miRNA target analysis
背景介绍
microRNA(miRNA)是一类能够调节基因表达的短单链内源非编码RNA(约22nt)，通过与互补的mRNA选择性结合抑制蛋白的产生，广泛存在于动物、植物、病毒等多种有机体中。miRNA在许多生物过程中起关键作用，包括发育、细胞分化、增值、凋亡、肿瘤转移等。
workflow
miRNA靶基因预测
1. download from database
miRTarBase: the experimentally validated microRNA-target interactions database
As a database, miRTarBase has accumulated more than three hundred and sixty thousand miRNA-target interactions (MTIs), which are collected by manually surveying pertinent literature after NLP of the text systematically to filter research articles related to functional studies of miRNAs. Generally, the collected MTIs are validated experimentally by reporter assay, western blot, microarray and next-generation sequencing experiments. While containing the largest amount of validated MTIs, the miRTarBase provides the most updated collection by comparing with other similar, previously developed databases.
http://mirtarbase.mbc.nctu.edu.tw/php/index.php
Chou et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Research, 2018
miRWalk2.0: a comphrehensive atlas of microRNA-target interactions
miRWalk2.0 is a comprehensive archive, supplying the largest available collection of predicted and experimentally verified microRNA(miRNA)-target interactions(~949 million).
http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/index.html
Dweep, H et al. miRWalk2.0: a comprehensive atlas of microRNA-target interactions, Nature Methods, 2015.
2. prediction by bioinformatics tools
目前常规的算法主要遵循以下几个常用原则：
（1）miRNA与靶基因的互补性；
（2）miRNA靶位点在不同物种之间的保守性；
（3）miRNA-mRNA双链之间的热稳定性；
（4）miRNA靶位点不会有复杂的二级结构；
（5）miRNA的5’端于靶基因的结合能力强于3’端。
除了这些基本原则外，不同的预测方法还会根据各自总结的规律对算法进行限制和优化。
（1）miRanda
miRanda是将miRNA-mRNA之间的序列匹配，保守性和热稳定性作为计算参数。
download miRanda from this page: http://www.microrna.org/microrna/getDownloads.do
miranda miRNA.fa target_sequence.fa >miRanda.output
miRanda is an algorithm for the detection of potential microRNA target sites in genomic sequences. miRanda reads RNA sequences (such as microRNAs) from file1 and genomic DNA/RNA sequences from file2. Both of these files should be in FASTA format. This is an example:
input file1: miRanda.miRNA.fa
