{"id":3084,"date":"2019-11-18T13:13:11","date_gmt":"2019-11-18T05:13:11","guid":{"rendered":"http:\/\/www.chenlianfu.com\/?p=3084"},"modified":"2019-11-18T13:22:17","modified_gmt":"2019-11-18T05:22:17","slug":"%e5%9f%ba%e5%9b%a0%e6%ad%a3%e9%80%89%e6%8b%a9%e5%88%86%e6%9e%90%e5%8e%9f%e7%90%86","status":"publish","type":"post","link":"http:\/\/www.chenlianfu.com\/?p=3084","title":{"rendered":"\u57fa\u56e0\u6b63\u9009\u62e9\u5206\u6790\u539f\u7406"},"content":{"rendered":"\n

\u4e00\u3001 \u6b63\u9009\u62e9\u5206\u6790\u7684\u76ee\u7684\uff1a<\/strong><\/h2>\n\n\n\n
  1. \u4e24\u4e24\u57fa\u56e0\u7684\u5bc6\u7801\u5b50\u5e8f\u5217\u8fdb\u884c\u6bd4\u8f83\uff0c\u4ece\u800c\u8ba1\u7b97dN\/dS\uff0c\u5373omega\uff08\u03c9\uff09\u503c\u3002\u82e5\u8be5\u503c<1\uff0c\u5219\u8868\u793a\u7eaf\u5316\u9009\u62e9\uff1bomega = 1\uff0c\u5219\u4e2d\u6027\u8fdb\u5316\uff1bomega > 1\uff0c\u5219\u6b63\u9009\u62e9\u3002\u82e5\u5206\u6790\u57fa\u56e0\u5728\u4e24\u4e2a\u7269\u79cd\u4e2d\u7684\u5e8f\u5217\uff0c\u53ef\u4ee5\u8ba1\u7b97dN\/dS\u7684\u503c\uff0c\u82e5omega > 1\uff0c\u5373\u8868\u660e\u8be5\u57fa\u56e0\u5728\u7269\u79cd\u8fdb\u5316\u8fc7\u7a0b\u4e2d\uff0c\u5373\u7531\u5176\u7956\u5148\u7269\u79cd\u5206\u5316\u6210\u8fd9\u4e24\u4e2a\u7269\u79cd\u65f6\uff0c\u57fa\u56e0\u53d7\u5230\u4e86\u6b63\u9009\u62e9\u3002\u5bf9\u4e8e\u4e24\u4e2a\u7269\u79cd\/\u5e8f\u5217\u7684\u6b63\u9009\u62e9\u5206\u6790\uff0c\u6bd4\u8f83\u7b80\u5355\u3002\u800c\u5b9e\u9645\u60c5\u51b5\u4e2d\uff0c\u8981\u5206\u6790\u7684\u7269\u79cd\u6570\u91cf\u5f88\u591a\uff0c\u5305\u542b\u591a\u4e2a\u7c7b\u7fa4\u3002\u8fd9\u4e2a\u65f6\u5019\u7684\u6b63\u9009\u62e9\u5206\u6790\u76f8\u5bf9\u590d\u6742\u4e9b\u3002<\/li>
  2. \u5bf9\u591a\u4e2a\u7269\u79cd\u7684\u57fa\u56e0\u5e8f\u5217\u8fdb\u884c\u6b63\u9009\u62e9\u5206\u6790\uff0c\u82e5\u4ecd\u7136\u6309\u7167\u4e24\u4e2a\u7269\u79cd\u65f6\u7684\u8981\u6c42\uff0c\u5373\u5206\u6790\u8be5\u57fa\u56e0\u5728\u7269\u79cd\u8fdb\u5316\u4e2d\u662f\u5426\u53d7\u5230\u4e86\u6b63\u9009\u62e9\uff1f\u8fd9\u79cd\u7ed3\u679c\u53ef\u80fd\u4e0d\u597d\u8bf4\u6e05\u695a\u3002\u56e0\u4e3a\u8be5\u57fa\u56e0\u53ef\u80fd\u5728\u67d0\u4e00\u7c7b\u7fa4\u4e2d\u5e8f\u5217\u5f88\u76f8\u4f3c\uff0c\u5176\u4e24\u4e24\u6bd4\u8f83\u65f6\uff0comega <= 1\uff1b\u800c\u5728\u53e6\u5916\u4e00\u7c7b\u7fa4\u4e2d\u4e24\u4e24\u6bd4\u8f83\u65f6\uff0c\u5f88\u591a\u65f6\u5019omega > 1\u3002\u6700\u540e\u8f6f\u4ef6\u53ef\u4ee5\u4ece\u603b\u4f53\u4e0a\u7ed9\u4e00\u4e2aomega\u503c\uff0c\u8be5\u503c\u4e0d\u53ef\u4ee5\u62ff\u6765\u7b80\u5355\u5730\u8bc4\u4ef7\u8be5\u57fa\u56e0\u662f\u5426\u53d7\u5230\u4e86\u6b63\u9009\u62e9\u3002\u6240\u4ee5\uff0c\u5bf9\u591a\u4e2a\u7269\u79cd\u8fdb\u884c\u6b63\u9009\u62e9\u5206\u6790\u65f6\uff0c\u6ca1\u6cd5\u76f4\u63a5\u8bc4\u4ef7\u8be5\u57fa\u56e0\u662f\u5426\u53d7\u5230\u4e86\u6b63\u9009\u62e9\u3002\u6b63\u9009\u62e9\u53ea\u6709\u5728\u8fdb\u884c\u4e24\u4e24\u5e8f\u5217\u6bd4\u8f83\u7684\u65f6\u5019\uff0c\u624d\u80fd\u8ba1\u7b97omega\u503c\uff0c\u4ece\u800c\u5f97\u5230\u7ed3\u679c\u3002<\/li>
  3. \u5bf9\u57fa\u56e0\u5728\u591a\u4e2a\u7269\u79cd\u4e0a\u7684\u6b63\u9009\u62e9\u5206\u6790\uff0c\u5206\u6790\u7684\u76ee\u7684\u5219\u662f\uff1a\u6bd4\u8f83\u67d0\u4e2a\u5206\u679d\u4e0a\u7956\u5148\u8282\u70b9\u548c\u540e\u88d4\u8282\u70b9\uff08\u53ef\u4ee5\u7406\u89e3\u6210\uff0c\u5bf9\u65e0\u6839\u6811\u4e0a\u67d0\u5206\u679d\u4e24\u4fa7\u7684\u4e24\u7ec4\u7269\u79cd\u8fdb\u884c\u6bd4\u8f83\uff0c\u4f9d\u7136\u5c5e\u4e8e\u4e24\u4e24\u6bd4\u8f83\uff09\uff0c\u4ece\u800c\u8ba1\u7b97\u8be5\u5206\u679d\u7684omega\u503c\u3002\u800c\u5728\u5b9e\u9645\u6570\u636e\u4e2d\uff0c\u57fa\u56e0\u5728\u4e0d\u540c\u7684\u8fdb\u5316\u5206\u679d\u4e0a\u5177\u6709\u4e0d\u540c\u7684omega\u503c\uff0c\u540c\u65f6\u5728\u5e8f\u5217\u4e0d\u540c\u7684\u4f4d\u70b9\u4e5f\u5177\u6709\u4e0d\u540c\u7684omega\u503c\u3002\u76ee\u6807\u5206\u679d\u4e24\u4fa7\u7684\u7269\u79cd\u6570\u91cf\u8f83\u591a\u65f6\uff0c\u53ef\u4ee5\u5bf9\u5e8f\u5217\u4e0a\u7684\u6bcf\u4e2a\u4f4d\u70b9\u8fdb\u884comega\u503c\u5206\u6790\uff0c\u4ece\u800c\u9274\u5b9a\u6b63\u9009\u62e9\u4f4d\u70b9\u3002\u6240\u4ee5\uff0c\u5bf9\u57fa\u56e0\u5728\u591a\u4e2a\u7269\u79cd\u4e0a\u7684\u6b63\u9009\u62e9\u5206\u6790\uff0c\u9700\u8981\u540c\u65f6\u5206\u6790\u5206\u6790\u76ee\u6807\u5206\u679d\u7684omega\u503c\u548c\u5e8f\u5217\u4f4d\u70b9\u7684omega\u503c\uff0c\u4ece\u800c\u5224\u65ad\u57fa\u56e0\u662f\u5426\u53d7\u5230\u6b63\u9009\u62e9\u538b\u3002<\/li><\/ol>\n\n\n\n

