{"id":61171,"date":"2024-10-17T13:57:32","date_gmt":"2024-10-17T05:57:32","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61171"},"modified":"2024-10-17T13:57:32","modified_gmt":"2024-10-17T05:57:32","slug":"%e5%ba%8f%e5%88%97%e6%af%94%e5%af%b9%e8%bd%bb%e6%9d%be%e4%b8%8a%e6%89%8b%ef%bc%81r%e8%af%ad%e8%a8%80%e5%ae%9e%e7%8e%b0%e5%a4%9a%e6%a0%b7%e5%8c%96%e7%9a%84%e5%ba%8f%e5%88%97%e6%af%94%e5%af%b9%e5%8f%8a","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61171","title":{"rendered":"\u5e8f\u5217\u6bd4\u5bf9\u8f7b\u677e\u4e0a\u624b\uff01R\u8bed\u8a00\u5b9e\u73b0\u591a\u6837\u5316\u7684\u5e8f\u5217\u6bd4\u5bf9\u53ca\u76f8\u5173\u53ef\u89c6\u5316\u7684\u653b\u7565\u8be6\u89e3\u6765\u5566\uff01"},"content":{"rendered":"\n

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

\u5927\u5bb6\u597d\uff01\u6211\u662f\u5c0f\u679c\uff01\u5c0f\u4f19\u4f34\u4eec\uff0c\u662f\u4e0d\u662f\u5728\u8fdb\u884c\u751f\u7269\u6570\u636e\u5206\u6790\u65f6\uff0c\u7ecf\u5e38\u56e0\u4e3a\u5e8f\u5217\u6bd4\u5bf9\u7684\u95ee\u9898\u800c\u611f\u5230\u82e6\u607c\u5462\uff1f\u590d\u6742\u7684\u5e8f\u5217\u6570\u636e\u3001\u7e41\u7410\u7684\u6bd4\u5bf9\u8fc7\u7a0b\uff0c\u603b\u662f\u8ba9\u6211\u4eec\u611f\u5230\u529b\u4e0d\u4ece\u5fc3\u3002\u4eca\u5929\u5c0f\uff0c\u679c\u5c06\u5e26\u5927\u5bb6\u7528Biostrings R\u5305\u7684\u51fd\u6570\u6765\u89e3\u51b3\u8fd9\u4e2a\u96be\u9898\uff0c\u638c\u63e1\u672c\u671f\u5185\u5bb9\uff0c\u5e8f\u5217\u6bd4\u5bf9\u7684\u95ee\u9898\u90fd\u5c06\u8fce\u5203\u800c\u89e3\uff01Biostrings R\u5305\u63d0\u4f9b\u4e86\u975e\u5e38\u5f3a\u5927\u7684\u5e8f\u5217\u6bd4\u5bf9\u529f\u80fd\uff0c\u4e0d\u4ec5\u652f\u6301\u591a\u6837\u5316\u7684\u6bd4\u5bf9\u7b97\u6cd5\uff0c\u8fd8\u80fd\u8f7b\u677e\u5b9e\u73b0\u6bd4\u5bf9\u7ed3\u679c\u7684\u53ef\u89c6\u5316\u3002\u5c0f\u4f19\u4f34\u4eec\u65e0\u9700\u518d\u4e3a\u590d\u6742\u7684\u6bd4\u5bf9\u8fc7\u7a0b\u800c\u70e6\u607c\uff0c\u53ea\u9700\u51e0\u4e2a\u7b80\u5355\u7684\u51fd\u6570\u8c03\u7528\uff0c\u5c31\u80fd\u8f7b\u677e\u5b8c\u6210\u5e8f\u5217\u6bd4\u5bf9\u4efb\u52a1\u3002\u5feb\u6765\u4f53\u9a8cBiostrings R\u5305\u7684\u5f3a\u5927\u9b45\u529b\u5427\uff0c\u8ba9\u6211\u4eec\u4e00\u8d77\u544a\u522b\u5e8f\u5217\u6bd4\u5bf9\u7684\u70e6\u607c\uff0c\u8f7b\u677e\u63a2\u7d22\u751f\u7269\u6570\u636e\u7684\u5965\u79d8\uff01\u540c\u65f6\u5462\uff0c\u5341\u5e74\u7684\u751f\u4fe1\u4e4b\u8def\uff0c\u5c0f\u679c\u5df2\u7ecf\u7ec3\u5c31\u4e86\u4e00\u8eab\u624e\u5b9e\u7684\u672c\u9886\uff0c\u73b0\u5728\u51c6\u5907\u7528\u8fd9\u8eab\u672c\u4e8b\u4e3a\u5c0f\u4f19\u4f34\u4eec\u670d\u52a1\u5566\uff01\u5982\u679c\u4f60\u5728\u751f\u4fe1\u5206\u6790\u4e0a\u9047\u5230\u4e86\u96be\u9898\uff0c\u90a3\u5c31\u6765\u627e\u5c0f\u679c\u5427\uff01\u5c0f\u679c\u4f1a\u7528\u81ea\u5df1\u7684\u4e13\u4e1a\u77e5\u8bc6\u548c\u6280\u80fd\uff0c\u4e3a\u4f60\u89e3\u51b3\u56f0\u6270\uff0c\u52a9\u4f60\u4e00\u81c2\u4e4b\u529b\u3002\u671f\u5f85\u4f60\u7684\u8054\u7cfb\u54e6~<\/strong><\/strong><\/p>\n\n\n\n