>gi|29565487|emb|AJ550546.1| Drosophila melanogaster microRNA miR-bantam
GTGAGATCATTTTGAAAGCTG
input file2: miRanda.target_sequence.fa
>gi|945100|gb|U31226.1|DMU31226 Drosophila melanogaster head involution defective protein (hid) mRNA, complete cds (3'UTR only)
TGACAAAAAATAAAAAACGAAATCCATCGTGAACAGTTTTGTGTTTTTAAATCAGTTCTAAACACGAAAA
GGGTTGATGAAAAACGCAGAAGAATCCGAAAAACTAACTAACCGAGCAAAAACTTGACTTGAGTGTTGTT
TGACAAATCAGGAAAGATAAAAAACAAATCATAAGAAAAAACTGCACGAAAAATGAAAAAGTTTCTAATA
TTCAAAATCTTGCACAAGAAATACAAAATCAATTAAAGTGAACTCTAACCAAAAGTTGTACACAAAATAA
AAAGCAAAACAAAGCAGCGAAGAACAATCACAAGAAGAGCAAAGTGCCAACAAAGTGCAGGAAGGAAGGA
AGCGGATAAGGACAAAAAGGAAGCCAGCACACACACACACACCCACACAATGGCCGTGCCCTTTTATTTG
CCCGAGGGCGGCGCCGATGACGTAGCGTCGAGTTCATCGGGAGCCTCGGGCAACTCCTCCCCCCACAACC
ACCCACTTCCCTCGAGCGCATCCTCGTCCGTCTCCTCCTCGGGCGTGTCCTCGGCCTCCGCCTCCTCGGC
CTCATCTTCGTCATCCGCATCGTCGGACGGCGCCAGCAGCGCCGCCTCGCAATCGCCGAACACCACCACC
TCGTCGGCCACGCAGACGCCGATGCAGTCTCCACTGCCCACCGACCAAGTGCTATACGCCCTCTACGAGT
GGGTCAGGATGTACCAGAGCCAGCAGAGTGCCCCGCAAATCTTCCAGTATCCGCCGCCAAGCCCCTCTTG
CAATTTCACTGGCGGCGATGTGTTCTTTCCGCACGGCCATCCGAATCCGAACTCGAATCCCCATCCGCGC
ACCCCCCGAACCAGCGTGAGCTTCTCCTCCGGCGAGGAGTACAACTTCTTCCGGCAGCAGCAGCCGCAAC
CACATCCGTCATATCCGGCGCCATCAACACCGCAGCCAATGCCACCGCAGTCAGCGCCGCCGATGCACTG
CAGCCACAGCTACCCGCAGCAGTCGGCGCACATGATGCCACACCATTCCGCTCCCTTCGGAATGGGCGGT
ACCTACTACGCCGGCTACACGCCACCACCCACTCCGAACACGGCCAGTGCGGGCACCTCCAGCTCATCGG
CGGCCTTCGGCTGGCACGGCCACCCCCACAGCCCCTTCACGTCGACCTCCACGCCGTTATCGGCGCCAGT
GGCGCCCAAGATGCGCCTGCAGCGCAGCCAGTCGGATGCGGCCAGACGCAAGCGATTGACCTCGACGGGC
GAGGATGAGCGCGAGTACCAGAGCGATCATGAGGCCACTTGGGACGAGTTTGGCGATCGCTACGACAACT
TTACGGCCGGCCGGGAGCGTCTGCAGGAGTTCAATGGACGCATCCCGCCCCGGAAGAAGAAGAGCTCCAA
TAGCCACTCGAGCAGCAGCAATAATCCAGTCTGCCATACCGACAGCCAGTCCGGTGGTACATCCCAAGCG
GAGAGCGGTGCCATCCATGGCCACATCAGTCAGCAGCGACAGGTGGAGCGAGAACGACAAAAGGCGAAGG
CCGAGAAGAAGAAACCACAGAGCTTCACTTGGCCAACTGTTGTGACCGTTTTCGTTTTGGCCATGGGCTG
TGGCTTCTTTGCGGCGCGATGAAAGCGCAGGAGACGTGTAATCGAATGATCTATAGTGAAATCAGCTAGC
CCTTAAGATATATGCCGATCTAAACATAGTTGTAGTTAAACCGTACATAAGTGCAACGAATTTATTGAAC
TGCAGGAGCGAAAGCAGAAAGTCATTAATTCGTAAACGGATTGTTAGATACACAAACAGCCAACATACAC
GAAGAGTGTGCCTAAGATTAAGAAGGTTGACGGGACACAAGAACAATATATTCTATCTGTCTATGGTAAC
