{"id":61481,"date":"2024-10-30T15:37:08","date_gmt":"2024-10-30T07:37:08","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61481"},"modified":"2024-10-30T15:37:08","modified_gmt":"2024-10-30T07:37:08","slug":"%e8%bf%99%e6%a0%b7%e5%a4%84%e7%90%86%e7%94%9f%e7%89%a9%e5%ba%8f%e5%88%97%e6%b3%b0%e8%a3%a4%e8%be%a3%ef%bc%81seqinr-r%e5%8c%85%e7%82%b9%e4%ba%ae%e5%a4%84%e7%90%86%e7%b9%81%e6%9d%82fasta%e6%96%87","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61481","title":{"rendered":"\u8fd9\u6837\u5904\u7406\u751f\u7269\u5e8f\u5217\u6cf0\u88e4\u8fa3\uff01Seqinr\u00a0R\u5305\u70b9\u4eae\u5904\u7406\u7e41\u6742FASTA\u6587\u4ef6\u65b0\u601d\u8def\uff01"},"content":{"rendered":"\n

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

\u5927\u5bb6\u597d\uff0c\u6211\u662f\u5c0f\u679c~ \u8fd8\u5728\u4e3a\u751f\u4fe1\u5206\u6790\u4e2dFASTA\u6587\u4ef6\u7684\u7e41\u7410\u5904\u7406\u800c\u72b9\u8c6b\u4e0d\u51b3\uff0c\u751a\u81f3\u5934\u75bc\u4e0d\u5df2\u5417\uff1f\u4e0d\u7528\u518d\u5fcd\u53d7\u624b\u52a8\u64cd\u4f5c\u7684\u7e41\u7410\u548c\u8017\u65f6\u4e86\uff01\u8ba9\u5c0f\u679c\u4e3a\u5927\u5bb6\u9686\u91cd\u4ecb\u7ecdSeqinr\u8fd9\u4e2a\u5f3a\u5927\u7684R\u5305\uff0c\u5b83\u5c31\u50cf\u4e00\u4e2a\u8d34\u5fc3\u7684\u52a9\u624b\uff0c\u4e13\u95e8\u6765\u62ef\u6551\u4f60\u7684\u751f\u4fe1\u5206\u6790\u4e4b\u8def\uff01Seqinr\u4e0d\u4ec5\u4ec5\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u5e8f\u5217\u5206\u6790\u5de5\u5177\uff0c\u5b83\u4ee5\u5176\u76f4\u89c2\u6613\u7528\u7684\u63a5\u53e3\u548c\u4e30\u5bcc\u7684\u529f\u80fd\uff0c\u4e3a\u6211\u4eec\u6253\u5f00\u4e86\u4e00\u4e2a\u5168\u65b0\u7684\u89c6\u89d2\u6765\u5904\u7406\u548c\u5206\u6790\u5e8f\u5217\u6570\u636e\u3002\u4e0e\u4f20\u7edf\u7684Biostrings\u5305\u76f8\u6bd4\uff0cSeqinr\u66f4\u52a0\u4e13\u6ce8\u4e8e\u63d0\u4f9b\u7528\u6237\u53cb\u597d\u7684\u4f53\u9a8c\u548c\u5f3a\u5927\u7684\u529f\u80fd\uff0c\u8ba9\u4f60\u5728\u5904\u7406FASTA\u6587\u4ef6\u7b49\u751f\u7269\u4fe1\u606f\u6570\u636e\u65f6\u4e8b\u534a\u529f\u500d\uff01\u6240\u4ee5\uff0c\u5982\u679c\u4f60\u6b63\u5728\u4e3a\u751f\u4fe1\u5206\u6790\u4e2d\u7684\u5e8f\u5217\u6570\u636e\u5904\u7406\u800c\u70e6\u607c\uff0c\u4e0d\u59a8\u8bd5\u8bd5Seqinr\u8fd9\u4e2aR\u5305\u5427\uff01\u5b83\u4e00\u5b9a\u4f1a\u8ba9\u4f60\u7684\u5206\u6790\u5de5\u4f5c\u53d8\u5f97\u66f4\u52a0\u8f7b\u677e\u548c\u9ad8\u6548\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><\/p>\n\n\n\n

(\u4e00) <\/strong>Seqinr R\u5305\u7b80\u8981\u4ecb\u7ecd<\/strong><\/strong><\/h2>\n\n\n\n

