最近在做某個植物小物種的miRNA分析师逸，看過幾個公司的miRNA分析報告和文獻后開始動手年栓。大概分以下幾步：
1坷虑、篩選出目標miRNA
(a)根據(jù)長度篩選clean data（植物18～30nt）
(b)比對到參考基因組上卸察，去除未匹配序列
(c)比對到Rfam中的ncRNA既忆，去除snRNA柠座，snoRNA邑雅，rRNA，tRNA等
(d)去除重復序列
2妈经、保守miRNA的鑒定（與mirbase數(shù)據(jù)庫比對）
3淮野、新miRNA的預測
4、差異表達（和mRNA流程類似）
5吹泡、靶基因的預測（植物用psRNATarget在線分析）
這其中第4骤星、5步我比較熟悉，前三步稍微陌生一點爆哑。能找到軟件整合流程一步跑下來最好洞难，不行的話只能分步嘗試（第一步如果參考基因組注釋比較完整，還可以將sRNA比對到mRNA的外顯子和內(nèi)含子揭朝，找出來自mRNA降解片段的sRNA并去掉）廊营。
還好我找到了很好用的miR-PREFeR歪泳，分享在這里～·github主頁https://github.com/hangelwen/miR-PREFeR
miR-PREFeR: microRNA PREdiction From small RNAseq data
miR-PREFeR成功解決了植物miRNA分析中的幾大痛點：1. 假陽性高; 2. 耗時長; 3. work only for genomes in their databases; 4. 難用 （親身經(jīng)歷證明確實好用）。該軟件于14年發(fā)表在Bioinformatics上并仍在改進中 http://bioinformatics.oxfordjournals.org/content/30/19/2837.abstract.
1露筒、前期準備
安裝samtools
git clone git://github.com/samtools/samtools.git
安裝ViennaRNA ( 最好是1.8.5或2.1.2, 2.1.5版本)
#我裝的是最新版
wget https://www.tbi.univie.ac.at/RNA/download/sourcecode/2_4_x/ViennaRNA-2.4.10.tar.gz
tar zvxf ViennaRNA-2.4.10.tar.gz
cd ViennaRNA-2.4.10
./configure --prefix="/user/tools/ViennaRNA/" --without-perl
make
make install
2呐伞、安裝
git clone https://github.com/hangelwen/miR-PREFeR.git
安裝很方便，可以在example里測試一下pipeline
3慎式、輸入數(shù)據(jù)處理
一共需要的輸入數(shù)據(jù)有三個：
參考基因組fasta文件
small RNAseq 數(shù)據(jù)比對基因組的sam文件（bowtie）
gff文件（可選伶氢，記錄需要被屏蔽掉的信息，比如重復序列）
其中在比對前瘪吏，需要把fasta處理一下癣防，samplename.txt里面記錄取樣信息：
$ vi samplename.txt
root #SAMPLE1.fasta
flower #SAMPLE2.fasta
salt #SAMPLE3.fasta
$ python scripts/process-reads-fasta.py samplename.txt SAMPLE1.fasta SAMPLE2.fasta SAMPLE3.fasta
得到 SAMPLE1.fasta.processed文件，>開頭包含sample name掌眠、sequential number和depth信息
$ less SAMPLE1.fasta.processed
>root_r0_x1 #sample name_sequential number_depth
ACTACTGCAAGGGCTGGCTCAACCCGC
>root_r1_x5
TGGTTGCTGTCGCTGGTCGCTGGT
4蕾盯、bowtie比對
python bowtie-align-reads.py -f -r TAIR10.fas -t bowtie-index/ -p 8 SAMPLE1.fasta.processed SAMPLE2.fasta.processed SAMPLE3.fasta.processed
生成pdep.chr1.sam，pind.chr1.sam蓝丙，cold.chr1.sam
5级遭、準備configuration文件
每個參數(shù)都有詳細說明
$more config.example
#example configuration file for the miR-PREFeR pipeline.
#lines start with '#' are comments
#miR-PREFeR path, please change to your path to the script folder.
#Absolute path perfered.
PIPELINE_PATH = /sRNA/miR-PREFeR/
#Genomic sequence file in fasta format. Absolute path perfered. If a path relative if used, it's relatvie to the working directory where you execute the pipeline.
FASTA_FILE = ./TAIR10.chr1.fa
#Small RNA read alignment file in SAM format. The SAM file should contain the SAM header. If N samples are used, then N file names are listed here, separated by comma. please note that before doing alignment, process the reads fasta files using the provided script 'process-reads-fasta.py' to collapse and rename the reads. Absolute path perfered. If a path relative if used, it's relatvie to the working directory where you execute the pipeline.