\u4e00\u3001 <\/strong>\u7528\u5230\u7684\u5305\u7b80\u8981\u4ecb\u7ecd<\/strong><\/strong><\/h3>\n\n\n\n
    \n
  • Biostrings<\/em><\/strong><\/em><\/strong>  R\u5305<\/em><\/strong><\/em><\/strong><\/em><\/strong><\/em><\/strong><\/li>\n<\/ul>\n\n\n\n

    Biostrings\uff0c\u5b83\u4f5c\u4e3aR\u8bed\u8a00\u4e2d\u7684\u4e00\u4e2a\u6838\u5fc3\u751f\u7269\u4fe1\u606f\u5b66\u5305\uff0c\u4e3a\u5904\u7406\u548c\u5206\u6790\u751f\u7269\u5e8f\u5217\u6570\u636e\u63d0\u4f9b\u4e86\u5f3a\u6709\u529b\u7684\u652f\u6301\u3002Biostrings\u7684\u6838\u5fc3\u529f\u80fd\u4e4b\u4e00\u662f\u5904\u7406\u5404\u79cd\u751f\u7269\u5e8f\u5217\u6570\u636e\uff0c\u5305\u62ecDNA\u3001RNA\u548c\u86cb\u767d\u8d28\u5e8f\u5217\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u6765\u8bfb\u53d6\u3001\u7f16\u8f91\u3001\u5408\u5e76\u548c\u8f6c\u6362\u8fd9\u4e9b\u5e8f\u5217\uff0c\u8ba9\u6211\u4eec\u80fd\u591f\u8f7b\u677e\u5730\u5bf9\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u548c\u6574\u7406\u3002\u9664\u4e86\u5e8f\u5217\u5904\u7406\u529f\u80fd\u5916\uff0cBiostrings\u8fd8\u5177\u5907\u5f3a\u5927\u7684\u5e8f\u5217\u6bd4\u5bf9\u80fd\u529b\u3002\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u6bd4\u5bf9\u7b97\u6cd5\uff0c\u5305\u62ec\u5168\u5c40\u6bd4\u5bf9\u3001\u5c40\u90e8\u6bd4\u5bf9\u548c\u534a\u5168\u5c40\u6bd4\u5bf9\u7b49\uff0c\u4ee5\u6ee1\u8db3\u4e0d\u540c\u7814\u7a76\u9700\u6c42\u3002\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528Biostrings\u4e2d\u7684\u51fd\u6570\u6765\u641c\u7d22\u7279\u5b9a\u5e8f\u5217\u5728\u57fa\u56e0\u7ec4\u4e2d\u7684\u4f4d\u7f6e\uff0c\u5e76\u751f\u6210\u76f8\u5e94\u7684\u53ef\u89c6\u5316\u56fe\u8868\u3002<\/p>\n\n\n\n