    \u4e8c\u3001\u4f7f\u7528PAM\u5bf9\u57fa\u56e0\u8fdb\u884c\u6b63\u9009\u62e9\u5206\u6790\uff0c\u6709\u4e09\u79cd\u65b9\u6cd5\uff1a<\/strong><\/h2>\n\n\n\n
    1. PAML\u00a0site\u00a0model:\u00a0\u4e3b\u8981\u7528\u4e8e\u68c0\u6d4b\u57fa\u56e0\u4e2d\u7684\u6b63\u9009\u62e9\u4f4d\u70b9\u3002\u8be5\u65b9\u6cd5\u5206\u6790\u65f6\uff0c\u8ba4\u4e3a\u8fdb\u5316\u6811\u4e2d\u5404\u5206\u679d\u7684omega\u503c\u662f\u4e00\u81f4\u7684\uff0c\u5e76\u6bd4\u8f83\u4e24\u79cd\u6a21\u578b\uff1a(1)\u6a21\u578bm1\u662fnull\u00a0model\uff0c\u8ba4\u4e3a\u6240\u6709\u4f4d\u70b9\u7684omega\u503c<1\u6216=1;\u00a0(2)\u6a21\u578bm2\u662f\u6b63\u9009\u62e9\u6a21\u578b\uff0c\u5b58\u5728omega <1\u3001=1\u6216>\u00a01\u7684\u4f4d\u70b9\u3002\u6bd4\u8f83\u4e24\u4e2a\u6a21\u578b\u7684\u4f3c\u7136\u503c\uff08lnL\uff09\u5dee\u5f02\uff0c\u5229\u7528\u5361\u65b9\u68c0\u9a8c\uff08\u81ea\u7531\u5ea6\u4e3a2\uff09\u7b97\u51fap\u503c\u3002\u82e5p\u503c\u00a0<\u00a00.05\uff0c\u5219\u5426\u5b9anull\u00a0model\uff0c\u8ba4\u4e3a\u5b58\u5728\u6b63\u9009\u62e9\u4f4d\u70b9\u3002\u6b64\u5916\uff0c\u63a8\u8350\u91c7\u7528\u6bd4\u8f83\u6a21\u578bm7\u548cm8\uff0c\u5b83\u4eec\u5c06omega\u503c\u5206\u6210\u4e8610\u7c7b\uff0c\u5176p\u503c\u7ed3\u679c\u6bd4\u4e0a\u4e00\u79cd\u6bd4\u8f83\u65b9\u6cd5\u66f4\u5bbd\u677e\uff0c\u80fd\u68c0\u6d4b\u5230\u66f4\u591a\u7684\u6b63\u9009\u62e9\u57fa\u56e0\u3002\u4f7f\u7528PAML\u00a0site\u00a0model\u65b9\u6cd5\u80fd\u5728\u6574\u4f53\u6c34\u5e73\u4e0a\u68c0\u6d4b\u57fa\u56e0\u7684\u6b63\u9009\u62e9\u4f4d\u70b9\uff0c\u800c\u4e0d\u80fd\u8868\u660e\u57fa\u56e0\u5728\u67d0\u4e2a\u8fdb\u5316\u5206\u679d\u4e0a\u662f\u5426\u53d7\u5230\u6b63\u9009\u62e9\u538b\u3002<\/li>
    2. PAML branch-site model: \u4e3b\u8981\u7528\u4e8e\u68c0\u6d4b\u57fa\u56e0\u5728\u67d0\u4e2a\u8fdb\u5316\u679d\u4e0a\u662f\u5426\u5b58\u5728\u7684\u6b63\u9009\u62e9\u4f4d\u70b9\u3002\u8be5\u5206\u6790\u65b9\u6cd5\u8ba4\u4e3a\u76ee\u6807\u5206\u5316\u679d\u5177\u6709\u4e00\u4e2aomega\u503c\uff0c\u5176\u5b83\u6240\u6709\u5206\u679d\u5177\u6709\u4e00\u4e2a\u76f8\u540c\u7684omega\u503c\uff0c\u7136\u540e\u518d\u68c0\u6d4b\u6b63\u9009\u62e9\u4f4d\u70b9\u3002\u540c\u6837\u5bf9\u4e24\u79cd\u6a21\u578b\u8fdb\u884c\u6bd4\u8f83\uff1a\uff081\uff09\u7b2c\u4e00\u79cd\u6a21\u578b\u4e3a\u6a21\u578b2\uff0c\u5c06omega\u503c\u5206\u6210<1\u3001=1\u3001>1\u7684\u4e09\u7c7b\uff0c\u8fd9\u548csite model\u4e2d\u7684\u4e00\u6837\uff1b\uff082\uff09\u7b2c\u4e8c\u79cd\u6a21\u578b\u548c\u524d\u8005\u4e00\u81f4\uff0c\u53ea\u662f\u5c06omega\u56fa\u5b9a\u62101\uff0c\u4f5c\u4e3anull model\u3002\u6bd4\u8f83\u4e24\u79cd\u6a21\u578b\u7684\u4f3c\u7136\u5dee\u5f02\uff0c\u5229\u7528\u5361\u65b9\u68c0\u9a8c\uff08\u81ea\u7531\u5ea6\u4e3a2\uff09\u7b97p\u503c\uff08chi2\u547d\u4ee4\u7b97\u51fa\u7684\u503c\u9664\u4ee52\uff09\u3002\u82e5p\u503c< 