Seqinr\u63d0\u4f9b\u4e86\u4e00\u5957\u5168\u9762\u7684\u529f\u80fd\uff0c\u5e2e\u52a9\u7814\u7a76\u4eba\u5458\u4eceDNA\u3001RNA\u548c\u86cb\u767d\u8d28\u5e8f\u5217\u4e2d\u63d0\u53d6\u6709\u4ef7\u503c\u7684\u4fe1\u606f\u3002Seqinr\u4e0d\u4ec5\u652f\u6301\u5e8f\u5217\u6570\u636e\u7684\u8bfb\u53d6\u548c\u5199\u5165\uff0c\u8fd8\u63d0\u4f9b\u4e86\u5e8f\u5217\u5904\u7406\u3001\u8f6c\u6362\u548c\u5206\u6790\u7684\u591a\u79cd\u51fd\u6570\u3002<\/p>\n\n\n\n

Seqinr\u80fd\u591f\u8f7b\u677e\u8bfb\u53d6\u548c\u5199\u5165\u5404\u79cd\u683c\u5f0f\u7684\u5e8f\u5217\u6570\u636e<\/strong>\uff0c\u5305\u62ecFASTA\u3001GenBank\u548cEMBL\u7b49\u3002\u65e0\u8bba\u662f\u4ece\u672c\u5730\u6587\u4ef6\u8fd8\u662f\u4ece\u5728\u7ebf\u6570\u636e\u5e93\uff0cSeqinr\u90fd\u80fd\u9ad8\u6548\u5730\u5904\u7406\u8fd9\u4e9b\u6570\u636e\u3002\u5e76\u4e14Seqinr\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u51fd\u6570\uff0c\u7528\u4e8e\u5e8f\u5217\u7684\u7f16\u8f91\u3001\u8f6c\u6362\u548c\u6bd4\u5bf9<\/strong>\u7b49\u64cd\u4f5c\u3002\u4f8b\u5982\uff0c\u8ba1\u7b97\u5e8f\u5217\u7684\u78b1\u57fa\u7ec4\u6210\uff08\u5305\u62ec\u78b1\u57fa\u9891\u7387\u548cGC\u542b\u91cf\uff09\u3002\u6b64\u5916\uff0cSeqinr\u8fd8\u4e3aR\u8bed\u8a00\u63d0\u4f9b\u4e86\u8bbf\u95ee\u5e8f\u5217\u6570\u636e\u5e93<\/strong>\u7684\u65b9\u5f0f\uff0c\u5982GenBank\u7b49\u3002\u8fd9\u4f7f\u5f97\u6211\u4eec\u80fd\u591f\u66f4\u52a0\u65b9\u4fbf\u5730\u68c0\u7d22\u548c\u83b7\u53d6\u6240\u9700\u7684\u5e8f\u5217\u6570\u636e\u3002<\/p>\n\n\n\n

(\u4e8c) <\/strong>Seqinr R\u5305\u4ee3\u7801\u5b9e\u64cd<\/strong><\/strong><\/h2>\n\n\n\n

Seqinr\u53ef\u4ee5\u901a\u8fc7R\u8bed\u8a00\u7684\u5305\u7ba1\u7406\u5668\u8fdb\u884c\u5b89\u88c5\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u52a0\u8f7dSeqinr\u5305\u5e76\u8c03\u7528\u76f8\u5e94\u7684\u51fd\u6570\u5373\u53ef\u5f00\u59cb\u4f7f\u7528\u3002Seqinr\u7684\u63a5\u53e3\u76f4\u89c2\u6613\u7528\uff0c\u5373\u4f7f\u5bf9\u4e8e\u6ca1\u6709\u4e30\u5bcc\u7f16\u7a0b\u7ecf\u9a8c\u7684\u5c0f\u767d\u6765\u8bf4\uff0c\u4e5f\u80fd\u591f\u5feb\u901f\u4e0a\u624b\u54e6~\u4f46\u662f\u8be5R\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

 \u5feb\u901f\u4e0a\u624b\uff1a<\/em><\/h2>\n\n\n\n

1. <\/strong>\u5b89\u88c5\u4e0e\u52a0\u8f7d\uff1a<\/strong><\/strong><\/h3>\n\n\n\n

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

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

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

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

2. <\/strong>\u8bfb\u53d6FASTA\u6587\u4ef6\uff1a<\/strong><\/strong><\/h3>\n\n\n\n

seq_records <- read.fasta(file = “sequence1.fasta”,as.string = TRUE)<\/p>\n\n\n\n

protein_sequence <- read.fasta(file = “sequence2.fasta”,as.string = TRUE)<\/p>\n\n\n\n

