miRNA分析--数据过滤（一）

在比对之前为了减少比对时间，将每一个样本中的reads进行合并，得到fasta格式，其命名规则如下：

样本_r数子_x数字

r 中的数字表示reads序号；

x 中的数字表示该条reads重复次数

比对分为两条策略

1、根据本物种已有的miRNA序列进行比对，

已知当miRNA序列从 miRBase或者 sRNAanno得到

(应该将clean reads比对到所研究物种到tRNA, rRNA, snoRNA,mRNA等数据，允许一个错配，将比对上等reads过滤，也可以比对到参考基因组，将为未比对到到reads过滤掉，但是本次我没有这么做)

对于第一种情况，我采用bowtie将reads比对到成熟miRNA

1 ##建立索引
2 bowtie-build ref.fa
3 ##比对
4 bowtie -v 0 -m 30 -p 10 -f  ref.fa sample.fa sample.bwt
5 参数解释
6 -v: 允许0个错配
7 -p: 10个线程
8 -m: 当比对超过这个数时，认为时未比对
9 -f： 输入序列fasta

根据.bwt 文件可以计算出每个已知当miRNA中比对上当reads数量，别忘记乘以 x后面的数

2、直接比对到参考基因组并进行新miRNA鉴定

采用miR-PREFeR进行Novel miRNA鉴定

其githup主页：https://github.com/hangelwen/miR-PREFeR

• 需安装ViennaRNA ( 最好是1.8.5或2.1.2, 2.1.5版本)

1 #我装的是最新版
2 wget https://www.tbi.univie.ac.at/RNA/download/sourcecode/2_4_x/ViennaRNA-2.4.10.tar.gz
3 tar zvxf ViennaRNA-2.4.10.tar.gz
4 cd ViennaRNA-2.4.10
5 ./configure --prefix="/user/tools/ViennaRNA/" --without-perl
6 make
7 make install