0.05\uff0c\u5219\u80fd\u901a\u8fc7Bayes Empirical Bayes (BEB)\u65b9\u6cd5\u8ba1\u7b97\u6b63\u9009\u62e9\u4f4d\u70b9\u7684\u540e\u9a8c\u6982\u7387\uff0c\u82e5\u5b58\u5728\u6982\u7387\u503c > 0.95\u6b63\u9009\u62e9\u4f4d\u70b9\uff0c\u5219\u8868\u793a\u57fa\u56e0\u5728\u76ee\u6807\u5206\u679d\u4e0a\u53d7\u5230\u6b63\u9009\u62e9\u538b\u3002PAML\u8f6f\u4ef6\u5728branch-site\u6a21\u5f0f\u4e0b\uff0c\u5e76\u4e0d\u7ed9\u51fa\u5206\u679d\u4e0a\u7684omega\u503c\u3002\u8fd9\u8868\u793abranch-site\u6a21\u5f0f\u867d\u7136\u8003\u8651\u4e86\u76ee\u6807\u5206\u679d\u4e0a\u5177\u6709\u4e0d\u540c\u7684omega\u503c\uff0c\u4f46\u4ecd\u7136\u4ee5\u5206\u6790\u4f4d\u70b9\u4e0a\u7684omega\u4e3a\u4e3b\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u5728branch-site\u6a21\u5f0f\u4e0b\u53ef\u80fd\u68c0\u6d4b\u5230\u6b63\u9009\u62e9\u4f4d\u70b9\uff0c\u4f46\u5728\u76ee\u6807\u5206\u679d\u4e0a\u7684omega\u503c\u4ecd\u7136\u53ef\u80fd\u4f4e\u4e8e1\u3002\u53ef\u80fd\u8f6f\u4ef6\u4f5c\u8005\u57fa\u4e8e\u8fd9\u70b9\u8003\u8651\uff0c\u5c31\u6ca1\u6709\u7ed9\u51fa\u76ee\u6807\u5206\u679d\u4e0a\u7684omega\u503c\uff0c\u4ee5\u514d\u5f71\u54cd\u4e00\u4e9b\u4eba\u5bf9\u6b63\u9009\u62e9\u7ed3\u679c\u7684\u5224\u65ad\u3002<\/li>
    3. PAML\u00a0branch\u00a0model:\u00a0\u4e3b\u8981\u7528\u4e8e\u68c0\u6d4b\u5728\u67d0\u4e2a\u5206\u679d\u4e0a\uff0c\u5176omega\u503c\u662f\u5426\u663e\u8457\u9ad8\u4e8e\u80cc\u666f\u5206\u679d\uff0c\u5373\u57fa\u56e0\u5728\u76ee\u6807\u5206\u679d\u4e0a\u8fdb\u5316\u901f\u5ea6\u52a0\u5feb\u3002\u8be5\u65b9\u6cd5\u8ba4\u4e3a\u57fa\u56e0\u5e8f\u5217\u4e0a\u6240\u6709\u4f4d\u70b9\u7684omega\u503c\u662f\u4e00\u81f4\u7684\uff0c\u5bf9\u4e24\u79cd\u6a21\u578b\u8fdb\u884c\u6bd4\u8f83\uff1a\uff081\uff09\u7b2c\u4e00\u79cd\u6a21\u578b\u4e3anull\u00a0model\uff0c\u6240\u6709\u5206\u679d\u5177\u6709\u76f8\u540c\u7684omega\u503c\uff1b\uff082\uff09\u7b2c\u4e8c\u79cd\u6a21\u578b\u8ba4\u4e3a\u76ee\u6807\u5206\u679d\u5177\u6709\u4e00\u4e2aomega\u503c\uff0c\u5176\u5b83\u6240\u6709\u5206\u679d\u5177\u6709\u4e00\u4e2a\u76f8\u540c\u7684omega\u503c\u3002\u6bd4\u8f83\u4e24\u79cd\u6a21\u578b\u7684\u4f3c\u7136\u5dee\u5f02\uff0c\u5229\u7528\u5361\u65b9\u68c0\u9a8c\uff08\u81ea\u7531\u5ea6\u4e3a1\uff09\u7b97p\u503c\u3002\u82e5p\u503c <= 0.05\uff0c\u4e14\u76ee\u6807\u5206\u679d\u4e0a\u7684omega\u503c\u9ad8\u4e8e\u80cc\u666f\u503c\uff0c\u5219\u8ba4\u4e3a\u8be5\u57fa\u56e0\u4e3a\u5feb\u901f\u8fdb\u5316\u57fa\u56e0\u3002\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u8be5\u65b9\u6cd5\u8ba1\u7b97\u5f97\u5230\u7684p\u503c\u4f1a\u4f4e\u4e8e\u7b2c\u4e8c\u79cd\u65b9\u6cd5\u7684\u7ed3\u679c\u3002<\/li><\/ol>\n\n\n\n