# \u67e5\u770b\u5e8f\u5217\u4fe1\u606f<\/p>\n\n\n\n

names(seq_records)<\/p>\n\n\n\n

summary(seq_records)<\/p>\n\n\n\n

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

\u4ece\u8f93\u51fa\u7ed3\u679c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0cnames(seq_records) \u51fd\u6570\u663e\u793a\u4e86\u8bfb\u53d6\u7684fasta\u6587\u4ef6\u4e2d\u5305\u542b\u7684\u4e94\u4e2a\u5e8f\u5217\u7684\u540d\u79f0\uff0c\u5b83\u4eec\u5206\u522b\u662f “MN963666.1″\u3001”MT350719.1″\u3001”MN939090.1″\u3001”MN963655.1” \u548c “MN939091.1″\u3002\u8fd9\u4e9b\u540d\u79f0\u662f\u5e8f\u5217\u7684\u6807\u8bc6\u7b26\u3001\u8bbf\u95ee\u53f7\uff0c\u6709\u52a9\u4e8e\u6211\u4eec\u5728\u7814\u7a76\u4e2d\u552f\u4e00\u8bc6\u522b\u6bcf\u4e2a\u5e8f\u5217\u3002<\/p>\n\n\n\n

summary(seq_records) \u51fd\u6570\u663e\u793a\u7684\u7ed3\u679c\u8ba9\u6211\u4eec\u4e86\u89e3\u5230 seq_records\u662f\u4e00\u4e2a\u5305\u542b\u5355\u4e2a\u5143\u7d20\u7684\u5217\u8868\uff0c\u800c\u6bcf\u4e2a\u5143\u7d20\u53c8\u662f\u4e00\u4e2a\u5b57\u7b26\u6a21\u5f0f\uff0c\u4ee3\u8868\u4e00\u4e2a DNA \u5e8f\u5217\u3002\u6bcf\u4e2a\u5e8f\u5217\u90fd\u4ee5\u5b83\u7684\u540d\u5b57\u4f5c\u4e3a\u6807\u7b7e\uff0c\u5b58\u50a8\u5728\u5217\u8868\u4e2d\u3002<\/p>\n\n\n\n

\u8fd9\u4e9b\u4fe1\u606f\u5bf9\u4e8e\u6211\u4eec\u8fdb\u4e00\u6b65\u7684\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u975e\u5e38\u91cd\u8981\uff0c\u4f8b\u5982\u5e8f\u5217\u6bd4\u5bf9\u3001\u53d8\u5f02\u68c0\u6d4b\u6216\u8fdb\u5316\u5206\u6790\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e8f\u5217\u7684\u6807\u8bc6\u7b26\uff0c\u6211\u4eec\u53ef\u4ee5\u8ffd\u6eaf\u5230\u539f\u59cb\u6570\u636e\u6e90\uff0c\u4ece\u800c\u83b7\u53d6\u66f4\u591a\u5173\u4e8e\u5e8f\u5217\u7684\u80cc\u666f\u4fe1\u606f\u3002<\/p>\n\n\n\n

3. <\/strong>\u5206\u6790\u5e8f\u5217\u7279\u6027\uff1a<\/strong><\/strong><\/h3>\n\n\n\n

# \u83b7\u53d6\u5e8f\u5217\u957f\u5ea6<\/p>\n\n\n\n

sequence_lengths <- c()<\/p>\n\n\n\n

# \u4f7f\u7528 for \u5faa\u73af\u83b7\u53d6\u6bcf\u4e2a\u5e8f\u5217\u7684\u957f\u5ea6<\/p>\n\n\n\n

for (i in seq_along(seq_records)) {<\/p>\n\n\n\n

  sequence_length <- nchar(as.character(seq_records[[i]]))  <\/p>\n\n\n\n

  sequence_lengths <- c(sequence_lengths, sequence_length)  <\/p>\n\n\n\n

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

# \u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846\u6765\u5b58\u50a8\u5e8f\u5217\u957f\u5ea6\u548c\u5bf9\u5e94\u7684\u5e8f\u5217\u540d\u79f0<\/p>\n\n\n\n

lengths_df <- data.frame(Length = sequence_lengths, Sequence = names(seq_records))<\/p>\n\n\n\n

