CN109754844B

CN109754844B - Method for predicting plant endogenous siRNA on whole genome level

Info

Publication number: CN109754844B
Application number: CN201910020480.0A
Authority: CN
Inventors: 张德强; 卜琛皞; 宋跃朋
Original assignee: Beijing Forestry University
Current assignee: Beijing Forestry University
Priority date: 2019-01-09
Filing date: 2019-01-09
Publication date: 2020-09-01
Anticipated expiration: 2039-01-09
Also published as: CN109754844A

Abstract

The invention provides a method for predicting endogenous siRNAs of plants on the whole genome level, which belongs to the technical field of bioinformatics, and the method utilizes MITE-Hunter to detect MITEs elements in whole genome sequence data of plants, predicts 24-nt siRNA based on the MITEs elements, and finally verifies the siRNA by using Pln24 NT. The invention combines the two bioinformatics tools to increase the prediction flux, thereby rapidly predicting the siRNA on the basis of large data volume, and the method is a method generally applicable to plants.

Description

Method for predicting plant endogenous siRNA on whole genome level

Technical Field

The invention relates to the technical field of bioinformatics, in particular to a method for predicting plant endogenous siRNA on the whole genome level.

Background

Transposons (TEs) are widely present in and constitute the largest component of the genome of eukaryotes, for example, in the genome of dogs, at least 31% are transposons, while in the human genome the proportion of transposons occupied is up to 46%, and in maize, transposons occupy even 85% of the entire genome. Transposons are classified into two major classes, DNA-mediated and RNA-mediated, depending on the manner of transposition. Research shows that retrotransposons account for 34% of the human genome, and mainly consist of Alu sequences, SVA (SINEVNRT-Alu), and long interspersed elements (L1). DNA transposons can be further classified into Terminal Inverted Repeats (TIRs), Miniature inverted repeat transposable elements (MITEs), Helitrons and the like according to structural features.

The transposon is one of the sources of endogenous small-molecule RNA in animals and plants, and can generate siRNA by induction. Research shows that transposon-derived small-molecule RNA and non-coding RNA have close relation and participate in a plurality of important regulation functions. Transposons can produce a variety of RNAs by induction, for example: miRNA, siRNA, piRNA, and the like. In the past, siRNAs have been thought to be formed by endogenous double-stranded RNA, but in some species, such as butterflies, etc., transposons have been found to be important sources of siRNA.

Miniature inverted repeat transposable elements (MITEs) are a Class II non-autonomously transposable DNA element found in bacterial, plant and animal genomes in recent years. Since MITEs lack coding sequences and evolve themselves very rapidly, it is still difficult for existing technical means to identify MITEs accurately. However, the rapid expansion of genomic databases now provides many advantages for the study of MITEs.

The current research shows that transposons are involved in many important physiological activities, exhibit many genetic effects, and are a class of DNA elements with important functions and research prospects, for example, the insertion of transposons into genes can cause insertion mutation, so that new genes appear at the inserted positions, resulting in the loss or change of the original gene functions. The MITEs have important functions and functions as a special transposon, and the identification of the MITEs in the whole genome based on the MITE _ Hunter can provide a new method and a new way for researching the MITEs.

Small interfering RNA (siRNA) is sometimes called short interfering RNA (short interfering RNA) or silencing RNA (silencing RNA), and is a double-stranded RNA molecule with the length of 20-25 bp, and an RNA interference pathway is regulated. Small interfering RNA inhibits the translation process by degrading complementary mRNA to interfere with the expression of a specific gene through post-transcriptional regulation. sirnas exert antiviral functions in mechanisms associated with RNA inhibition and play an important role in the formation of genomic chromatin. siRNA can enter the interior of a cell by transfection. In principle, any one gene can be inhibited by synthetic siRNA with complementary sequences, which is an important tool for validating gene function and drug targeting in the post-genome era.

The stem-loop structure of siRNA precursors is quite conserved in different species and has great sequence homology. The prediction method at present is mainly based on the two characteristics of siRNA, bioinformatics software is utilized to search and compare in a sequenced genome sequence, and candidate siRNA meeting conditions and siRNA molecules which are verified through experiments are analyzed and compared according to the sequence of homology, so that the quantity and distribution of siRNA of the species are finally determined. The prediction method at the present stage has large workload, needs genome searching comparison, homology analysis and the like, has long searching comparison process time, usually has species limitation, is mostly only suitable for animals, is also only suitable for a few model plants such as arabidopsis thaliana and the like in the aspect of plants, and lacks of a prediction method which is generally suitable for plants.

