• 第四次考核 Jimmy 学徒考核 Linux安装软件 rnaseq上游分析-2 ascp kingfisher数据下载ena

     1

    第四次考核 Jimmy 学徒考核 Linux安装软件 rnaseq上游分析_YoungLeelight的博客-CSDN博客添加命令 export PATH=$PATH:/path/to/bwa-0.7.12/bin # Add bwa to your PATH by editing ~/.bashrc file (or .bash_profile or .profile file) 在全新服务器配置转录组测序数据处理环境 1.2 配置rna转录组环境1.3 下载并且整理数据库文件数据下载也是老规矩，利用prefetch下载SRA数据，准备进行转录组数据分析。在GSE数据集对应网页，点击SRA_Rhttps://blog.csdn.net/qq_52813185/article/details/128172862?csdn_share_tail=%7B%22type%22%3A%22blog%22%2C%22rType%22%3A%22article%22%2C%22rId%22%3A%22128172862%22%2C%22source%22%3A%22qq_52813185%22%7D

    01-rna-seq从头开始 卖萌哥 Linux生信技能树Linux安装软件 Linux实战RNASEQ上游_YoungLeelight的博客-CSDN博客

    1.首先构建项目所需目录，这样比较哦清晰。共分为四个目录，

    2.原始数据上传测序数据
    或者自己通过kingfisher下载sra原始数据或者 fastq.gz数据

    下载公共测序数据的另一种姿势（kingfisher） - 简书

    生物信息常见文件的格式以及查看方式 | Public Library of Bioinformatics  

    高速快速下载基因组ref文件

    查看文件下载是否完整

    md5值检查文件完整性，因为原始文件 太大，需要检查数据完成性    md5值给每个文件一个独特的id，根据id是否相等来检查文件完整性cd  01raw_data/
     

    1.  md5sum *gz>md5.txt     #给每个 gz文件都生成md5值，，并且输入到 md5.txt 
    2.      ls
    3.  cat md5.txt          #查看内容
    4.  
    5.  md5sum -c md5.txt   #比较文件自带的md5值和自己生成的md5值是否相等。若相等则文件相等。  -c参数，check一下
    6. md5sum --help

    查看fastq文件内容

      1. # zcat查看gzip压缩的文件
      2.  
      3. # head -n 8 显示前8行文件内容（前8行代表2条序列）
      4.  
      5.  
      6. zcat filename.fq.gz | head -n 8

    3 查看原始数据的质量情况 质控

    1. conda activate rna
    2. #当前目录下
    3. fastqc S*gz
    4. ls -lh
    5.  
    6.  
    7.  
    8. multiqc ./

    fastq.gz为原始的测序数据

    fastqc.zip 为fastqc质控时候产生的数据 

    似乎质量不行啊

    通过前面这些步骤，已经初步判定数据是否合格。如果不合格，那如何修改？或者把不合格的数据扔掉？trimmgalore质控

    4 trim_galore去接头(并行处理) - 简书

    trimmgalore批量质控  双端数据

    2 分离_1和_2文件

    1. (wes) pc@lab-pc:/project/raw_fq$ ls|grep _1.fastq.gz>gz1
    2. (wes) pc@lab-pc:/project/raw_fq$ ls|grep _2.fastq.gz>gz2
    3. (wes) pc@lab-pc:/project/raw_fq$ paste gz1 gz2>config
    4. (wes) pc@lab-pc:/project/raw_fq$ cat config|head

     然后写一个脚本 用于 trimmgalore批量质控  双端数据 同时处理两个参数

    $一定不要忘记加上 

    1. (rna) tree119 21:48:01 ~/pipeline/download/rnaseq/01_rawdata
    2. $ cat trim.sh 
    3. outdir=~/pipeline/download/rnaseq/02cleandata/
    4.  
    5.  
    6. cat config |while read id
    7. do
    8. 	arr=${id}
    9. 	fq1=${arr[0]}
    10. 	fq2=${arr[1]}
    11.  
    12. 	nohup trim_galore  -q 25 --phred33 --length 36 --stringency 3 --paired  -o $outdir  $fq1 $fq2 & 
    13. done