      \u4e09\u3001\u5176\u5b83\u6ce8\u610f\u4e8b\u9879<\/h2>\n\n\n\n

      Branch-site model\u76f8\u6bd4\u4e8esite model\u7684\u4f18\u70b9\u662f\u8003\u8651\u4e86\u4e0d\u540c\u7684\u5206\u679d\u5177\u6709\u4e0d\u540c\u7684\u9009\u62e9\u538b\uff0c\u5373\u5177\u6709\u4e0d\u540c\u7684omega\u503c\u3002\u8be5\u65b9\u6cd5\u8ba9\u76ee\u6807\u5206\u679d\u5177\u6709\u4e00\u4e2a\u4e0d\u540c\u7684omega\u503c\uff0c\u5e76\u6ca1\u6709\u8ba9\u6240\u6709\u5206\u679d\u7684omega\u503c\u72ec\u7acb\u8fdb\u884c\u8ba1\u7b97\uff08\u7406\u8bba\u4e0a\u8fd9\u6837\u662f\u6700\u597d\u7684\uff09\u3002\u8fd9\u6837\u7b97\u6cd5\u5f88\u590d\u6742\uff0c\u7a0b\u5e8f\u8fd0\u884c\u975e\u5e38\u975e\u5e38\u6d88\u8017\u65f6\u95f4\u3002\u4f46\u5176\u5b9e\u4e5f\u6ca1\u5fc5\u8981\u8fd9\u6837\u505a\uff0c\u56e0\u4e3a\u6b63\u9009\u62e9\u5206\u6790\u5176\u5b9e\u662f\u4e24\u6761\u5e8f\u5217\u6bd4\u8f83\u540e\uff0c\u5206\u6790dN\/dS\uff0c\u518d\u627e\u6b63\u9009\u62e9\u4f4d\u70b9\uff0c\u5176\u5206\u6790\u7ed3\u679c\u5c31\u5e94\u8be5\u662f\u67d0\u4e2a\u5206\u679d\u4e0a\u57fa\u56e0\u662f\u5426\u53d7\u5230\u6b63\u9009\u62e9\uff0c\u5728\u5e8f\u5217\u90a3\u4e2a\u4f4d\u70b9\u4e0a\u53d7\u5230\u6b63\u9009\u62e9\u3002<\/p>\n\n\n\n