TGCATTTGTATTTCTAAAACGAAACGAAAGATAACAATCTTAACTGCTCAAAGTAATGAAAACTCTTAGA
CTGGCAAGAGACTCAAATCACACTTATTTTTTTGCTGATCCATATTTTTGTACAACCTTTTGAGCGATAT
TTACAAATTATACTAGTACAAAAAAAAGAGAGAGAGAGATAAGCAAAAGAAAACTGCCACTTTTGAGATA
CTTTTGATAATCTTTGATTTGCATTTAATCATTTCCACACTTGCATTTTTTATAAACAACAAACAAAATT
ACTTCCATTGTAGAACAAAGTAAACTGCAATTTCAATGTCTTCGCATTTGTAATTCCGAATTGCAAGAAA
AACAAAAATATTTTAAATATGTTTAACTAGTAGAATTTTTTAAACGTAAGTCCACAAAAACAAGCACATC
TAGCTTTAATTGTTGAAACAAAAGCAGAAAAAACGCAACAAAAAAATGAATGAAAATCATTAAATTAATT
TTGTATATAGTTTTTATGCCATTTTTGTGATGTTTTGTGTCTACGGTTTATGTCATGTTATTTTAGTTAA
ATTTCTTATGATTTATGTTTATTTGTAATATTTTTTGTCATTGTTTGTTCATCATCATATTCAAATTGGT
CTCACAATATAATAGTTTTAAGCTCCACGCCCGGGAGATTGATGGCAAAACGATTGAAATTTGGCCAGAA
GAGAGATAGTTTTCCCCATTCGTACACAGTCTTTTTTGGAATGCACATTAATGATCTCTCACAATGGAAA
TTAATGAAAATTGATCTCCGCAGCTAGCCAAAGTTAAAAAAGAAATGAAGAGGAAAACATATTCTATAGG
CAATTTTCACTATATGCTAGAATTTCCCGGGCGTTTCAATGCTAATCGAATACAGTGACATGAAAGCAAA
CATAGCGAAAATATTAAGAAAATCAATCAAAAAGAAAGAAAAACCAATTCCCAAAAATCGCATTGATCTC
ATGGATTTATACAATACAATTACATCAACCGTTTTTTTACAATGAGAAATGTTATAAAAAGCAGAAAGTG
AAACACAGAAACATAAACAAAAATTAACGAAAAGCTTAGATATAAGTTCGCCAAGCGTTTTAGTTCTATT
TTCTAGAATGTCTAAGTCGGTTTAGTGAGTTTATTAAGCTGTCTTCGGACACAAGTTTATTTGTATATAA
GCAATATTATTTGTGTAGCCTAAGTGACAGTCCCAATCAAATCCAATCCAATATCACCCAGTCCCGGACA
TTTCCCAGCAAAACAATAGACTATTCTCGCGTTCACATGTATCAATCTTAATTTGAATTACCACAAAATG
AAATGAAATACTAAAACCATACACAAATGAAAAATTATTTTTGTAAATTGTTTGCATCAAGTGAGCAAGG
GGATTAGATTAAGGAATCATCCTTGCTTTATCCCCTGCTTATTGCTAATTAGTTTTCACAATGATCTCGG
TAAAGTTTTGTGGCCTTGCGCCCAAAAGTCGTACAGATTTTTGGTTTGCCATAAATACTCGAACAAAAAG
TTAATGAAAAACGAAGCAAATGGAAAAAAAATCAGAATGAAACACAAGAAATTTATATTTTTGACCCAAT
GCTACTTAATCCGTTTTTGTAATTTAAGTATCTTTACTCGACCTTGTATATAGCGCAGTTCGAATCACAG
AATCAAATGCCATTTTTGTATAGAATTTTATTTGGTGCCAAAACAGTGACAGATAATTAAATGTCTATGA
ACCCGTGTATTTCGCATATTATACATTTATACATATATCGTAACTTCAATGATAAGTTTGATTCTGAAAT
TTTGTCAACTCAATTTAAGAAACATTTCTGTTGTAGTTTAGTGATTGCTAGCAGAAAGCACTTTGTTTAA
TTGTACATTTTATATTATGCTGTAATATTTTAATATACATAAATATCATTATTGATCTCATGAATATGTT
CATAAGACAACAAAAATTATATATATGAATACATCTATGTGTATGTGTAAAG
miRanda.output.txt (example)
Forward: Score: 167.000000 Q:2 to 20 R:3340 to 3360 Align Len (18) (83.33%) (94.44%)
Query: 3' gtCGAAAGTTTTACTAGAGTg 5'