      \n
    • ggplot2<\/em><\/strong><\/em><\/strong> R\u5305<\/em><\/strong><\/em><\/strong><\/em><\/strong><\/em><\/strong><\/li>\n<\/ul>\n\n\n\n

      ggplot2\uff0c\u6211\u4eec\u7684\u8001\u670b\u53cb~\u975e\u5e38\u5f3a\u5927\u7684\u7ed8\u56fe\u5305\uff0c\u57fa\u4e8eWilkinson\u7684\u300aThe Grammar of Graphics\u300b\u4e00\u4e66\u4e2d\u63d0\u51fa\u7684\u56fe\u5f62\u8bed\u6cd5\u7406\u8bba\u800c\u5f00\u53d1\uff0c\u4f5c\u8005\u662fHadley Wickham\u3002ggplot2\u7684\u8bbe\u8ba1\u601d\u60f3\u662f\u5c06\u6570\u636e\u96c6\u6620\u5c04\u5230\u56fe\u5f62\u5c5e\u6027\u4e0a\uff0c\u5982\u989c\u8272\u3001\u5f62\u72b6\u3001\u5927\u5c0f\u7b49\uff0c\u4ece\u800c\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u7ed8\u5236\u51fa\u6f02\u4eae\u7684\u56fe\u5f62\u3002\u5176\u6838\u5fc3\u7406\u5ff5\u662f\u5c06\u7ed8\u56fe\u4e0e\u6570\u636e\u5206\u79bb\uff0c\u6570\u636e\u76f8\u5173\u7684\u7ed8\u56fe\u4e0e\u6570\u636e\u65e0\u5173\u7684\u7ed8\u56fe\u5206\u79bb\uff0c\u662f\u6309\u56fe\u5c42\u4f5c\u56fe\uff0c\u540c\u65f6\u5b83\u4fdd\u6709\u547d\u4ee4\u5f0f\u4f5c\u56fe\u7684\u8c03\u6574\u51fd\u6570\uff0c\u4f7f\u5176\u66f4\u5177\u7075\u6d3b\u6027\u3002<\/p>\n\n\n\n

        \n
      • reshape2<\/em><\/strong><\/em><\/strong> R\u5305<\/em><\/strong><\/em><\/strong><\/em><\/strong><\/em><\/strong><\/li>\n<\/ul>\n\n\n\n

        reshape2\u4e3b\u8981\u529f\u80fd\u662f\u5728\u5bbd\u683c\u5f0f\uff08wide-format\uff09\u548c\u957f\u683c\u5f0f\uff08long-format\uff09\u4e4b\u95f4\u8f7b\u677e\u8f6c\u6362\u6570\u636e\u3002\u5bbd\u683c\u5f0f\u6570\u636e\u901a\u5e38\u6307\u7684\u662f\u6bcf\u4e2a\u53d8\u91cf\u5355\u72ec\u6210\u4e00\u5217\u7684\u6570\u636e\u7ed3\u6784\uff0c\u800c\u957f\u683c\u5f0f\u6570\u636e\u5219\u662f\u5c06\u53d8\u91cfID\u6574\u5408\u5728\u540c\u4e00\u5217\u4e2d\uff0c\u6bcf\u884c\u4ee3\u8868\u4e00\u4e2a\u89c2\u5bdf\u503c\uff0c\u6bcf\u5217\u4ee3\u8868\u4e00\u4e2a\u53d8\u91cf\u3002<\/p>\n\n\n\n

        reshape2\u5305\u4e3b\u8981\u6709\u4e24\u4e2a\u6838\u5fc3\u51fd\u6570\uff1amelt()\u548ccast()\uff08\u6216\u8005dcast()\uff0c\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u4f7f\u7528\u60c5\u51b5\uff09\u3002<\/p>\n\n\n\n