      \u82e5\u5728\u76ee\u6807\u5206\u679d\u4e0a\uff0c\u5176omega\u503c\u5c0f\u4e8e1\uff0c\u4f46\u662f\u5374\u80fd\u627e\u5230\u6b63\u9009\u62e9\u4f4d\u70b9\u3002\u5373\u8be5\u57fa\u56e0\u5728\u8be5\u5206\u679d\u4e0a\u7684dN\/dS < 1\uff0c\u4f46\u662f\u5728\u67d0\u4e9b\u4f4d\u70b9\u4e0a\uff0cdN\/dS > 1\u3002\u90a3\u4e48\u8be5\u57fa\u56e0\u662f\u5426\u5c5e\u4e8e\u6b63\u9009\u62e9\u57fa\u56e0\uff1f\u6211\u8ba4\u4e3a\uff1a\u5c5e\u4e8e\u3002\u4e4b\u6240\u4ee5\u4e3a\u6b63\u9009\u62e9\u57fa\u56e0\uff0c\u4e3b\u8981\u662f\u56e0\u4e3a\u57fa\u56e0\u7684\u4e2a\u522b\u4f4d\u70b9\u6216\u591a\u4e2a\u4f4d\u70b9\u5b58\u5728\u6b63\u9009\u62e9\u3002\u5f53\u53ea\u6709\u4e2a\u522b\u4f4d\u70b9\u53d7\u5230\u6b63\u9009\u62e9\u538b\u65f6\uff0c\u800c\u5176\u5b83\u591a\u4e2a\u4f4d\u70b9\u5b58\u5728\u7eaf\u5316\u9009\u62e9\u65f6\uff0c\u53ef\u80fd\u5bfc\u81f4\u6574\u4f53\u4e0a\u7684omega\u503c\u5c0f\u4e8e1\u3002\u6b64\u65f6\uff0c\u8be5\u57fa\u56e0\u4e5f\u5e94\u8be5\u662f\u5c5e\u4e8e\u6b63\u9009\u62e9\u57fa\u56e0\u3002<\/p>\n\n\n\n

      \u56db\u3001\u53c2\u8003\u6587\u732e\u4e2d\u7684\u6b63\u9009\u62e9\u5206\u6790\u65b9\u6cd5\u63cf\u8ff0<\/h2>\n\n\n\n

      Science\u6587\u7ae0\uff08<\/strong>https:\/\/science.sciencemag.org\/content\/364\/6446\/eaav6202<\/a>\uff09\u4e2d\u7684\u6b63\u9009\u62e9\u57fa\u56e0\u5206\u6790\u65b9\u6cd5\uff1a<\/strong>To estimate the lineage-specific evolutionary rate for each branch, the Codeml program in the PAML package (version 4.8) (134) with the free-ratio model (model = 1) was run for each ortholog. Positive selection signals on genes along specific lineages were detected using the optimized branch-site model following the author’s recommendation. A likelihood ratio test (LRT) was conducted to compare a model that allowed sites to be under positive selection on the foreground branch with the null model in which sites could evolve either neutrally and under purifying selection.[ The p values were computed based on Chi-square statistics, and genes with p value less than 0.05 were treated as candidates that underwent positive selection. We identified PSGs at the ancestral branch of Ruminantia (table S22), the ancestral branch of Pecora (table S23), each ancestral family branch of Ruminantia (tableS24), and each ancestral subfamily branch of Bovidae (table S24). We also compared the dN\/dS values of Ruminantia families with outgroup mammals (fig. S52).<\/p>\n\n\n\n

      Science\u6587\u7ae0\uff08<\/strong>https:\/\/science.sciencemag.org\/content\/364\/6446\/eaav6202<\/a>\uff09\u4e2d\u5feb\u901f\u8fdb\u5316\u57fa\u56e0\u5206\u6790\u65b9\u6cd5\uff1a<\/strong>The branch model in PAML was used, with the null model (model=0) assuming that all branches have been evolving at the same rate and the alternative model (model=2), allowing the foreground branch to evolve under a different rate. An LRT with df =1 was used to discriminate between alternative models for each ortholog in the gene set. Genes with a p value less than 0.05 and a higher \u03c9 value for the foreground than the background branches were considered as evolving with a significantly faster rate in the foreground branch.<\/p>\n\n\n\n