|:||||| |||||||||:
Ref: 5' taGTTTTCACAATGATCTCGg 3'
Energy: -24.540001 kCal/Mol
Scores for this hit:
>gi|29565487|emb|AJ550546.1| gi|945100|gb|U31226.1|DMU31226 167.00 -24.54 2 20 3340 3360 18 83.33% 94.44%
mirna Target Score Energy-Kcal/Mol Query-Aln(start-end) Subjetct-Al(Start-End) Al-Len Subject-Identity Query-Identity
（2）psRobot
使用计算机预测植物miRNA靶基因比较简单，因为在植物中miRNA与靶基因几乎是以完全互补配对的方式结合，预测不需要复杂的算法。
website: http://omicslab.genetics.ac.cn/psRobot/target_prediction_1.php
psRobot_tar is designed to find potential small RNA targets
psRobot_tar -s smRNA -t target -o smRNA-target.gTP.standard
input file1: psRobot.miRNA.fa
>smRNA01
TGACAGAAGAGAGTGAGCAC
>smRNA02
TGCCAAAGGAGATTTGCCCTG
input file2: psRobot.target_sequence.fa
>tar01
GTGGGATACTGAGATACTGTTGGTATATTCTCTTCTTCTCCACCGAATATATCTTTGTCTTTGCCCTTTCCCCCTTCCTT
CGATGTGATACCTAATCAACCACTCTCAACACCATCCAACCACCACCTCCAGAGCAAAGAAACAGAGCAGACAACGAAAC
AGAGCAGAGAACGAAACAGAGAGAGAGAAGGAGAGAACGAAACAGAGAGGGAATTCTGTTGGTTTTGCAATGGTTTGGAG
GTTCGTTAATGGCGTCATAGACTCGGGAGATTAAGGGAGAACAAGTACTCTGCATCTTCTGGATTTCTCACTATTTCTCC
TTCTCTAGAGGAATAGGCGAGAAATTGAGATTAATAAAAAGGGGTGTTTTTTGGTTCTCCTTCGTTTGTATAAAGCCTGC
CGCTTCTTAAGGTTCTTTGGAAATTCTCAAGTGATGGAGGAAAATCTGGTTTACTTCGCATGAGCTATTAACGAAATTTC
ATCATGATTGTCTCACTGTCACACTTGTGGGAGTATGCGTGAATGATTGTCAAGAGACCGATCTTGTTTCTCACTTCGTT
TGAACTCTGTCCTGTTTCCCATTTATACTTCAGATTCTAGCTCCTTTAGGGCAAATCTTCTTTGGCAAAAGTAACAATTA
AGCAGCAGCAGCAGAAGTGAAGTTTCTCCCGTTAGGTGGCTTTGATACAAGTCTTGGAGCTTTCGAATTTCTTTGAGGTT
TGAAGCTCCACCCTCATTCTAGGGCATATCTCCTTTGGCAATTGGAACTCCAATCACATCTTGGGTTCCCCTTTGAAGTT
TATCCAACTGGAAACTTCAGTCCATTCGTTGGGCAAATCTCCTTTGGCATTTCGGAGTCCTCAGTTGGCTCACCTCATCT
CTTGTTCGAGCAAATCTCCTTTGGCATTATCCGAGTTCGTAGTTGACTCACGTTATCTTTCGTAGAGCAAATCTCCTTTG
GCATTATCTGAGTCTTACAGTTGACTCCTATTATCTTAGTCGAGCAGTTATCATCACGGTCTGCAGTTTCAGCTGACCGT
TGCTGTCAGTCCATATCAGTCATCTAACGTGAGAATCCACACTCCTATCAAAGTAACTGGATCTTTTAACAAACAGGGAA
GCTACACCGTTTCTCATCAAGGCGTCTAAACTATGGAAATGTCCCTTACTGACTCTGATTGGGATAGCTCCAGCGACAGT
GGTAGCAGTGAACACGAAGAAGTCGAGTTTTCTTATGGTGGACGGGCACAGAACATTTTCTCAAACCTTGAAGAAACCAT
TGGCAAAATCGATGAGTTCCTGTCGTTCGAGAGGGGATTTATGTATGGTGACATTGTGCGGTCCGCCACTGAACCATCAG
GACAGAGTGGCAGGGTTATCAACATAGACATGTTTGTCAATCTCGAAAGTACTCATGGGAAGATCATGAAGGAAGTTGAT