          \n
        • melt()\u51fd\u6570\u7528\u4e8e\u5c06\u6570\u636e\u4ece\u5bbd\u683c\u5f0f\u8f6c\u6362\u4e3a\u957f\u683c\u5f0f\u3002\u53ef\u4ee5\u60f3\u8c61\u6210\u6211\u4eec\u5728\u5904\u7406\u91d1\u5c5e\uff0c\u5f53\u7194\u5316\u91d1\u5c5e\u6210\u6db2\u4f53\u6ef4\u4e0b\u65f6\uff0c\u91d1\u5c5e\u4f1a\u88ab\u62c9\u957f\uff08long-format\uff09\u3002<\/li>\n\n\n\n
        • cast()\u6216dcast()\u51fd\u6570\u5219\u7528\u4e8e\u5c06\u957f\u683c\u5f0f\u6570\u636e\u201c\u91cd\u94f8\u201d\u6210\u5bbd\u683c\u5f0f\u6570\u636e\u3002\u5982\u679c\u6211\u4eec\u628a\u91d1\u5c5e\u94f8\u6210\u4e00\u4e2a\u6a21\u5b50\uff0c\u5b83\u5c31\u4f1a\u53d8\u5bbd\uff08wide-format\uff09\u3002<\/li>\n\n\n\n
        • circlize<\/em><\/strong><\/em><\/strong> R\u5305<\/em><\/strong><\/em><\/strong><\/em><\/strong><\/em><\/strong><\/li>\n<\/ul>\n\n\n\n

          circlize\u662f\u4e00\u4e2a\u7528\u4e8e\u7ed8\u5236\u73af\u5f62\u56fe\u7684R\u8bed\u8a00\u5305\uff0c\u7531\u5fb7\u56fd\u764c\u75c7\u4e2d\u5fc3\u7684\u534e\u4eba\u535a\u58ebZuguang Gu\u5f00\u53d1\u3002\u5b83\u5e76\u4e0d\u662f\u751f\u6210Circos\u914d\u7f6e\u6587\u4ef6\u7684\u524d\u7aef\u5c01\u88c5\uff0c\u800c\u662f\u5b8c\u5168\u57fa\u4e8eR\u8bed\u8a00\u98ce\u683c\u7684\u7edf\u8ba1\u548c\u56fe\u5f62\u8bed\u6cd5\u5b9e\u73b0\u7684\u3002circlize\u7684\u8bbe\u8ba1\u539f\u7406\u5728\u4e8e\u5176\u5706\u5f62\u5e03\u5c40\u7531sectors\uff08\u6247\u5f62\uff09\u548ctracks\uff08\u8f68\u8ff9\uff09\u7ec4\u6210\uff0c\u5bf9\u4e8e\u6570\u636e\u4e2d\u7684\u4e0d\u540c\u5206\u7c7b\uff0c\u5b83\u4eec\u4f1a\u4f5c\u4e3a\u4e0d\u540c\u7684sectors\uff0c\u800c\u540c\u4e00\u4e2a\u7c7b\u522b\u7684\u4e0d\u540c\u7ef4\u5ea6\u7684\u89c2\u6d4b\u503c\uff0c\u5219\u4f1a\u4f5c\u4e3a\u4ece\u5706\u5916\u5411\u5706\u5185\u4e0d\u65ad\u5806\u53e0\u7684\u56fe\u5f62\u8f68\u8ff9tracks\u3002<\/p>\n\n\n\n

          \u4e8c\u3001 <\/strong>\u4ee3\u7801\u5b9e\u64cd<\/strong><\/strong><\/h3>\n\n\n\n

          \u5e9f\u8bdd\u4e0d\u591a\u8bf4\uff0c\u5c0f\u679c\u76f4\u63a5\u5e26\u5c0f\u4f19\u4f34\u4eec\u6765\u5b9e\u64cd\u4ee3\u7801\uff0c\u8fdb\u884c\u5e8f\u5217\u5206\u6790\u7684\u5168\u9762\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528R\u8bed\u8a00\u8fdb\u884c\u5e8f\u5217\u6bd4\u5bf9\u548c\u53ef\u89c6\u5316\uff01\u672c\u6b21\u4ecb\u7ecd\u7684R\u5305\u64cd\u4f5c\u4f1a\u5360\u7528\u5185\u5b58\u8f83\u5927\uff0c\u5c0f\u679c\u5efa\u8bae\u4f7f\u7528\u670d\u52a1\u5668\uff0c\u6b22\u8fce\u5c0f\u4f19\u4f34\u4eec\u8054\u7cfb\u5c0f\u679c\u79df\u8d41\u6027\u4ef7\u6bd4\u5c45\u9ad8\u7684\u670d\u52a1\u5668\uff01<\/strong><\/strong><\/p>\n\n\n\n

