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
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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
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安装
1 git clone https://github.com/hangelwen/miR-PREFeR.git
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数据准备
1⃣️ref.fa
2⃣️miRNA 比对到ref.fa的sam文件
3⃣️gff 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