ALIGNMENT_FILE = ./cold.chr1.sam, ./pdep.chr1.sam, ./pind.chr1.sam
#GFF file which list all existing annotations on genomic sequences FASTA_FILE. If no GFF file is availble, comment this line out or leave the value blank. Absolute path perfered. If a path relative if used, it's relatvie to the working directory where you execute the pipeline.
#CAUTION: please only list the CDS regions, not the entire miRNA region, because miRNAs could be in introns. This option is mutual exclusive with 'GFF_FILE_INCLUDE' option. If you have a GFF file that contains regions in which you want to predict whether they include miRNAs, please use the 'GFF_FILE_INCLUDE' option instead.
GFF_FILE_EXCLUDE = ./TAIR10.chr1.CDS.gff
# Only predict miRNAs from the regions given in the GFF file. This option is mutual exclusive with 'GFF_FILE_EXCLUDE'. Thus, only one of them can be used.
#GFF_FILE_INCLUDE = ./TAIR10.chr1.candidate.gff
#The max length of a miRNA precursor. The range is from 60 to 3000. The default is 300.
PRECURSOR_LEN = 300
#The first step of the pipeline is to identify candidate regions of the miRNA loci. If READS_DEPTH_CUTOFF = N, then bases that the mapped depth is smaller than N is not considered. The value should >=2.
READS_DEPTH_CUTOFF = 20
#Number of processes for this computation. Using more processes speeds up the computation, especially if you have a multi-core processor. If you have N cores avalible for the computation, it's better to set this value in the range of N to 2*N.
#If comment out or leave blank, 1 is used.
NUM_OF_CORE = 4
#Outputfolder. If not specified, use the current working directory. Please make sure that you have enough disk space for the folder, otherwise the pipeline may fail.
OUTFOLDER = example-result
#Absolute path of the folder that contains intermidate/temperary files during the run of the pipeline. If not specified, miR-PREFeR uses a folder with suffix "_tmp" under OUTFOLDER by default. Please make sure that you have enough disk space for the folder, otherwise the pipeline may fail.
TMPFOLDER = /tmp/exmaple
#prefix for naming the output files. For portability, please DO NOT contain any spaces and special characters. The prefered includes 'A-Z', 'a-z', '0-9', and underscore '_'.
NAME_PREFIX = TAIR10-example
#Maximum gap length between two contigs to form a candidate region.
MAX_GAP = 100
# Minimum and maximum length of the mature sequence. Default values are 18 and 24.
MIN_MATURE_LEN = 18
MAX_MATURE_LEN = 24
# If this is 'Y', then the criteria that requries the star sequence must be expressed is loosed if the expression pattern is good enough (.e.g. the majority of the reads mapped to the region are mapped to the mature position.). There are lots of miRNAs which do not have star sequence expression. The default value is Y.
ALLOW_NO_STAR_EXPRESSION = Y
# In most cases, the mature star duplex has 2nt 3' overhangs. If this is set to 'Y', then 3nt overhangs are allowed. Default is 'N'.
ALLOW_3NT_OVERHANG = N
#The pipeline makes a checkpoint after each major step. In addition, because the folding stage is the most time consuming stage, it makes a checkpiont for each folding process after folding every CHECKPOINT_SIZE sequences. If the pipeline is killed for some reason in the middle of folding, it can be restarted using
#'recover' command from where it was stopped. The default value is 3000. On my system this means making a checkpoint about every 5 minutes.
CHECKPOINT_SIZE = 3000
6、跑流程
python miR_PREFeR.py -L -k pipeline configfile
pipeline里包含prepare, candidate, fold, predict四步渺尘。如果某步中斷了挫鸽，還可以續(xù)跑
python miR_PREFeR.py -L recover configfile
7、輸出結(jié)果
result文件夾里包含如下文件：
吐槽幾個大坑：
mirdeep2整合了bowtie、ViennaRNA医咨、randfold枫匾、PDF-API2等多個軟件，動物miRNA分析用的多拟淮，有專門為植物開發(fā)的mirdeep-P婿牍，但是目前下載不了。而且mirdeep2的安裝很死板惩歉，路徑受限各種報錯不好裝等脂，本來都裝好跑通了后來重新跑的時候就又要重新裝一遍，不推薦撑蚌。
新miRNA預測軟件mireap核心腳本使perl寫的上遥，里面的模塊FFW1現(xiàn)在找不到了，所以用不了