ACCAAGAGGCTTCAAAAGTTGCGTTCTATTTCACTCTCTGATTATGTGATTAACGGACCTTGGGTTGGAAGGGTTGACAA
AATAGTTGAGCGTGTCTCTGTCACCCTTGATGATGGGACCAACTATGAGGTCCTTGTAGATGGTCAAGATAAACTTGTGG
CCATTCCCCCAAATTTACTCGAGGATTCTCAATATTCGTATTACCCAGGGCAAAGAGTTCAGGTAAAGCTGGCCCATGCC
CCCAGATCAACTACATGGTTATGCGGGACCTGGAGAGGAACCCAGGTTATGGGAACTGTTTGCACTGTAGAAGCAGGACT
TGTCTACGTCGATTGGGTTGCCTCCATCGTAATGGAGGGTGATCGGAATTTAACTGCACCTCAAGCTTTGCAGAATCCTG
AGAGTTTAACTTTGTTACCTTGTGTTTCTCATGCGAGTTGGCAGCTTGGTGACTGGTGTATACTCCCTGGCTCTTCCCAC
TGTGATATAGCAGAGCGGCAAACTCCAAATGTGGCTGCCTACAATCTCAATGAATGCCATAAGACATTCCAAAAAGGGTT
TAACAGAAATATGCAGAACTCAGGTTTGGATGAGCTATTTGTTATCACAAAGACAAAGATGAAGGTTGCTGTTATGTGGC
AAGATGGTAGCTGCAGTCTGGGAGTTGATTCCCAACAGCTGCTTCCTGTTGGTGCTGTTAATGCTCATGATTTTTGGCCC
GAACAGTTTGTTGTGGAAAAGGAAACCTGCAACAGCAAAAAATGGGGAGTTGTGAAGGCTGTCAATGCTAAGGAACAAAC
TGTGAAGGTACAATGGACAATACAGGTTGAGAAAGAAGCAACAGGTTGTGTTGATGAGGTGATGGAAGAAATTGTCAGTG
CATATGAACTGCTTGAGCACCCTGATTTTGGATTCTGTTTCAGCGATGTGGTGGTCAAGTTACTTCCAGAAGGAAAATTT
GATCCAAATGCAGACACAATCGTCGCTACAGAGGCGAAACACCTACTTACAGAGAGTGACTACAGTGGCGCATATTTTTT
GTCAAGTATTGGTGTTGTTACAGGTTTTAAAAATGGTTCCGTGAAGGTGAAATGGGCCAATGGTTCTACTAGCAAGGTTG
CACCATGTGAAATTTGGAAAATGGAAAGGTCTGAATATTCCAACTCTAGCACTGTAAGTTCGGAAGGCAGTGTTCAAGAT
CTAAGTCAGAAGATTAGTCAATCGGATGAAGCATCTTCAAACCATCAGGAAACGGGTCTGGTGAAGCTCTACAGTGTTGG
TGAAAGTTGCAACGAGAACATTCCGGAATGTAGTTCATTTTTCCTTCCAAAAGCTGCCATTGGATTTATCACAAACCTTG
CATCAAGCCTTTTTGGTTATCAGGGTTCCACTTCGGTTATAAGTTCACATTCACGTTGCAATGATTCTGAAGATCAAAGT
GACTCTGAGGTCCTTGTTCAAGAAACAGCAGAATCATATGACAACTCTGAAACAAATTCAGGTGAAGTGGATATGACCAC
CACGATGGTCAACATACCTATAGAAGGAAAAGGAATTAACAAGACACTGGATTCAACTCTTCTAGAGAACAGCAGGAACC
AAGTGAGATTCAGACAGTTTGATATGGTTAATGACTGCTCGGACCATCATTTCCTTTCTTCGGATAAAGGGTTGGCTCAG
TCCCAGGTCACAAAGAGTTGGGTGAAGAAAGTCCAGCAAGAATGGAGCAATTTGGAGGCAAATCTTCCGAACACGATATA
TGTGCGTGTGTGTGAAGAAAGGATGGACCTTCTGCGTGCAGCCCTGGTTGGTGCCCCTGGAACGCCATATCACGATGGGC
TTTTCTTTTTCGACATAATGCTTCCACCTCAATATCCTCATGAGCCACCAATGGTACATTATCATTCAGGTGGGATGCGA
CTGAATCCGAACCTGTATGAGTCAGGAAGAGTTTGCTTGAGTCTGCTGAATACATGGAGTGGCTCTGGCACTGAAGTATG
GAACGCAGGGAGCTCCTCCATCCTTCAACTTCTTCTTTCGTTTCAGGCTCTGGTTCTGAATGAGAAGCCTTACTTCAATG