    运行即可

    bash trim.sh

    结果输入到了outdir文件夹下 （这里指的是02cleandata文件夹）

    得到的质控过滤后的文件：

    $ ls -lh  *fq.gz|cut -d" " -f 5-

     

    选取运行过程中其中一对结果展示如下

    1. RUN STATISTICS FOR INPUT FILE: SRR11618782_1.fastq.gz
    2. =============================================
    3. 343183 sequences processed in total
    4. The length threshold of paired-end sequences gets evaluated later on (in the validation step)
    5.  
    6. Writing report to '/home/st8/ssd2/tree119/pipeline/download/rnaseq/01_rawdata/outdir/SRR11618782_2.fastq.gz_trimming_report.txt'
    7.  
    8. SUMMARISING RUN PARAMETERS
    9. ==========================
    10. Input filename: SRR11618782_2.fastq.gz
    11. Trimming mode: paired-end
    12. Trim Galore version: 0.6.7
    13. Cutadapt version: 1.18
    14. Number of cores used for trimming: 1
    15. Quality Phred score cutoff: 25
    16. Quality encoding type selected: ASCII+33
    17. Adapter sequence: 'CTGTCTCTTATA' (Nextera Transposase sequence; auto-detected)
    18. Maximum trimming error rate: 0.1 (default)
    19. Minimum required adapter overlap (stringency): 3 bp
    20. Minimum required sequence length for both reads before a sequence pair gets removed: 36 bp
    21. Output file(s) will be GZIP compressed
    22.  
    23. Cutadapt seems to be reasonably up-to-date. Setting -j -j 1
    24. Writing final adapter and quality trimmed output to SRR11618782_2_trimmed.fq.gz
    25.  
    26.  
    27.   >>> Now performing quality (cutoff '-q 25') and adapter trimming in a single pass for the adapter sequence: 'CTGTCTCTTATA' from file SRR11618782_2.fastq.gz <<< 
    28. This is cutadapt 1.18 with Python 3.7.15
    29. Command line parameters: -j 1 -e 0.1 -q 25 -O 3 -a CTGTCTCTTATA SRR11618610_2.fastq.gz
    30. Processing reads on 1 core in single-end mode ...
    31. Finished in 2.45 s (18 us/read; 3.36 M reads/minute).
    32.  
    33. === Summary ===
    34.  
    35. Total reads processed:                 137,124
    36. Reads with adapters:                     4,643 (3.4%)
    37. Reads written (passing filters):       137,124 (100.0%)
    38.  
    39. Total basepairs processed:    13,712,400 bp
    40. Quality-trimmed:                 305,580 bp (2.2%)
    41. Total written (filtered):     13,368,511 bp (97.5%)
    42.  
    43. === Adapter 1 ===
    44.  
    45. Sequence: CTGTCTCTTATA; Type: regular 3'; Length: 12; Trimmed: 4643 times.
    47. No. of allowed errors:
    48. 0-9 bp: 0; 10-12 bp: 1
    50. Bases preceding removed adapters:
    51.   A: 18.6%
    52.   C: 33.4%
    53.   G: 24.8%
    54.   T: 23.1%
    55.   none/other: 0.0%
    57. Overview of removed sequences
    58. length	count	expect	max.err	error counts
    59. 3	3150	2142.6	0	3150
    60. 4	577	535.6	0	577
    61. 5	174	133.9	0	174
    62. 6	39	33.5	0	39
    63. 7	12	8.4	0	12
    64. 8	15	2.1	0	15
    65. 9	15	0.5	0	13 2
    66. 10	29	0.1	1	13 16
    67. 11	3	0.0	1	2 1
    68. 12	10	0.0	1	8 2
    69. 13	11	0.0	1	11
    70. 14	13	0.0	1	11 2
    71. 15	16	0.0	1	12 4
    72. 16	15	0.0	1	10 5
    73. 17	8	0.0	1	6 2
    74. 18	6	0.0	1	5 1
    75. 19	12	0.0	1	11 1
    76. 20	10	0.0	1	6 4
    77. 21	12	0.0	1	11 1