print(lengths_df)<\/p>\n\n\n\n

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

\u5728\u5bf9\u63d0\u4f9b\u7684FASTA\u6587\u4ef6 “sequence1.fasta” \u8fdb\u884c\u5206\u6790\u540e\uff0c\u6211\u4eec\u5f97\u5230\u4e86\u4e94\u4e2a\u4e0d\u540c\u7684DNA\u5e8f\u5217\uff0c\u5b83\u4eec\u5206\u522b\u88ab\u6807\u8bc6\u4e3aMN963666.1\u3001MT350719.1\u3001MN939090.1\u3001MN963655.1\u548cMN939091.1\u3002\u8fd9\u4e9b\u5e8f\u5217\u7684\u957f\u5ea6\u4ece576\u5230908\u4e2a\u6838\u82f7\u9178\u4e0d\u7b49\uff0c\u663e\u793a\u51fa\u5e8f\u5217\u95f4\u5b58\u5728\u4e00\u5b9a\u7684\u957f\u5ea6\u591a\u6837\u6027\u3002\u5e8f\u5217\u957f\u5ea6\u7684\u591a\u6837\u6027\u53ef\u80fd\u5bf9\u67d0\u4e9b\u751f\u7269\u5b66\u5206\u6790\u6709\u91cd\u8981\u5f71\u54cd\uff0c\u4f8b\u5982\u57fa\u56e0\u7ec4\u7ec4\u88c5\u6216\u57fa\u56e0\u8868\u8fbe\u5206\u6790\u3002<\/p>\n\n\n\n

\u8fd9\u4e9b\u5e8f\u5217\u7684\u5177\u4f53\u957f\u5ea6\u5982\u4e0b\uff1aMN963666.1\u5e8f\u5217\u957f\u5ea6\u4e3a908\uff0cMT350719.1\u4e3a736\uff0cMN939090.1\u4e3a576\uff0cMN963655.1\u4e3a837\uff0c\u800cMN939091.1\u4e3a656\u3002\u5e8f\u5217\u957f\u5ea6\u7684\u8fd9\u79cd\u5dee\u5f02\u53ef\u80fd\u53cd\u6620\u4e86\u5b83\u4eec\u5728\u57fa\u56e0\u7ec4\u4e2d\u7684\u529f\u80fd\u591a\u6837\u6027\u6216\u5b83\u4eec\u6240\u5c5e\u7684\u57fa\u56e0\u5bb6\u65cf\u7684\u4e0d\u540c\u3002\u5728\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u4e2d\uff0c\u8fd9\u4e9b\u5e8f\u5217\u53ef\u7528\u4e8e\u6bd4\u8f83\u57fa\u56e0\u7ec4\u5b66\u7814\u7a76\uff0c\u4ee5\u63a2\u7d22\u5b83\u4eec\u5728\u8fdb\u5316\u8fc7\u7a0b\u4e2d\u7684\u4fdd\u5b88\u6027\u548c\u53d8\u5f02\u6027\u3002<\/p>\n\n\n\n

# \u7ed8\u5236\u76f4\u65b9\u56fe\uff0c\u4e3a\u6bcf\u4e2a\u5e8f\u5217\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272<\/p>\n\n\n\n

par(mfrow=c(1,1))  <\/p>\n\n\n\n

colors <- rainbow(length(seq_records))  <\/p>\n\n\n\n

# \u521d\u59cb\u5316\u76f4\u65b9\u56fe\uff0c\u4e0d\u586b\u5145\u989c\u8272<\/p>\n\n\n\n

hist(lengths_df$Length, col=”white”, main=”Sequence Length Distribution”, xlab=”Sequence Length”, freq=TRUE, border=”white”)<\/p>\n\n\n\n

for (i in seq_along(seq_records)) {<\/p>\n\n\n\n

  hist(lengths_df$Length,  col=colors, add=TRUE, freq=TRUE)<\/p>\n\n\n\n

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

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

\u5728\u5206\u6790\u5e8f\u5217\u957f\u5ea6\u5206\u5e03\u65f6\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u76f4\u65b9\u56fe\u6765\u53ef\u89c6\u5316\u4e0d\u540c\u5e8f\u5217\u7684\u957f\u5ea6\u3002\u901a\u8fc7rainbow\u51fd\u6570\u4e3a\u6bcf\u4e2a\u5e8f\u5217\u5206\u914d\u4e0d\u540c\u7684\u989c\u8272\uff0c\u589e\u5f3a\u4e86\u56fe\u8868\u7684\u89c6\u89c9\u533a\u5206\u5ea6\u3002\u66f4\u6e05\u6670\u5730\u5c55\u793a\u5e8f\u5217\u957f\u5ea6\u7684\u591a\u6837\u6027\u548c\u5206\u5e03\u7279\u5f81\u3002<\/p>\n\n\n\n