            \n
          • \u5e8f\u5217\u5b9a\u4e49<\/strong>\uff1a\u5148\u7528Biostrings\u5305\u5b9a\u4e49\u4e24\u4e2aDNA\u5e8f\u5217s1\u548cs2\u3002<\/li>\n<\/ul>\n\n\n\n

            library(Biostrings)<\/p>\n\n\n\n

            s1<-DNAString(“ACTTCACCAGCTCCCTGGCGGTAAGTTGATCAAAGGAAACGCAAAGTTTTCAAG”)<\/p>\n\n\n\n

            s2<-DNAString(“GTTTCACTACTTCCTTTCGGGTAAGTAAATATATAAATATATAAAAATATAATTTTCATC”)<\/p>\n\n\n\n

              \n
            • \u78b1\u57fa\u8ba1\u6570<\/strong>\uff1a\u8ba1\u7b97\u6bcf\u4e2a\u5e8f\u5217\u4e2d\u6bcf\u4e2a\u78b1\u57fa\uff08A, C, G, T\uff09\u7684\u6570\u91cf\uff0c\u5e76\u751f\u6210\u6761\u5f62\u56fe\u3002<\/li>\n<\/ul>\n\n\n\n

              base_counts_s1 <- tapply(unlist(strsplit(as.character(s1), “”)), unlist(strsplit(as.character(s1), “”)), length)<\/p>\n\n\n\n

              base_counts_s2 <- tapply(unlist(strsplit(as.character(s2), “”)), unlist(strsplit(as.character(s2), “”)), length)<\/p>\n\n\n\n

              base_counts_df1 <- data.frame(<\/p>\n\n\n\n

                Base = names(base_counts_s1),<\/p>\n\n\n\n

                Count = base_counts_s1<\/p>\n\n\n\n

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

              base_counts_df2 <- data.frame(<\/p>\n\n\n\n

                Base = names(base_counts_s2),<\/p>\n\n\n\n

                Count = base_counts_s2<\/p>\n\n\n\n

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

              library(ggplot2)<\/p>\n\n\n\n

              ggplot(base_counts_df1, aes(x = Base, y = Count)) +<\/p>\n\n\n\n

                geom_bar(stat = “identity”, fill = c(“lightblue”, “yellow”, “pink”, “purple”)) +<\/p>\n\n\n\n

                theme_minimal() +<\/p>\n\n\n\n

                labs(title = “Base Counts in s1 Sequence”, x = “Base”, y = “Count”)<\/p>\n\n\n\n

              ggplot(base_counts_df2, aes(x = Base, y = Count)) +<\/p>\n\n\n\n

                geom_bar(stat = “identity”, fill = c(“lightgreen”, “pink”, “lightblue”, “orange”)) +<\/p>\n\n\n\n

                theme_minimal() +<\/p>\n\n\n\n

                labs(title = “Base Counts in s2 Sequence”, x = “Base”, y = “Count”)<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n
              \"\"<\/figure>\n\n\n\n
                \n
              • \u5168\u5c40\u3001\u5c40\u90e8\u548c\u91cd\u53e0\u5e8f\u5217\u6bd4\u5bf9<\/strong>\uff1a\u5c0f\u679c\u4f7f\u7528pairwiseAlignment\u51fd\u6570\u8fdb\u884c\u4e09\u79cd\u4e0d\u540c\u7684\u5e8f\u5217\u6bd4\u5bf9\uff08\u5168\u5c40\u3001\u5c40\u90e8\u548c\u91cd\u53e0\uff09\uff0c\u5e76\u4f7f\u7528\u4e86\u81ea\u5b9a\u4e49\u7684\u6838\u82f7\u9178\u66ff\u6362\u77e9\u9635\u3002<\/li>\n<\/ul>\n\n\n\n

                mat <- nucleotideSubstitutionMatrix(match = 1, mismatch = -3, baseOnly = TRUE)<\/p>\n\n\n\n