    78. 22	9	0.0	1	8 1
    79. 23	16	0.0	1	13 3
    80. 24	8	0.0	1	6 2
    81. 25	10	0.0	1	9 1
    82. 26	7	0.0	1	6 1
    83. 27	16	0.0	1	14 2
    84. 28	5	0.0	1	4 1
    85. 29	11	0.0	1	9 2
    86. 30	26	0.0	1	19 7
    87. 31	32	0.0	1	29 3
    88. 32	10	0.0	1	9 1
    89. 33	22	0.0	1	13 9
    90. 34	11	0.0	1	11
    91. 35	17	0.0	1	9 8
    92. 36	7	0.0	1	3 4
    93. 37	3	0.0	1	3
    94. 38	20	0.0	1	19 1
    95. 39	14	0.0	1	13 1
    96. 40	7	0.0	1	4 3
    97. 41	22	0.0	1	20 2
    98. 42	39	0.0	1	37 2
    99. 43	5	0.0	1	4 1
    100. 44	15	0.0	1	15
    101. 45	7	0.0	1	5 2
    102. 46	12	0.0	1	9 3
    103. 47	1	0.0	1	0 1
    104. 48	8	0.0	1	7 1
    105. 49	6	0.0	1	6
    106. 50	2	0.0	1	1 1
    107. 51	3	0.0	1	3
    108. 52	3	0.0	1	0 3
    109. 53	5	0.0	1	3 2
    110. 54	2	0.0	1	0 2
    111. 55	1	0.0	1	1
    112. 57	8	0.0	1	5 3
    113. 58	8	0.0	1	7 1
    114. 59	1	0.0	1	1
    115. 60	11	0.0	1	8 3
    116. 61	26	0.0	1	24 2
    117. 62	5	0.0	1	4 1
    118. 63	6	0.0	1	3 3
    119. 64	8	0.0	1	7 1
    120. 65	1	0.0	1	0 1
    121. 67	3	0.0	1	1 2
    122. 68	3	0.0	1	3
    123. 69	1	0.0	1	0 1
    124. 70	1	0.0	1	0 1
    125. 72	3	0.0	1	0 3
    126. 73	1	0.0	1	0 1
    127. 74	3	0.0	1	0 3
    128. 77	14	0.0	1	2 12
    129. 78	1	0.0	1	1
    130. 79	5	0.0	1	0 5
    131. 81	5	0.0	1	0 5
    132. 84	6	0.0	1	1 5
    133. 86	2	0.0	1	0 2
    134. 88	2	0.0	1	0 2
    135. 90	3	0.0	1	0 3
    136. 91	2	0.0	1	1 1
    137. 92	2	0.0	1	1 1
    138. 93	1	0.0	1	0 1
    139. 95	1	0.0	1	0 1
    140. 98	1	0.0	1	0 1
    143. RUN STATISTICS FOR INPUT FILE: SRR11618610_2.fastq.gz
    144. =============================================
    145. 137124 sequences processed in total
    146. The length threshold of paired-end sequences gets evaluated later on (in the validation step)
    148. Validate paired-end files SRR11618610_1_trimmed.fq.gz and SRR11618610_2_trimmed.fq.gz
    149. file_1: SRR11618610_1_trimmed.fq.gz, file_2: SRR11618610_2_trimmed.fq.gz
    152. >>>>> Now validing the length of the 2 paired-end infiles: SRR11618610_1_trimmed.fq.gz and SRR11618610_2_trimmed.fq.gz <<<<<
    153. Writing validated paired-end Read 1 reads to SRR11618610_1_val_1.fq.gz
    154. Writing validated paired-end Read 2 reads to SRR11618610_2_val_2.fq.gz
    156. Total number of sequences analysed: 137124
    158. Number of sequence pairs removed because at least one read was shorter than the length cutoff (36 bp): 2597 (1.89%)
    160. Deleting both intermediate output files SRR11618610_1_trimmed.fq.gz and SRR11618610_2_trimmed.fq.gz
    162. ====================================================================================================
    164. This is cutadapt 1.18 with Python 3.7.15
    165. Command line parameters: -j 1 -e 0.1 -q 25 -O 3 -a CTGTCTCTTATA SRR11618782_2.fastq.gz
    166. Processing reads on 1 core in single-end mode ...
    167. Finished in 4.63 s (13 us/read; 4.44 M reads/minute).
    169. === Summary ===
    171. Total reads processed:                 343,183
    172. Reads with adapters:                    31,276 (9.1%)
    173. Reads written (passing filters):       343,183 (100.0%)
    175. Total basepairs processed:    34,318,300 bp