# \u8ba1\u7b97 GC \u542b\u91cf<\/p>\n\n\n\n

calculate_gc_content <- function(sequence) {<\/p>\n\n\n\n

  g_count <- sum(unlist(strsplit(tolower(sequence), “”)) == “g”)<\/p>\n\n\n\n

  c_count <- sum(unlist(strsplit(tolower(sequence), “”)) == “c”)<\/p>\n\n\n\n

  seq_length <- nchar(sequence)<\/p>\n\n\n\n

  if (seq_length > 0) {<\/p>\n\n\n\n

    return((g_count + c_count) \/ seq_length)<\/p>\n\n\n\n

  }<\/p>\n\n\n\n

  else {<\/p>\n\n\n\n

    return(NA)<\/p>\n\n\n\n

  }<\/p>\n\n\n\n

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

gc_content_list <- numeric(length(seq_records))<\/p>\n\n\n\n

for (i in seq_along(seq_records)) {<\/p>\n\n\n\n

  gc_content_list[i] <- calculate_gc_content(seq_records[i])<\/p>\n\n\n\n

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

# \u6253\u5370\u6bcf\u4e2a\u5e8f\u5217\u7684 GC \u542b\u91cf\u767e\u5206\u6bd4<\/p>\n\n\n\n

for (i in seq_along(gc_content_list)) {<\/p>\n\n\n\n

  sequence_name <- names(gc_content_list)[i]<\/p>\n\n\n\n

  gc_percentage <- gc_content_list[i] * 100   <\/p>\n\n\n\n

  cat(paste0(“GC content of sequence “, sequence_name, “: “, gc_percentage, “%\\n”))<\/p>\n\n\n\n

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

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

\u4ece\u8f93\u51fa\u7ed3\u679c\u53ef\u4ee5\u77e5\u9053\uff0c\u5728\u5bf9\u4e94\u4e2aDNA\u5e8f\u5217\u8fdb\u884c\u5206\u6790\u540e\uff0c\u6211\u4eec\u8ba1\u7b97\u4e86\u5b83\u4eec\u7684GC\u542b\u91cf\uff08\u5373Guanine\u548cCytosine\u7684\u6bd4\u4f8b\uff09\uff0c\u7ed3\u679c\u663e\u793a\u8fd9\u4e9b\u5e8f\u5217\u7684GC\u542b\u91cf\u5b58\u5728\u663e\u8457\u5dee\u5f02\u3002\u5177\u4f53\u6765\u770b\uff0c\u5e8f\u5217\u7684GC\u542b\u91cf\u8303\u56f4\u4ece32.01%\u523054.36%\u4e0d\u7b49\uff0c\u8868\u660e\u5b83\u4eec\u5728\u78b1\u57fa\u7ec4\u6210\u4e0a\u5177\u6709\u591a\u6837\u6027\u3002<\/p>\n\n\n\n

\u8f83\u9ad8\u7684GC\u542b\u91cf\u901a\u5e38\u4e0e\u57fa\u56e0\u8868\u8fbe\u7684\u7a33\u5b9a\u6027\u548c\u86cb\u767d\u8d28\u7f16\u7801\u80fd\u529b\u6709\u5173\uff0c\u800c\u8f83\u4f4e\u7684GC\u542b\u91cf\u53ef\u80fd\u4e0e\u57fa\u56e0\u7ec4\u4e2d\u7684\u975e\u7f16\u7801\u533a\u57df\u6216\u57fa\u56e0\u95f4\u7684\u95f4\u9694\u533a\u57df\u6709\u5173\u3002\u8fd9\u79cdGC\u542b\u91cf\u7684\u591a\u6837\u6027\u53ef\u80fd\u53cd\u6620\u4e86\u4e0d\u540c\u5e8f\u5217\u5728\u751f\u7269\u5b66\u529f\u80fd\u548c\u8fdb\u5316\u538b\u529b\u4e0a\u7684\u4e0d\u540c\u9002\u5e94\u6027\u3002<\/p>\n\n\n\n

\u5206\u6790\u7ed3\u679c\u8868\u660e\uff0c\u5176\u4e2d\u4e24\u4e2a\u5e8f\u5217\u5177\u6709\u8d85\u8fc750%\u7684GC\u542b\u91cf\uff0c\u8fd9\u53ef\u80fd\u6307\u793a\u5b83\u4eec\u5728\u7ed3\u6784\u7a33\u5b9a\u6027\u548c\u529f\u80fd\u4e0a\u7684\u91cd\u8981\u6027\u3002\u800c\u5176\u4ed6\u4e09\u4e2a\u5e8f\u5217\u7684GC\u542b\u91cf\u8f83\u4f4e\uff0c\u53ef\u80fd\u5728\u57fa\u56e0\u8868\u8fbe\u8c03\u63a7\u6216\u57fa\u56e0\u7ec4\u7ed3\u6784\u4e2d\u6709\u5176\u4ed6\u4f5c\u7528\u3002\u8fd9\u4e9b\u6570\u636e\u5bf9\u4e8e\u8fdb\u4e00\u6b65\u7684\u57fa\u56e0\u7ec4\u5b66\u7814\u7a76\u548c\u7406\u89e3\u5e8f\u5217\u7684\u751f\u7269\u5b66\u7279\u6027\u5177\u6709\u91cd\u8981\u610f\u4e49\u3002<\/p>\n\n\n\n