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

                globalAlign <-pairwiseAlignment(s1, s2, substitutionMatrix = mat,<\/p>\n\n\n\n

                                    gapOpening = 5, gapExtension = 2)<\/p>\n\n\n\n

                localAlign <-pairwiseAlignment(s1, s2, type = “local”, substitutionMatrix = mat,<\/p>\n\n\n\n

                                    gapOpening = 5, gapExtension = 2)<\/p>\n\n\n\n

                overlapAlign <-pairwiseAlignment(s1, s2, type = “overlap”, substitutionMatrix = mat,<\/p>\n\n\n\n

                                    gapOpening = 5, gapExtension = 2)<\/p>\n\n\n\n

                  \n
                • \u8d28\u91cf\u8bc4\u5206\u548c\u6a21\u7cca\u77e9\u9635<\/strong>\uff1a\u5c0f\u679c\u5bf9\u5e8f\u5217\u8fdb\u884c\u5e26\u6709\u8d28\u91cf\u8bc4\u5206\u548c\u6a21\u7cca\u77e9\u9635\u7684\u6bd4\u5bf9\uff08\u5728\u5904\u7406\u6d4b\u5e8f\u6570\u636e\u65f6\u5f88\u6709\u7528\uff09<\/li>\n<\/ul>\n\n\n\n

                  pairwiseAlignment(s1, s2,<\/p>\n\n\n\n

                                    patternQuality = SolexaQuality(rep(c(22L, 12L), times = c(36, 18))),<\/p>\n\n\n\n

                                    subjectQuality = SolexaQuality(rep(c(22L, 12L), times = c(40, 20))),<\/p>\n\n\n\n

                                    scoreOnly = TRUE)<\/p>\n\n\n\n

                  pairwiseAlignment(s1, s2,<\/p>\n\n\n\n

                                    patternQuality = SolexaQuality(rep(c(22L, 12L), times = c(36, 18))),<\/p>\n\n\n\n

                                    subjectQuality = SolexaQuality(rep(c(22L, 12L), times = c(40, 20))),<\/p>\n\n\n\n

                                    type = “local”)<\/p>\n\n\n\n

                  mapping <- diag(4)<\/p>\n\n\n\n

                  dimnames(mapping) <- list(DNA_BASES, DNA_BASES)<\/p>\n\n\n\n

                  mapping[“C”, “T”] <- mapping[“T”, “C”] <- 1<\/p>\n\n\n\n

                  mapping[“G”, “A”] <- mapping[“A”, “G”] <- 1<\/p>\n\n\n\n

                  pairwiseAlignment(s1, s2,<\/p>\n\n\n\n

                                    patternQuality = SolexaQuality(rep(c(22L, 12L), times = c(36, 18))),<\/p>\n\n\n\n

                                    subjectQuality = SolexaQuality(rep(c(22L, 12L), times = c(40, 20))),<\/p>\n\n\n\n

                                    fuzzyMatrix = mapping,<\/p>\n\n\n\n

                                    type = “local”)<\/p>\n\n\n\n

                  pairwiseAlignment(AAString(“PAWHEAE”), AAString(“HEAGAWGHEE”),<\/p>\n\n\n\n

                                    substitutionMatrix = “BLOSUM50”,<\/p>\n\n\n\n

                                    gapOpening = 0, gapExtension = 8)<\/p>\n\n\n\n

                  \"\"<\/figure>\n\n\n\n
                  \"\"<\/figure>\n\n\n\n
                  \"\"<\/figure>\n\n\n\n
                    \n
                  • \u5e8f\u5217\u6bd4\u5bf9\u7ed3\u679c\u6253\u5370<\/strong>\uff1a\u6253\u5370\u51fa\u5168\u5c40\u6bd4\u5bf9\u3001\u5c40\u90e8\u6bd4\u5bf9\u548c\u91cd\u53e0\u6bd4\u5bf9\u7684\u7ed3\u679c\u3002<\/li>\n<\/ul>\n\n\n\n
                    \"\"<\/figure>\n\n\n\n
                      \n
                    • \u6bd4\u5bf9\u5f97\u5206<\/strong>\uff1a\u8ba1\u7b97\u5e76\u6bd4\u8f83\u4e0d\u540c\u6bd4\u5bf9\u7c7b\u578b\u7684\u5f97\u5206\uff0c\u5e76\u5c06\u7ed3\u679c\u5b58\u50a8\u5728scores_df\u6570\u636e\u6846\u4e2d\u3002<\/li>\n<\/ul>\n\n\n\n