AAGCTGGCTATGATAAGCAGTTGGGCCGAGCCGAGGGAGAGAAAAACTCAGTGAGTTACAATGAGAATGCATTCCTCATA
ACCTGCAAATCCATGATCTCAATGCTCCGTAAGCCTCCAAAGCATTTTGAGATGCTTGTGAAGGACCATTTTACGCACCG
GGCCCAGCATGTTCTGGCTGCGTGCAAGGCTTATATGGAAGGCGTCCCTGTAGGATCATCAGCTAACCTGCAGGGGAACT
CAACCACAAATTCCACCGGTTTCAAGATCATGCTCTCCAAACTCTACCCAAAACTTCTTGAAGCATTCTCAGAGATTGGA
GTTGATTGCGTCCAAGAGATTGGACCAGAATCATAAGGTCGGTAATGTTCTGATGTCAAAACATCTCCCTAACATCCAAT
AACTGCTGTATAAAATGGGTTTGCGAGGTCGGTAGTCTCCTCCCCATGTAGAATATGTTTTGTAATTCAATGCGTGAGAA
TAAAACCGCTGTAAAATAGGCTTATCTTGTATAATATCACAGGGTTGTTACTTGTGCAAGAACGATGTACCATCTTTTGT
AATAAAAAAATTGTGTTTGTATC
>tar02
ATCCGATTCCCCTGAGGCGATTATGGTGTCTCCTCCTTCTCACTCTCGCTCTTAGTAGTAGTCTTTCTTCTCCGATCGCC
GACTAACCTCCGGAGCTTTCTTCGGCGGTCTCTCCTCTCTGTTTTCTACTTGAGGTTTCTCTCTTCTCTTACTTTTTTTA
TTCTTTGTGCATATCTCTCTTCTCTTACTTTACTCACCAAATTTCTAGGAATACTTTTTCTCCTGAGATTTGGGCTTTTA
ATTTAGCTTAATTCTTCTGTTAAGTAGAAATATATCATGGCGAATCTATGAAATTATTTCTTCTGTTAAGTAGACATTGA
AATTTGTTACAATGGCGAATCGTGTTTTTTTTTTGGTCTGTCTCCATTTTTTGGTATTGTCTCTAAGAAAATAAGACGTT
TGTGGTATTTAGTTTATTAGGCGAATTGGATTTGTTGAAGCATGGATAACTTGTATCTTAATCCCTTGGGGTTAGTTAGA
TGAATTGAATGGTCGAGGGACCATTTTGCTTTTTCATGGGAAACCCTGGCTGAGGGGACCGTGATTGGGATCGAAACCTA
ACTTGCTGTCTCATTTTGTTAGTTCAATCTTTATTGCTGGAGATAGATCCCTTTCATTGGCTTAGCTTGGTGCCGTTTTC
TTTGACATATACTTAATTCAATATGTGTGTATCTGTAGACTCAATAGTAAGCTTTAGATTAACCCTTTATTTCATTTAAA
CCTTAGCTAAGAACTTTCTGCAAGTACAAAGAAGGAATGGGTAGGGAAGGGTTTGCAATTATGATGGCTATAGGGTTTTA
ATTGACTTGTTGAAGTCGTGTTCTTTAACAAGTTGTTGATTTTCTGGGCAATCTGATTTGAGGAGTGTGGGACCAGTTGG
AATAAATGCCACTTAGTTTTTGTGTGATGGAGAGCACAAGAGGGAGCTAAGAAAAGTAATACCCTTTCTCTTGCCCTTTT
TGTTGGTCTGGGTGAAACATAGCAAGGTTTCTCTTGCTGAGGTTATTGATAAACGGGTGAGTTAAATAAAAACGTTGATC
AGCAGTGTGTGGTGCTTGGAATTCATAAGGTTTTTCTTCTTTCTTATGCTTTTGGTAGTTAATGGTATTGTCTATTAGGT
ATCTGTTTGGGTTGCCAGCGTTATTTATAGGCTTGACAAGTTTTCTCTTATTTTTGATGTTTGGCTTTCATTTACAATCA
CTAGTTCTGCAAACTTATGGTTGTTTCAGTTTTTGTTACTCTGGCTTTTGGTGATAGGTCTATGAAATCAACCCACATCT
TGAATGGACTGCAACATGGTATCTTCGTTCCCGTGGGACTGGGAGAATTTGATCATGTCCAATCAGTCGAAGACTGAAAA
TGAAAAAAAACAGCAATCTACTGAGTGGGAATTTGAAAAAGGTGAAGGAATTGAATCTATAGTTCCAGATTTCTTAGGCT
TTGAGAAAGTCAGTAGTGGCTCTGCTACTAGTTTCTGGCACACTGCCGTATCAAAAAGCTCGCAGTCGACCTCTATCAAC
TCATCATCTCCCGAGGACAAACGATGCAATCTTGCATCACAAAGTTCCCCTGGAGATTCTTCCAGCAACATAGATTTTCT