• 安装

1 git clone https://github.com/hangelwen/miR-PREFeR.git

• 数据准备

1??ref.fa

2??miRNA 比对到ref.fa的sam文件

3??gff 文件（可选，记录需要屏蔽掉的信息，比如重复序列等）

bowtie 比对

1 bowtie -v 0 -m 30 -p 10 -f ref.fa sample.fa -S sample.sam

准备configure 文件

 1 #example configuration file for the miR-PREFeR pipeline.
 2 #lines start with ‘#‘ are comments
 3
 4 #miR-PREFeR path, please change to your path to the script folder.
 5 #Absolute path perfered.
 6 PIPELINE_PATH = /miR
 7
 8 #Genomic sequence file in fasta format.  Absolute path perfered. If a path
 9 #relative if used, it‘s relatvie to the working directory where you execute
10 #the pipeline.
11 FASTA_FILE =  genome_v1.fa
12
13 #Small RNA read alignment file in SAM format. The SAM file should contain
14 #the SAM header. If N samples are used, then N file names are listed here,
15 #separated by comma. please note that before doing alignment, process the
16 #reads fasta files using the provided script ‘process-reads-fasta.py‘ to
17 #collapse and rename the reads. Absolute path perfered. If a path
18 #relative if used, it‘s relatvie to the working directory where you execute
19 #the pipeline.
20 ALIGNMENT_FILE = ./trm_XX-1_L1_I309.R1.fastq_trm_fa.fa.sam, ./trm_XX-2_L1_I310.R1.fastq_trm_fa.fa.sam, ./trm_XX-3_L1_I311.R1.fastq_trm_fa.fa.sam, ./trm_XY-1_L1_I312.R1.fastq_trm_fa.fa.sam, ./trm_XY-2_L1_I313.R1.fastq_trm_fa.fa.sam, ./trm_XY-3_L1_I314.R1.fastq_trm_fa.fa.sam, ./trm_YY-1_L1_I315.R1.fastq_trm_fa.fa.sam, ./trm_YY-2_L1_I316.R1.fastq_trm_fa.fa.sam, ./trm_YY-3_L1_I332.R1.fastq_trm_fa.fa.sam
21
22 #GFF file which list all existing annotations on genomic sequences FASTA_FILE.
23 #If no GFF file is availble, comment this line out or leave the value blank.
24 #Absolute path perfered. If a path relative if used, it‘s relatvie to the
25 #working directory where you execute the pipeline.
26 #CAUTION: please only list the CDS regions, not the entire miRNA region, because
27 #miRNAs could be in introns. This option is mutual exclusive with ‘GFF_FILE_INCLUDE‘
28 #option.
29 # If you have a GFF file that contains regions in which you want to predict whehter
30 # they include miRNAs, please use the ‘GFF_FILE_INCLUDE‘ option instead.
31 GFF_FILE_EXCLUDE = CDS.gff
32
33 # Only predict miRNAs from the regions given in the GFF file. This option is mutual
34 # exclusive with ‘GFF_FILE_EXCLUDE‘. Thus, only one of them can be used.
35 #GFF_FILE_INCLUDE = ./TAIR10.chr1.candidate.gff
36
37 #The max length of a miRNA precursor. The range is from 60 to 3000. The default
38 #is 300.
39 PRECURSOR_LEN = 300
40
41 #The first step of the pipeline is to identify candidate regions of the miRNA
42 #loci. If READS_DEPTH_CUTOFF = N, then bases that the mapped depth is smaller
43 #than N is not considered. The value should >=2.
44 READS_DEPTH_CUTOFF = 20
45
46 #Number of processes for this computation. Using more processes speeds up the computation,
47 #especially if you have a multi-core processor. If you have N cores avalible for the
48 #computation, it‘s better to set this value in the range of N to 2*N.
49 #If comment out or leave blank, 1 is used.
50 NUM_OF_CORE = 4
51
52 #Outputfolder. If not specified, use the current working directory. Please make sure that
53 #you have enough disk space for the folder, otherwise the pipeline may fail.
54 OUTFOLDER = spinach-result
55
56 #Absolute path of the folder that contains intermidate/temperary files during the
57 # run of the pipeline. If not specified, miR-PREFeR uses a folder with suffix "_tmp"
58 #under OUTFOLDER by default. Please make sure that you have enough disk space for the
59 # folder, otherwise the pipeline may fail.
60 #TMPFOLDER = /tmp/exmaple
61 TMPFOLDER =
62
63 #prefix for naming the output files. For portability, please DO NOT contain any
64 #spaces and special characters. The prefered includes ‘A-Z‘, ‘a-z‘, ‘0-9‘, and
65 #underscore ‘_‘.
66 NAME_PREFIX = spinach-example
67
68 #Maximum gap length between two contigs to form a candidate region.
69 MAX_GAP = 100
70
71 # Minimum and maximum length of the mature sequence. Default values are 18 and 24.
72 MIN_MATURE_LEN = 18
73 MAX_MATURE_LEN = 24
74
75 # If this is ‘Y‘, then the criteria that requries the star sequence must be expressed
76 # is loosed if the expression pattern is good enough (.e.g. the majority of the reads
77 # mapped to the region are mapped to the mature position.). There are lots of miRNAs
78 # which do not have star sequence expression. The default value is Y.
79 ALLOW_NO_STAR_EXPRESSION = Y
80
81 # In most cases, the mature star duplex has 2nt 3‘ overhangs. If this is set to ‘Y‘, then
82 # 3nt overhangs are allowed. Default is ‘N‘.
83 ALLOW_3NT_OVERHANG = N
84
85 #The pipeline makes a checkpoint after each major step. In addition, because the
86 #folding stage is the most time consuming stage, it makes a checkpiont for each
87 #folding process after folding every CHECKPOINT_SIZE sequences. If the pipeline
88 #is killed for some reason in the middle of folding, it can be restarted using
89 #‘recover‘ command from where it was stopped. The default value is 3000. On my
90 #system this means making a checkpoint about every 5 minutes.
91 CHECKPOINT_SIZE = 3000

运行

1 python miR_PREFeR.py -L -k pipeline configfile

pipeline里包含prepare, candidate, fold, predict四步。如果某步中断了，还可以续跑

1 python miR_PREFeR.py -L recover configfile

输出结果

根据example_miRNA.detail.csv 文件 写一个脚本 提取每个miRNA的reads 数量，进而做差异分析

差异分析

差异分析采用DESeq2， 可看我之前写的miRAN 分析以及mRNA分析

ref

1、计算已知miRNA当表达量

2、省心省事的植物miRNA分析软件miR-PREFeR,值得拥有

原文地址：https://www.cnblogs.com/zhanmaomao/p/12150502.html