#\u53ef\u89c6\u5316GC\u542b\u91cf\u5dee\u5f02\u60c5\u51b5<\/p>\n\n\n\n

par(mar = c(5 ,5, 3, 3))<\/p>\n\n\n\n

barplot(height = gc_content_list,<\/p>\n\n\n\n

        names.arg = names(gc_content_list),<\/p>\n\n\n\n

        main = “GC Content Percentage for Each Sequence”,<\/p>\n\n\n\n

        xlab = “Sequence Name”,<\/p>\n\n\n\n

        ylab = “GC Content (%)”,<\/p>\n\n\n\n

        col = “lightblue”,<\/p>\n\n\n\n

        border = “darkblue”)<\/p>\n\n\n\n

# \u5728\u6761\u5f62\u56fe\u4e0a\u6dfb\u52a0\u6570\u503c\u6807\u7b7e<\/p>\n\n\n\n

for (i in seq_along(gc_content_list)) {<\/p>\n\n\n\n

  label <- sprintf(“%.2f”, gc_content_list[i])<\/p>\n\n\n\n

  text(x = i, y = gc_content_list[i] + 0.01 * max(gc_content_list),<\/p>\n\n\n\n

       labels = label, pos = 1, offset = 0.5, cex = 0.7)}<\/p>\n\n\n\n

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

# \u83b7\u53d6\u5e8f\u5217\u540d\u79f0\uff0c\u4f7f\u7528\u5e8f\u5217\u5728\u5217\u8868\u4e2d\u7684\u7d22\u5f15\u4f5c\u4e3a\u540d\u79f0<\/p>\n\n\n\n

sequence_names <- names(seq_records)<\/p>\n\n\n\n

# \u521d\u59cb\u5316\u4e00\u4e2a\u7a7a\u5217\u8868\u6765\u5b58\u50a8\u6bcf\u4e2a\u5e8f\u5217\u7684\u78b1\u57fa\u8ba1\u6570\u7ed3\u679c<\/p>\n\n\n\n

base_counts_list <- list()<\/p>\n\n\n\n

# \u904d\u5386\u6bcf\u4e2a\u5e8f\u5217\uff0c\u8ba1\u7b97\u78b1\u57fa\u8ba1\u6570<\/p>\n\n\n\n

for (i in seq_along(seq_records)) {<\/p>\n\n\n\n

  sequence <- seq_records[[i]]<\/p>\n\n\n\n

  split_sequence <- strsplit(sequence, “”)[[1]]  <\/p>\n\n\n\n

  base_counts <- table(split_sequence)        <\/p>\n\n\n\n

  base_counts_list[[i]] <- base_counts          <\/p>\n\n\n\n

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

# \u5c06\u5217\u8868\u8f6c\u6362\u4e3a\u6570\u636e\u6846<\/p>\n\n\n\n

base_counts_df <- do.call(rbind, base_counts_list)<\/p>\n\n\n\n

rownames(base_counts_df) <- sequence_names      <\/p>\n\n\n\n

# \u6253\u5370\u7ed3\u679c<\/p>\n\n\n\n

print(base_counts_df)<\/p>\n\n\n\n

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

\u6839\u636e\u7ed3\u679c\u663e\u793a\uff0c\u4e94\u4e2aDNA\u5e8f\u5217\u7684\u78b1\u57fa\u7ec4\u6210\u5404\u6709\u7279\u70b9\u3002\u5e8f\u5217MN963666.1\u5177\u6709\u6700\u9ad8\u7684G\u78b1\u57fa\u6570\u91cf\uff08257\uff09\uff0c\u800c\u5e8f\u5217MN939090.1\u7684C\u78b1\u57fa\u6570\u91cf\u76f8\u5bf9\u8f83\u5c11\uff0890\uff09\u3002\u6574\u4f53\u6765\u770b\uff0c\u5404\u5e8f\u5217\u7684A\u548cT\u78b1\u57fa\u6570\u91cf\u76f8\u5bf9\u5747\u8861\uff0c\u4f46G\u548cC\u78b1\u57fa\u7684\u6570\u91cf\u5728\u4e0d\u540c\u5e8f\u5217\u95f4\u53d8\u5316\u8f83\u5927\u3002\u8fd9\u4e9b\u5dee\u5f02\u53ef\u80fd\u4e0e\u5e8f\u5217\u7684\u529f\u80fd\u3001\u7ed3\u6784\u7a33\u5b9a\u6027\u4ee5\u53ca\u5b83\u4eec\u5728\u57fa\u56e0\u7ec4\u4e2d\u7684\u4f4d\u7f6e\u6709\u5173\u3002GC\u542b\u91cf\u8f83\u9ad8\u7684\u5e8f\u5217\u53ef\u80fd\u5728\u70ed\u7a33\u5b9a\u6027\u548c\u57fa\u56e0\u8868\u8fbe\u4e0a\u5177\u6709\u7279\u5b9a\u7684\u751f\u7269\u5b66\u610f\u4e49\u3002\u6b64\u5916\uff0c\u78b1\u57fa\u7ec4\u6210\u4e5f\u53ef\u80fd\u5f71\u54cdDNA\u7684\u4e09\u7ef4\u7ed3\u6784\u548c\u4e0e\u5176\u4ed6\u5206\u5b50\u7684\u76f8\u4e92\u4f5c\u7528\u3002<\/p>\n\n\n\n