CCAGGTGAAACCATCCACAGCTCTCGAGGTACCTATTGCCTCAGCTGAATCAGATCTTTGTTTGAAACTAGGAAAGCGGA
CATACTCTGAAGAATTTTGGGGTAGGAACAATAATGACCTTTCAGCGGTTTCTATGAATTTGTTGACTCCATCTGTTGTT
GCTCGGAAGAAAACCAAATCGTGTGGTCAGAGCATGCAAGTTCCGCGTTGCCAAATTGATGGCTGTGAGCTGGATCTCTC
ATCTTCTAAGGATTATCATCGCAAGCATAGAGTCTGCGAAACGCATTCAAAGTGCCCAAAAGTTGTTGTGAGTGGCCTGG
AACGTCGTTTCTGCCAACAGTGTAGCAGGTTCCATGCTGTCTCAGAATTTGATGAAAAGAAACGAAGCTGCCGCAAACGT
CTTTCTCATCATAATGCAAGGCGTCGCAAGCCACAAGGAGTATTTCCACTGAATTCAGAGAGGGTGTTCGATCGAAGACA
GCATACAAGTATGTTGTGGAATGGGTTGTCCCTTAACACGAGATCTGAAGAAAAGTATACATGGGGTACCACTTATGAGA
CAAAGCCTACACAGATGGAAAGCGGCTTTACTCTGAGCTTCCAGAGAGGTAATGGCTCTGAGGACCAACTGTTTACTGGT
AGCACCCTCTCTTTCTCTGCGTTTCAAACATCTGGTGGGTTCTCAGCAGGGAAATCCAACATTCAACTTCCAGACAAAGG
TGTGGGAGAATGCTCAGGAGGCCTCCATGAATCTCATGATTTCTACAGTGCTCTCTCTCTTCTGTCAACTACTTCGGATT
CACAAGGGATCAAACACACTCCCGTGGCCGAACCACCGCCAATATTTGGCACTTTCCCTAGTCATTTCATCTGAAAGAGT
TAAAAAATGCGAAGTCACTCCATAAAAAGTCGGGTTGCATCATCCGATGGAGTTTGACAGTTTGTTTTCAAATAAAATAA
AAATGTGCAACATCCATTGCTCAAGCTAAACCAGTCACTCGGTTAGCTCAGCTTTTCTTATACATTTCCAAACATATTGT
CAATTTCCTTCTGGATTCTGTTGGTATGACAACTTTGTTACTCTGTCAAACATGTCTACGACTATTTTAAAACCATTTCA
GAGATTA
psRobot.output.txt
Score: target penalty score, lower is better (0-5), default = 2.5
>smRNA01 Score: 1.0 tar02
Query: 1 TGACAGAAGAGAGTGAGCAC 20
|||||||||||||*||||||
Sbjct: 2387 ACTGTCTTCTCTCTCTCGTG 2368
>smRNA02 Score: 0.8 tar01
Query: 1 TGCCAAAGGAGATTTGCCCTG 21
|||||||||||||||||*||:
Sbjct: 963 ACGGTTTCCTCTAAACGAGAT 943
>smRNA02 Score: 1.0 tar01
Query: 1 TGCCAAAGGAGATTTGCCCTG 21
|||||||||||||||||*|*|
Sbjct: 906 ACGGTTTCCTCTAAACGAG-C 887
>smRNA02 Score: 0.8 tar01
Query: 1 TGCCAAAGGAGATTTGCCCTG 21
|||||||||||||||||||*:
Sbjct: 849 ACGGTTTCCTCTAAACGGGTT 829
>smRNA02 Score: 1.2 tar01
Query: 1 TGCCAAAGGAGATTTGCCCTG 21
|||||||||||||*||||||:
Sbjct: 760 ACGGTTTCCTCTATACGGGAT 740
>smRNA02 Score: 0.8 tar01
Query: 1 TGCCAAAGGAGATTTGCCCTG 21
||||||||:|||||||||||:
Sbjct: 627 ACGGTTTCTTCTAAACGGGAT 607
（3）other recommended tools:
RNAhybrid: https://bibiserv2.cebitec.uni-bielefeld.de/rnahybrid
TargetScan: http://www.targetscan.org/vert_72/
psRNATarget: http://plantgrn.noble.org/psRNATarget/ (specific for plant)