    176. Quality-trimmed:               2,815,627 bp (8.2%)
    177. Total written (filtered):     30,713,243 bp (89.5%)
    179. === Adapter 1 ===
    181. Sequence: CTGTCTCTTATA; Type: regular 3'; Length: 12; Trimmed: 31276 times.
    182.  
    183. No. of allowed errors:
    184. 0-9 bp: 0; 10-12 bp: 1
    185.  
    186. Bases preceding removed adapters:
    187.   A: 17.2%
    188.   C: 45.5%
    189.   G: 19.7%
    190.   T: 17.6%
    191.   none/other: 0.0%
    192.  
    193. Overview of removed sequences
    194. length	count	expect	max.err	error counts
    195. 3	6021	5362.2	0	6021
    196. 4	1325	1340.6	0	1325
    197. 5	542	335.1	0	542
    198. 6	372	83.8	0	372
    199. 7	358	20.9	0	358
    200. 8	348	5.2	0	348
    201. 9	291	1.3	0	283 8
    202. 10	386	0.3	1	281 105
    203. 11	384	0.1	1	305 79
    204. 12	490	0.0	1	387 103
    205. 13	388	0.0	1	331 57
    206. 14	512	0.0	1	406 106
    207. 15	371	0.0	1	323 48
    208. 16	392	0.0	1	307 85
    209. 17	449	0.0	1	370 79
    210. 18	354	0.0	1	272 82
    211. 19	517	0.0	1	422 95
    212. 20	440	0.0	1	371 69
    213. 21	380	0.0	1	294 86
    214. 22	310	0.0	1	249 61
    215. 23	220	0.0	1	199 21
    216. 24	291	0.0	1	255 36
    217. 25	604	0.0	1	510 94
    218. 26	370	0.0	1	306 64
    219. 27	489	0.0	1	411 78
    220. 28	314	0.0	1	265 49
    221. 29	600	0.0	1	491 109
    222. 30	432	0.0	1	382 50
    223. 31	1108	0.0	1	954 154
    224. 32	361	0.0	1	306 55
    225. 33	501	0.0	1	418 83
    226. 34	509	0.0	1	421 88
    227. 35	548	0.0	1	472 76
    228. 36	411	0.0	1	346 65
    229. 37	665	0.0	1	507 158
    230. 38	1168	0.0	1	1053 115
    231. 39	2363	0.0	1	2233 130
    232. 40	276	0.0	1	235 41
    233. 41	537	0.0	1	506 31
    234. 42	278	0.0	1	249 29
    235. 43	452	0.0	1	428 24
    236. 44	368	0.0	1	344 24
    237. 45	60	0.0	1	53 7
    238. 46	74	0.0	1	65 9
    239. 47	129	0.0	1	122 7
    240. 48	225	0.0	1	200 25
    241. 49	366	0.0	1	346 20
    242. 50	163	0.0	1	146 17
    243. 51	43	0.0	1	40 3
    244. 52	26	0.0	1	20 6
    245. 53	79	0.0	1	63 16
    246. 54	14	0.0	1	12 2
    247. 55	80	0.0	1	61 19
    248. 56	35	0.0	1	22 13
    249. 57	152	0.0	1	146 6
    250. 58	184	0.0	1	179 5
    251. 59	121	0.0	1	114 7
    252. 60	242	0.0	1	227 15
    253. 61	796	0.0	1	756 40
    254. 62	146	0.0	1	134 12
    255. 63	64	0.0	1	62 2
    256. 64	146	0.0	1	134 12
    257. 65	66	0.0	1	59 7
    258. 66	20	0.0	1	18 2
    259. 67	104	0.0	1	85 19
    260. 68	65	0.0	1	58 7
    261. 69	16	0.0	1	11 5
    262. 70	11	0.0	1	10 1
    263. 71	11	0.0	1	9 2
    264. 72	19	0.0	1	13 6
    265. 73	17	0.0	1	14 3
    266. 74	40	0.0	1	27 13
    267. 75	21	0.0	1	14 7
    268. 76	9	0.0	1	8 1
    269. 77	18	0.0	1	15 3
    270. 78	13	0.0	1	10 3
    271. 79	16	0.0	1	10 6
    272. 80	11	0.0	1	9 2
    273. 81	9	0.0	1	3 6
    274. 82	14	0.0	1	14
    275. 83	13	0.0	1	12 1
    276. 84	17	0.0	1	15 2
    277. 85	33	0.0	1	30 3
    278. 86	17	0.0	1	16 1
    279. 87	20	0.0	1	20
    280. 88	5	0.0	1	5
    281. 89	13	0.0	1	13
    282. 90	6	0.0	1	5 1
    283. 91	7	0.0	1	6 1
    284. 92	13	0.0	1	2 11
    285. 95	1	0.0	1	0 1
    286. 96	1	0.0	1	0 1
    287. 97	7	0.0	1	1 6
    288. 100	3	0.0	1	0 3
    289.