#\u53ef\u89c6\u5316\u6bcf\u6761\u5e8f\u5217\u7684\u78b1\u57fa\u7ec4\u6210\u60c5\u51b5<\/p>\n\n\n\n

unique_bases <- names(base_counts)<\/p>\n\n\n\n

base_colors <- colorRampPalette(c(“pink”, “lightgreen”, “skyblue”, “yellow”))(length(unique_bases))<\/p>\n\n\n\n

draw_pie <- function(data, name) {<\/p>\n\n\n\n

  percentages <- data\/sum(data) * 100<\/p>\n\n\n\n

  pie(data, labels = paste0(unique_bases, “: “, percentages, “%”),<\/p>\n\n\n\n

      main = name, col = base_colors,<\/p>\n\n\n\n

      border = NA)<\/p>\n\n\n\n

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

# \u4f7f\u7528layout\u51fd\u6570\u8bbe\u7f6e\u753b\u5e03\u5e03\u5c40\uff0c\u8fd9\u91cc\u8bbe\u7f6e\u4e3a2\u884c3\u5217<\/p>\n\n\n\n

layout(matrix(1:6, nrow = 2, ncol = 3))<\/p>\n\n\n\n

# \u7ed8\u5236\u6bcf\u4e2a\u5e8f\u5217\u7684\u997c\u56fe<\/p>\n\n\n\n

for (i in 1:nrow(base_counts_df)) {<\/p>\n\n\n\n

  par(mar = c(0, 0, 0, 0))  <\/p>\n\n\n\n

  draw_pie(base_counts_df[i, ], rownames(base_counts_df)[i])<\/p>\n\n\n\n

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

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

# \u8ba1\u7b97\u5e8f\u5217\u957f\u5ea6<\/p>\n\n\n\n

sequence_length <- nchar(protein_sequence)<\/p>\n\n\n\n

print(sequence_length)<\/p>\n\n\n\n

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

#\u8fdb\u884c\u5e8f\u5217\u68c0\u7d22<\/p>\n\n\n\n

sequence_name <- “MN963666.1”<\/p>\n\n\n\n

specific_sequence <- as.character(seq_records[[sequence_name]])<\/p>\n\n\n\n

print(specific_sequence)<\/p>\n\n\n\n

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

#\u7279\u6b8a\u5e8f\u5217\u67e5\u8be2<\/p>\n\n\n\n

pattern <- “agtc”<\/p>\n\n\n\n

matches <- grep(pattern, as.character(specific_sequence))<\/p>\n\n\n\n

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

\u5728\u5bf9\u7279\u5b9a\u5e8f\u5217 specific_sequence \u8fdb\u884c\u6a21\u5f0f\u5339\u914d\u5206\u6790\u65f6\uff0c\u6211\u4eec\u4f7f\u7528\u4e86 grep \u51fd\u6570\u6765\u641c\u7d22\u5b50\u4e32 “agtc”\u3002\u7ed3\u679c\u663e\u793a\uff0c\u8be5\u6a21\u5f0f\u5728\u5e8f\u5217\u4e2d\u51fa\u73b0\u4e86\u4e00\u6b21\uff0c\u5176\u4f4d\u7f6e\u7d22\u5f15\u4e3a1\u3002\u8fd9\u8868\u660e “agtc” \u5b50\u4e32\u5728\u5e8f\u5217\u4e2d\u81f3\u5c11\u6709\u4e00\u4e2a\u786e\u5207\u7684\u5339\u914d\u3002<\/p>\n\n\n\n