                      globalScore <- globalAlign@score<\/p>\n\n\n\n

                      localScore <- localAlign@score<\/p>\n\n\n\n

                      overlapScore <- overlapAlign@score<\/p>\n\n\n\n

                      scores_df <- data.frame(<\/p>\n\n\n\n

                        Alignment = c(“Global”, “Local”, “Overlap”),<\/p>\n\n\n\n

                        Score = c(globalScore, localScore, overlapScore)<\/p>\n\n\n\n

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

                        \n
                      • \u5f97\u5206\u6bd4\u8f83\u56fe\u8868<\/strong>\uff1a\u4f7f\u7528ggplot2\u751f\u6210\u6761\u5f62\u56fe\u6bd4\u8f83\u4e0d\u540c\u6bd4\u5bf9\u7c7b\u578b\u7684\u5f97\u5206\u3002<\/li>\n<\/ul>\n\n\n\n

                        ggplot(scores_df, aes(x = Alignment, y = Score, fill = Alignment)) +<\/p>\n\n\n\n

                          geom_bar(stat = “summary”, fun = “mean”,color = “white”, width = 0.5) +<\/p>\n\n\n\n

                          geom_text(aes(label = Score), position = position_stack(vjust = 0.5),color=’black’)+<\/p>\n\n\n\n

                          theme_minimal() +<\/p>\n\n\n\n

                          labs(title = “Alignment Scores Comparison”, x = “Alignment Type”, y = “Score”)<\/p>\n\n\n\n

                        \"\"<\/figure>\n\n\n\n
                          \n
                        • \u5f97\u5206\u70ed\u56fe<\/strong>\uff1a\u4f7f\u7528reshape2\u5c06scores_df\u6570\u636e\u6846\u91cd\u5851\uff0c\u7136\u540e\u7528ggplot2\u751f\u6210\u5f97\u5206\u70ed\u56fe\u3002<\/li>\n<\/ul>\n\n\n\n

                          library(reshape2)<\/p>\n\n\n\n

                          melted_scores <- melt(scores_df)<\/p>\n\n\n\n

                          ggplot(melted_scores, aes(x = Alignment, y = variable, fill = value)) +<\/p>\n\n\n\n

                            geom_tile() +<\/p>\n\n\n\n

                            theme_minimal() +<\/p>\n\n\n\n

                            scale_fill_gradient(low = “lightblue”, high = “darkblue”) +<\/p>\n\n\n\n

                            labs(title = “Alignment Scores Heatmap”, x = “Alignment Type”, y = “Score Type”)<\/p>\n\n\n\n

                          \"\"<\/figure>\n\n\n\n
                            \n
                          • \u5b89\u88c5\u548c\u52a0\u8f7d<\/strong>circlize<\/strong>\u5305<\/strong>\u7ed8\u5236<\/strong>\u548c\u5f26\u56fe<\/strong>\uff1a\u5c0f\u679c\u4f7f\u7528chordDiagram\u51fd\u6570\u751f\u6210\u4e24\u4e2a\u5e8f\u5217\u7684\u548c\u5f26\u56fe\uff0c\u5c55\u793a\u5e8f\u5217\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u548c\u5dee\u5f02\u6027\u3002<\/li>\n<\/ul>\n\n\n\n