    5.比对 align索引文件和质控好的测序数据进行比对

    下载参考基因组 下载已经建立好索引的基因组

    在全新服务器配置转录组测序数据处理环境

    查看下载的基因组是否正确  查看下载内容时候有误

    md5sum检查完整性 查看gtf文件是否完整

    案例一

    1. md5sum ./* >md5sum.txt
    2.  
    3. md5sum -c md5sum.txt

    结果如下，说明文件是完整的 

    $ md5sum -c md5sum.txt
    ./CM000663.1: OK
    ./CM000664.1: OK
    ./CM000665.1: OK
    ./CM000666.1: OK
    ./CM000667.1: OK
    ./CM000668.1: OK
    ./CM000669.1: OK
    ./CM000670.1: OK
    ./CM000671.1: OK
    ./CM000672.1: OK
    ./CM000673.1: OK
    ./CM000674.1: OK
    ./CM000675.1: OK
    ./CM000676.1: OK
    ./CM000677.1: OK
    ./CM000678.1: OK
    ./CM000679.1: OK
    ./CM000680.1: OK
    ./CM000681.1: OK
    ./CM000682.1: OK
    ./CM000683.1: OK
    ./CM000684.1: OK
    ./CM000685.1: OK
    ./CM000686.1: OK
    ./GL000191.1: OK
    ./GL000192.1: OK
    ./GL000193.1: OK
    ./GL000194.1: OK
    ./GL000195.1: OK
    ./GL000196.1: OK
    ./GL000197.1: OK
    ./GL000198.1: OK
    ./GL000199.1: OK
    ./GL000200.1: OK
    ./GL000201.1: OK
    ./GL000202.1: OK
    ./GL000203.1: OK
    ./GL000204.1: OK
    ./GL000205.1: OK
    ./GL000206.1: OK
    ./GL000207.1: OK
    ./GL000208.1: OK
    ./GL000209.1: OK
    ./GL000210.1: OK
    ./GL000211.1: OK
    ./GL000212.1: OK
    ./GL000213.1: OK
    ./GL000214.1: OK
    ./GL000215.1: OK
    ./GL000216.1: OK
    ./GL000217.1: OK
    ./GL000218.1: OK
    ./GL000219.1: OK
    ./GL000220.1: OK
    ./GL000221.1: OK
    ./GL000222.1: OK
    ./GL000223.1: OK
    ./GL000224.1: OK
    ./GL000225.1: OK
    ./GL000226.1: OK
    ./GL000227.1: OK
    ./GL000228.1: OK
    ./GL000229.1: OK
    ./GL000230.1: OK
    ./GL000231.1: OK
    ./GL000232.1: OK
    ./GL000233.1: OK
    ./GL000234.1: OK
    ./GL000235.1: OK
    ./GL000236.1: OK
    ./GL000237.1: OK
    ./GL000238.1: OK
    ./GL000239.1: OK
    ./GL000240.1: OK
    ./GL000241.1: OK
    ./GL000242.1: OK
    ./GL000243.1: OK
    ./GL000244.1: OK
    ./GL000245.1: OK
    ./GL000246.1: OK
    ./GL000247.1: OK
    ./GL000248.1: OK
    ./GL000249.1: OK
    ./NC_012920.1: OK
    ./SRR11832836.sra: OK

