ADNP\u6572\u9664\u548c\u91ce\u751f\u578b\u6837\u672c\u4e4b\u95f4\u7684\u5bf9\u6570\u500d\u6570\u53d8\u5316\uff08logFC\uff09\u3001\u5bf9\u6570\u6807\u51c6\u5316\u4e30\u5ea6\uff08logCPM\uff09\u3001F\u7edf\u8ba1\u91cf\u3001P\u503c\u3001FDR\uff08false discovery rate\uff09\u4ee5\u53ca\u5bf9\u6bd4\u7ec4\u3002<\/p>\n\n\n\n

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

\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u7b80\u5355\u5730\u7ed8\u5236\u6bcf\u79cdROI\u7c7b\u578b\u4e2d\u53d7\u8c03\u63a7\u7684ROI\u6570\u91cf\uff1a<\/p>\n\n\n\n

res %>%<\/p>\n\n\n\n

    group_by(contrasts, motif, regulated) %>%<\/p>\n\n\n\n

    summarize(n = n()) %>%<\/p>\n\n\n\n

    ggplot(aes(x = motif, y = n, fill = regulated)) +<\/p>\n\n\n\n

    geom_bar(stat = “identity”) +<\/p>\n\n\n\n

    facet_grid(~contrasts) +<\/p>\n\n\n\n

    theme_bw() +<\/p>\n\n\n\n

    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +<\/p>\n\n\n\n

    scale_fill_manual(values = c(“orange”, “grey”, “blue3”))<\/p>\n\n\n\n

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

\u6216\u8005\uff0c\u6211\u4eec\u53ef\u4ee5\u7528MA\u56fe\u6765\u5c55\u793a\u7ed3\u679c\uff1a<\/p>\n\n\n\n

ggplot(res, aes(y = logFC, x = logCPM, col = regulated)) +<\/p>\n\n\n\n

    geom_point() +<\/p>\n\n\n\n

    facet_grid(~contrasts) +<\/p>\n\n\n\n

    theme_bw() +<\/p>\n\n\n\n

    scale_color_manual(values = c(“orange”, “grey”, “blue3”))<\/p>\n\n\n\n

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

\u8fd9\u4e9b\u56fe\u8868\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u770b\u5230ADNP\u6572\u9664\u548c\u91ce\u751f\u578b\u6837\u672c\u4e4b\u95f4\u5728\u4e0d\u540c\u8db3\u8ff9\u7c7b\u578b\u4e0a\u7684\u4e30\u5ea6\u5dee\u5f02\u3002\u901a\u8fc7\u8fd9\u4e9b\u5206\u6790\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u6e05\u695a\u5730\u4e86\u89e3ADNP\u57fa\u56e0\u5bf9\u67d3\u8272\u8d28\u8db3\u8ff9\u6a21\u5f0f\u7684\u5f71\u54cd\u3002<\/p>\n\n\n\n

\u63a2\u7d22ADNP\u6572\u9664\u4e0e\u91ce\u751f\u578b\u6837\u672c\u7684\u67d3\u8272\u8d28\u8db3\u8ff9\u5dee\u5f02<\/strong><\/strong><\/p>\n\n\n\n

\u9996\u5148\uff0c\u6211\u4eec\u8981\u8ba1\u7b97\u5728ADNP\u6572\u9664\u548c\u91ce\u751f\u578b\u6837\u672c\u4e2d\uff0c\u6bcf\u79cd\u8db3\u8ff9\u7c7b\u578b\u7684\u7247\u6bb5\u6240\u5360\u7684\u767e\u5206\u6bd4\u3002\u8fd9\u5c06\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u4e0d\u540c\u8db3\u8ff9\u7c7b\u578b\u5728\u6837\u672c\u4e2d\u7684\u76f8\u5bf9\u4e30\u5ea6\u3002<\/p>\n\n\n\n

footprint_percentages <- footprintPerc(NomeData)<\/p>\n\n\n\n

fpPercHeatmap(footprint_percentages)<\/p>\n\n\n\n

\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u751f\u6210\u4e00\u4e2a\u70ed\u56fe\uff0c\u5c55\u793a\u91ce\u751f\u578b\u548cADNP\u6572\u9664\u6837\u672c\u4e2d\u6bcf\u79cd\u8db3\u8ff9\u7c7b\u578b\u7684\u767e\u5206\u6bd4\u3002\u901a\u8fc7\u8fd9\u4e2a\u70ed\u56fe\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u89c2\u5730\u6bd4\u8f83\u4e24\u7ec4\u6837\u672c\u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/p>\n\n\n\n

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

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8981\u6bd4\u8f83ADNP\u6572\u9664\u548c\u91ce\u751f\u578b\u6837\u672c\u4e4b\u95f4\u8db3\u8ff9\u767e\u5206\u6bd4\u7684\u5dee\u5f02\uff0c\u5e76\u8fdb\u884c\u663e\u8457\u6027\u6d4b\u8bd5\u3002<\/p>\n\n\n\n