      \n\n\n\n

      https:\/\/journals.plos.org\/plosntds\/article?id=10.1371%2Fjournal.pntd.0007463<\/a>\u6587\u732e\u4e2d\u5bf9\u6b63\u9009\u62e9\u57fa\u56e0\u7684\u5206\u6790\u65b9\u6cd5\uff1a<\/strong><\/p>\n\n\n\n

      A calculation of mutational rate ratio \u03c9\nbetween two gene sequences was the basis for the positive selection analysis.\nThe \u03c9 was calculated as a ratio of nonsynonymous to synonymous mutational\nrates. The ratio indicates negative purifying selection (0 < \u03c9 < 1),\nneutral evolution (\u03c9 = 1), and positive selection (\u03c9 > 1) [54]. A set of\nselected genes from complete genomes was tested relative to positive selection\nusing the maximum likelihood method using the CODEML of the PAML software\npackage [55]. PAML version 4 [56] and its user interface PAMLX [57] were used\nin our study. For each analyzed gene, its maximum likelihood phylogenetic tree\nwas used as an input tree. The CODEML offers several different codon\nevolutionary models, and the statistical likelihood ratio test (LRT) was used\nto compare the codon evolutionary model to the null model. The Bayes empirical\nBayes method (BEB) [58] was then used to evaluate the posterior probability of\nsites considered to have been positively selected.<\/p>\n\n\n\n

      The CODEML models could produce different\nresults (i.e., a list of sites under positive selection) since they calculate\ndifferent parameter estimates. Site models allow \u03c9 to vary in each site (codon)\nwithin the gene. Statistical testing was required for sites with \u03c9 > 1. Two\npairs of models were predominantly used since their LRTs have low\nfalse-positive rates. M1a (nearly neutral evolution) was compared to M2a\n(positive selection) [58,59] and M7 (beta) was compared to M8 (beta & \u03c9)\n[60]. Our preliminary testing found that the two model pairs gave the same or\nvery similar results. Therefore we chose to use the M7-M8 model pair. The M7\nmodel is a null model that allows 10 classes of sites with a \u03c9\nbeta-distribution within the interval 0 \u2264 \u03c9 \u2264 1. Sites with \u03c9 > 1 are not\nallowed. The alternative M8 model adds an eleventh class of sites with \u03c9 >\n1. Each site was tested to determine the class to which it belongs. The LRT\ncompares twice the log-likelihood difference 2\u0394l = 2(l1-l0) between the M7\nmodel (log likelihood value l0) and the M8 model (log likelihood value l1) to\nthe \u03c72 distribution [61]. If the twice log-likelihood difference is above a\ncritical \u03c72 value, then the null model is rejected, and the positive selection\nis statistically significant.<\/p>\n\n\n\n

      A considerable disadvantage of the site\nmodels is that \u03c9 was calculated as an average over all codons of the site.\nTherefore, the site models are not suitable for the data where \u03c9 also varies\nbetween lineages. In contrast, the branch-site models search for positive\nselection in sites and pre-specifies lineages where different rates of \u03c9 may\noccur [62]. Sequences of lineages are a priori divided into a group of\nforeground lineages where positive selection may occur and group of background\nlineages where only purifying selection or neutral evolution occurs. We used\nbranch-site model A, which allows four classes of sites and different setups of\nforeground lineages to be tested depending on the gene phylogeny. In\nbranch-site model A, all lineages under purifying selection with a low value of\n\u03c90 belong to site class 0. Weak purifying selection and neutral evolution with\n\u03c91 near to value 1 are allowed in site class 1. In site class 2a, a proportion\nof class 0 sites in foreground lineages is under positive selection with \u03c92\n> 1. Similarly, site class 2b is a proportion of class 1 sites under\npositive selection with \u03c92 > 1. The null model for LRT has \u03c92 = 1. Critical\nvalues of LRT (2\u0394l) are 2.71 at 5% and 5.41 at 1% [63]. The posterior\nprobabilities of suggested sites under positive selection were calculated using\nthe BEB method.<\/p>\n","protected":false},"excerpt":{"rendered":"

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