    1.  md5sum *.gz >md5.txt
    2. cat md5.txt
    3. md5sum -c md5.txt

    下载索引文件

    Hisat2官网上人类基因组索引的下载 - 简书

    wget -c 

    在全新服务器配置转录组测序数据处理环境

    对基因组文件解压缩 去掉gz后缀 

    ls *gz |xargs gunzip

    RNA-seq(5):序列比对：Hisat2 - 简书

    对索引文件解压缩 

    1. $ cd reference/index
    2. $ wget ftp://ftp.ccb.jhu.edu/pub/infphilo/hisat2/data/hg19.tar.gz
    3. $ wget ftp://ftp.ccb.jhu.edu/pub/infphilo/hisat2/data/mm10.tar.gz
    4. # 解压得到两个目录，hg19和mm10
    5. $ tar -zxvf *.tar.gz

    得到8个索引

    基因组注释文件(GFF,GTF)下载的五种方法_白墨石的博客-CSDN博客_基因组注释文件

    hg38的gtf文件下载

    genecode中参考基因组的名字  更新很快！

     新建脚本来运行  制备文件准备工作

    1. 489  ls |grep _1.fq.gz
    2.   490  ls |grep _1.fq.gz >gz1
    3.   491  ls |grep _2.fq.gz >gz2
    4.   492  paste gz1 gz2 >file_for_align
    5.   493  cat file_for_align

     

    新建脚本来运行  失败

    1. vim align.sh
    2.  
    3.  
    4. cat file_for_align |while read id
    5. do
    6. 	 arr=${id}
    7. 	 fq1=${arr[0]}
    8. 	 fq2=${arr[1]}
    9. 	 
    10.  
    11. 	 nohup sh -c " hisat2 -p 2 -x ~/pipeline/download/rnaseq/00ref/index_file/grch38/genome  -1 ${fq1} -2 ${fq2}  2>${id%%_*}.log  | samtools sort -@ 2 -o ${id%%_*}.bam  " & 
    12.  
    13. done

     联合多人的代码之后，成功！

    1. ls |grep  .fq.gz|cut -d "_" -f 1|
    2. while read id; do nohup sh -c " hisat2 -p 2 
    3. -x ~/pipeline/download/rnaseq/00ref/index_file/grch38/genome  
    4. -1 ${id}_1_val_1.fq.gz  -2 ${id}_1_val_1.fq.gz 
    5.  2>${id%%_*}.log  | 
    6. samtools sort -@ 2 -o ${id%%_*}.bam  " & done

     

    RNA-seq(5):序列比对：Hisat2 - 简书 别人的代码

    1. #启动miniconda3环境（hisat2所在的环境）
    2. $ source ~/miniconda3/bin/activate
    3. #进入data目录
    4. $cd /mnt/f/rna_seq/aligned
    5. (base) kelly@DESKTOP-MRA1M1F:/mnt/f/rna_seq/aligned$
    6.  
    7. # 小鼠和人是分开各自比对自己的index
    8. # 人的比对
    9. $ for ((i=56;i<=58;i++));do hisat2 -t -x /mnt/f/rna_seq/data/reference/index/hg19/genome -1 /mnt/f/rna_seq/data/SRR35899${i}.sra_1.fastq.gz -2 /mnt/f/rna_seq/data/SRR35899${i}.sra_2.fastq.gz -S SRR35899${i}.sam ;done
    10. # 小鼠比对
    11. $ for ((i=59;i<=62;i++));do hisat2 -t -x /mnt/f/rna_seq/data/reference/index/mm10/genome -1 /mnt/f/rna_seq/data/SRR35899${i}.sra_1.fastq.gz -2 /mnt/f/rna_seq/data/SRR35899${i}.sra_2.fastq.gz -S SRR35899${i}.sam; done
    12. #结果一共得到7个sam文件

    genome指的是这里的genome.x.ht2的basename 

    4.定量 featurecounts

    featurecounts的使用说明 - 简书

    1. gtf=~/pipeline/download/rnaseq/00ref/genom_file/gencode.v41.annotation.gtf
    2.  
    3. nohup featureCounts -T 5 -p -t exon -g gene_id  -a $gtf -o  all.id.txt  *bam  1>counts.id.log 2>&1 &

    reads计数原理及featureCounts统计counts后的cpm和tpm计算 - 简书

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  • 原文地址：https://blog.csdn.net/qq_52813185/article/details/128177352