compareFootprints(footprint_percentages, res,<\/p>\n\n\n\n

    WTsamples = c(“WT_1”, “WT_2”),<\/p>\n\n\n\n

    KOsamples = c(“AdnpKO_1”, “AdnpKO_2”), plotcols =<\/p>\n\n\n\n

        c(“#f03b20”, “#a8ddb5”, “#bdbdbd”))<\/p>\n\n\n\n

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

\u901a\u8fc7\u8fd9\u4e2a\u6bd4\u8f83\uff0c\u6211\u4eec\u53ef\u4ee5\u53d1\u73b0\u5728ADNP\u6572\u9664\u6837\u672c\u4e2d\uff0c\u56f4\u7ed5ADNP\u57fa\u5e8f\u7684\u8f6c\u5f55\u56e0\u5b50\u8db3\u8ff9\u663e\u8457\u589e\u52a0\uff0c\u800c\u6838\u5c0f\u4f53\u8db3\u8ff9\u5219\u51cf\u5c11\u3002\u8fd9\u8868\u660eADNP\u57fa\u56e0\u7684\u7f3a\u5931\u53ef\u80fd\u4f1a\u5f71\u54cd\u8fd9\u4e9b\u533a\u57df\u7684\u67d3\u8272\u8d28\u72b6\u6001\u3002<\/p>\n\n\n\n

\u5408\u5e76\u6838\u5c0f\u4f53\u6a21\u5f0f<\/strong><\/p>\n\n\n\n

\u5982\u679c\u6211\u4eec\u5bf9\u4e0a\u6e38\u548c\u4e0b\u6e38\u6838\u5c0f\u4f53\u6a21\u5f0f\u4e0d\u611f\u5174\u8da3\uff0c\u800c\u662f\u5e0c\u671b\u5c06\u6240\u6709\u6838\u5c0f\u4f53\u6a21\u5f0f\u7247\u6bb5\u4fdd\u7559\u5728\u6838\u5c0f\u4f53\u7ec4\u5185\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e`combineNucCounts = TRUE`\u6765\u5b9e\u73b0\u3002<\/p>\n\n\n\n

res <- diNOMeTest(NomeData,<\/p>\n\n\n\n

    WTsamples = c(“WT_1”, “WT_2”),<\/p>\n\n\n\n

    KOsamples = c(“AdnpKO_1”, “AdnpKO_2”), combineNucCounts = TRUE<\/p>\n\n\n\n

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

footprint_percentages <- footprintPerc(NomeData, combineNucCounts = TRUE)<\/p>\n\n\n\n

compareFootprints(footprint_percentages, res,<\/p>\n\n\n\n

    WTsamples = c(“WT_1”, “WT_2”),<\/p>\n\n\n\n

    KOsamples = c(“AdnpKO_1”, “AdnpKO_2”), plotcols =<\/p>\n\n\n\n

        c(“#f03b20”, “#a8ddb5”, “#bdbdbd”))<\/p>\n\n\n\n

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

\u8fd9\u6837\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5c06\u6240\u6709\u6838\u5c0f\u4f53\u6a21\u5f0f\u7247\u6bb5\u5408\u5e76\u5728\u4e00\u8d77\uff0c\u4ee5\u4fbf\u66f4\u5168\u9762\u5730\u5206\u6790\u6838\u5c0f\u4f53\u6a21\u5f0f\u7684\u53d8\u5316\u3002\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u7b80\u5316\u5206\u6790\u8fc7\u7a0b\uff0c\u540c\u65f6\u63d0\u4f9b\u66f4\u76f4\u89c2\u7684\u6838\u5c0f\u4f53\u6a21\u5f0f\u53d8\u5316\u89c6\u56fe\u3002<\/p>\n\n\n\n

\u901a\u8fc7\u4eca\u5929\u7684\u6559\u7a0b\uff0c\u6211\u4eec\u4e00\u8d77\u63a2\u7d22\u4e86\u5982\u4f55\u4f7f\u7528dinoR\u5305\u6765\u5206\u6790ADNP\u6572\u9664\u4e0e\u91ce\u751f\u578b\u6837\u672c\u95f4\u7684\u67d3\u8272\u8d28\u8db3\u8ff9\u5dee\u5f02\u3002\u5e0c\u671b\u8fd9\u4e9b\u5206\u6790\u65b9\u6cd5\u80fd\u4e3a\u4f60\u7684\u7814\u7a76\u5e26\u6765\u65b0\u7684\u6d1e\u89c1\u3002\u5982\u679c\u4f60\u5728\u5206\u6790\u8fc7\u7a0b\u4e2d\u9047\u5230\u4efb\u4f55\u96be\u9898\uff0c\u8bb0\u5f97\u627e\u5c0f\u5e08\u59b9\u5e2e\u5fd9\u54e6\uff01<\/p>\n\n\n\n

\u65e0\u8bba\u4f60\u662f\u5728\u4f18\u5316\u4ee3\u7801\uff0c\u8fd8\u662f\u5728\u4e91\u7aef\u8fdb\u884c\u4fbf\u6377\u7684\u5206\u6790\uff0c\u4e91\u751f\u4fe1\u795e\u5668\u90fd\u80fd\u4e3a\u4f60\u63d0\u4f9b\u5f3a\u5927\u7684\u652f\u6301\u3002\u6b22\u8fce\u8bd5\u8bd5\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u795e\u5668\uff0c\u53ea\u9700\u4e00\u952e\u4e0a\u4f20\u6570\u636e\uff0c\u60f3\u8981\u7684\u56fe\u5c31\u80fd\u8f7b\u677eget~<\/strong><\/strong><\/p>\n\n\n\n

\u4e91\u751f\u4fe1\u5e73\u53f0\u94fe\u63a5\uff1a<\/strong>http:\/\/www.biocloudservice.com\/home.html<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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