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

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

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

                            library(circlize)<\/p>\n\n\n\n

                            genome <- data.frame(breaks = c(1, nchar(as.character(s1))),<\/p>\n\n\n\n

                                                 cols = c(“red”, “blue”),<\/p>\n\n\n\n

                                                 labels = c(“Sequence 1”, “Sequence 2”),<\/p>\n\n\n\n

                                                 stringsAsFactors = FALSE)<\/p>\n\n\n\n

                            chordDiagram(genome, transparency = FALSE)<\/p>\n\n\n\n

                            genome <- data.frame(breaks = c(1, nchar(as.character(s2))),<\/p>\n\n\n\n

                                                 labels = c(“Sequence 1”, “Sequence 2”),<\/p>\n\n\n\n

                                                 stringsAsFactors = FALSE)<\/p>\n\n\n\n

                            chordDiagram(genome, transparency = FALSE)<\/p>\n\n\n\n

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

                            \u4e09\u3001 <\/strong>\u6587\u7ae0\u5c0f\u7ed3<\/strong><\/strong><\/h3>\n\n\n\n

                            \u672c\u671f\u5c0f\u679c\u548c\u5c0f\u4f19\u4f34\u4eec\u4e00\u8d77\u5b66\u4e60\u4e86R\u8bed\u8a00\u8fdb\u884c\u5e8f\u5217\u5206\u6790\u7684\u5168\u9762\u793a\u4f8b\uff0c\u5b66\u4e60\u4e86\u5982\u4f55\u4f7f\u7528R\u8bed\u8a00\u8fdb\u884c\u5e8f\u5217\u6bd4\u5bf9\uff0c\u638c\u63e1\u4e86\u6570\u636e\u5904\u7406\u7684\u5173\u952e\u6280\u5de7\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u5b66\u4e60\u4e86\u5982\u4f55\u5c06\u5206\u6790\u7ed3\u679c\u4ee5\u76f4\u89c2\u7684\u65b9\u5f0f\u5448\u73b0\u51fa\u6765\uff0c\u5b9e\u73b0\u4e86\u6570\u636e\u7684\u53ef\u89c6\u5316\u3002\u8fd9\u4e0d\u4ec5\u52a0\u6df1\u4e86\u6211\u4eec\u5bf9\u751f\u7269\u4fe1\u606f\u5b66\u77e5\u8bc6\u7684\u7406\u89e3\uff0c\u4e5f\u953b\u70bc\u4e86\u6211\u4eec\u7684R\u8bed\u8a00\u5b9e\u8df5\u80fd\u529b\u3002\u5e0c\u671b\u672c\u671f\u5185\u5bb9\u5bf9\u5c0f\u4f19\u4f34\u4eec\u6709\u5e2e\u52a9~\u8ba9\u6211\u4eec\u7ee7\u7eed\u63a2\u7d22\u751f\u4fe1\u7684\u65e0\u9650\u53ef\u80fd\uff0c\u5171\u540c\u5f00\u542f\u66f4\u591a\u79d1\u7814\u65b0\u7bc7\u7ae0\uff01\u6700\u540e\u5982\u679c\u5404\u4f4d\u5c0f\u4f19\u4f34\u4eec\u89c9\u5f97\u81ea\u5df1\u8fd0\u884c\u4ee3\u7801\u592a\u9ebb\u70e6\uff0c\u6b22\u8fce\u7528\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u5c0f\u5de5\u5177\uff0c\u53ea\u8981\u8f93\u5165\u5408\u9002\u7684\u6570\u636e\u5c31\u53ef\u4ee5\u76f4\u63a5\u51fa\u60f3\u8981\u7684\u56fe\u54e6\uff0c\u9644\u4e91\u751f\u4fe1\u94fe\u63a5\uff08http:\/\/www.biocloudservice.com\/home.html\uff09\u3002<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

                            \u5927\u5bb6\u597d\uff01\u6211\u662f\u5c0f\u679c\uff01\u5c0f\u4f19\u4f34\u4eec\uff0c\u662f\u4e0d\u662f\u5728\u8fdb\u884c\u751f\u7269\u6570\u636e\u5206\u6790\u65f6\uff0c\u7ecf\u5e38\u56e0\u4e3a\u5e8f\u5217\u6bd4\u5bf9\u7684\u95ee\u9898\u800c\u611f\u5230\u82e6\u607c\u5462\uff1f\u590d\u6742\u7684\u5e8f\u5217\u6570\u636e\u3001\u7e41 […]<\/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\/61171"}],"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=61171"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61171\/revisions"}],"predecessor-version":[{"id":61183,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61171\/revisions\/61183"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=61171"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=61171"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=61171"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}