\u5206\u6790\u8fd9\u4e00\u7ed3\u679c\uff0c\u6211\u4eec\u53ef\u4ee5\u63a8\u65ad “agtc” \u53ef\u80fd\u662f\u8be5\u5e8f\u5217\u4e2d\u7684\u4e00\u4e2a\u4fdd\u5b88\u533a\u57df\u6216\u529f\u80fd\u5355\u5143\u3002\u8fd9\u79cd\u6a21\u5f0f\u7684\u51fa\u73b0\u53ef\u80fd\u4e0e\u5e8f\u5217\u7684\u751f\u7269\u5b66\u529f\u80fd\u6216\u7ed3\u6784\u7279\u5f81\u76f8\u5173\u3002\u867d\u7136\u4ec5\u53d1\u73b0\u4e00\u6b21\u5339\u914d\uff0c\u4f46\u8fd9\u8db3\u4ee5\u5f15\u8d77\u6ce8\u610f\uff0c\u7279\u522b\u662f\u5728\u5bfb\u627e\u57fa\u56e0\u8c03\u63a7\u5143\u4ef6\u6216\u86cb\u767d\u8d28\u7ed3\u5408\u4f4d\u70b9\u65f6\u3002<\/p>\n\n\n\n

\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u53ef\u80fd\u9700\u8981\u8003\u8651\u5e8f\u5217\u7684\u4e0a\u4e0b\u6587\u4fe1\u606f\uff0c\u4ee5\u53ca “agtc” \u6a21\u5f0f\u5728\u5e8f\u5217\u4e2d\u7684\u786e\u5207\u4f4d\u7f6e\u548c\u5206\u5e03\u60c5\u51b5\u3002\u8fd9\u79cd\u5206\u6790\u5bf9\u4e8e\u7406\u89e3\u5e8f\u5217\u7684\u751f\u7269\u5b66\u610f\u4e49\u548c\u8bbe\u8ba1\u540e\u7eed\u7684\u5b9e\u9a8c\u9a8c\u8bc1\u662f\u6709\u4ef7\u503c\u7684\u3002<\/p>\n\n\n\n

#\u7ffb\u8f6c\u5e8f\u5217\u5e76\u4e14\u8fdb\u884c\u6587\u4ef6\u7684\u4fdd\u5b58\u8f93\u51fa<\/p>\n\n\n\n

reversed_seq <- rev(specific_sequence)<\/p>\n\n\n\n

write.fasta(reversed_seq, “path_to_output_file.fasta”)<\/p>\n\n\n\n

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

(\u4e09) <\/strong>\u6587\u7ae0\u5c0f\u7ed3<\/strong><\/strong><\/h2>\n\n\n\n

\u672c\u671f\u5c0f\u679c\u548c\u5c0f\u4f19\u4f34\u4eec\u4e00\u8d77\u8fdb\u884c\u4e86Seqinr\u5305\u7684\u5b66\u4e60, \u901a\u8fc7Seqinr\u5305\uff0c\u6211\u4eec\u80fd\u591f\u66f4\u9ad8\u6548\u5730\u5904\u7406\u548c\u5206\u6790\u5e8f\u5217\u6570\u636e\uff0c\u4e3a\u672a\u6765\u7684\u79d1\u7814\u5de5\u4f5c\u6253\u4e0b\u4e86\u575a\u5b9e\u7684\u57fa\u7840\u3002\u5e0c\u671b\u8fd9\u6b21\u5b66\u4e60\u4e0d\u4ec5\u80fd\u8ba9\u5c0f\u4f19\u4f34\u4eec\u9886\u7565\u5230\u751f\u7269\u6570\u636e\u5206\u6790\u7684\u5f3a\u5927\u9b45\u529b\uff0c\u8fd8\u80fd\u6df1\u523b\u611f\u53d7\u5230\u79d1\u5b66\u63a2\u7d22\u7684\u65e0\u9650\u53ef\u80fd\u3002\u8ba9\u6211\u4eec\u643a\u624b\u524d\u884c\uff0c\u7ee7\u7eed\u5728\u751f\u7269\u4fe1\u606f\u5b66\u7684\u9053\u8def\u4e0a\u63a2\u7d22\u672a\u77e5\uff0c\u8ffd\u5bfb\u771f\u7406\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\u5462\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\uff0c\u6211\u662f\u5c0f\u679c~ \u8fd8\u5728\u4e3a\u751f\u4fe1\u5206\u6790\u4e2dFASTA\u6587\u4ef6\u7684\u7e41\u7410\u5904\u7406\u800c\u72b9\u8c6b\u4e0d\u51b3\uff0c\u751a\u81f3\u5934\u75bc\u4e0d\u5df2\u5417\uff1f\u4e0d\u7528\u518d\u5fcd\u53d7\u624b […]<\/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\/61481"}],"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=61481"}],"version-history":[{"count":3,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61481\/revisions"}],"predecessor-version":[{"id":61605,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61481\/revisions\/61605"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=61481"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=61481"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=61481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}