Disclosure of Invention

The invention aims to provide a method for predicting plant endogenous siRNA on the whole genome level, and the method has the advantages of rapidness and high flux.

In order to achieve the above object, the present invention provides the following technical solutions:

the invention provides a method for predicting plant endogenous siRNA on the whole genome level, which comprises the following steps:

1) using MITE-Hunter to screen candidate MITEs element from the whole genome sequence data;

2) analyzing the candidate MITEs element in the step 1) by using a multi-sequence comparison method, and filtering false positive results again to obtain an MITEs element sample;

3) extracting sequences with the length of 40-60 bp at two ends of each MITEs element sample from the MITEs element samples in the step 2) to obtain sequences to be analyzed;

4) performing complementary blast analysis on the sequence to be analyzed in the step 3), and screening the sequence with mismatch of 0 and sequence length of 24-100 nt to obtain candidate siRNA;

5) comparing the candidate siRNA in the step 4) with the siRNA in the database by using Pln24NT software, wherein the siRNA with the consistent comparison result is the plant endogenous siRNA.

Preferably, the length of the sequence extracted at both ends of each MITEs element in step 3) is 50 bp.

Preferably, the whole genome sequence data in step 1) is gene fragment data, and the length of each gene fragment is 1.8-2.2 kb.

Preferably, the screening in step 1) is performed by identifying TIR and TSD structural features, obtaining transposons with flanking sequences, and filtering structurally similar false positive results to obtain candidate MITEs elements.

Preferably, after obtaining the MITEs element sample, the method further comprises identifying the MITEs element sample as belonging to a different family.

The invention has the beneficial effects that: the invention provides a method for predicting plant endogenous siRNA at the genome-wide level, which utilizes plant genome-wide sequence data, utilizes MITE-Hunter to detect MITEs elements in the genome-wide level, predicts 24-nt siRNA based on the MITEs elements and finally verifies the siRNA by using Pln24 NT. The invention combines the two bioinformatics tools to increase the prediction flux, thereby rapidly predicting the siRNA on the basis of large data volume, and the method is a method generally applicable to plants.

Description of the drawings:

FIG. 1 is a schematic diagram of the structure of MITEs;

FIG. 2 shows a flow chart of a method for predicting endogenous siRNA in plants at the whole genome level;

FIG. 3 shows the number of families classified by the Mites of Populus tomentosa;

FIG. 4 shows a portion of Chinese white poplar MITEs;

FIG. 5 shows a schematic diagram of the arrangement structure of Chinese white poplar MITEs;

FIG. 6 shows the verification (part) of the prediction result of the whole genome endogenous siRNA of populus trichocarpa;

FIG. 7 shows the validation of the prediction results of Arabidopsis thaliana whole genome endogenous siRNA (part).

Detailed Description

The method utilizes MITE-Hunter to screen and obtain candidate MITEs elements from the whole genome sequence data; the schematic structure of MITEs is shown in FIG. 1; preferably, the screening process comprises identifying TIR and TSD structural features to obtain transposons with flanking sequences, and filtering structurally similar false positive results to obtain candidate MITEs elements; removing the result with only TIR or TSD sequence according to the alignment condition of the preferred pairing sequence of the false positive result with similar filter structure; the whole genome sequence data is preferably gene fragment data; the length of each gene fragment is preferably 1.8-2.2 kb, more preferably 2 kb.

In the present invention, the operating parameters of the MITE-Hunter are preferably:

s12345678 represents the running of the program starting from the initial step 1 and ending of the program at step 8;

i genome sequencing data (input file);

p is preferably 0.25 to 1; -P represents the percentage of genome sequencing data to the full genome data as input file;

g-name (by default, "genome") indicates the entire process name, the name of the subsequent output file, which will start with;

-n is preferably 5; n represents the maximum value of the number of groups into which the MITEs elements are grouped, and "-n 5" in the example represents a grouping with a maximum of 5 groups.

After candidate MITEs elements are obtained, the candidate MITEs elements are analyzed by using a multi-sequence comparison method, and false positive results are filtered again to obtain an MITEs element sample.

According to the invention, after the MITEs element sample is obtained, the MITEs element sample is identified and classified into different families. In the present invention, the classification of samples of MITEs into distinct families serves to further analyze the structural and functional characteristics of MITEs.

After classifying MITEs element samples, extracting sequences with the length of 40-60 bp at two ends of each MITEs element sample from the MITEs element samples to obtain sequences to be analyzed; the length of the sequence extracted from two ends of each MITEs element is preferably 45-55 bp, and more preferably 50 bp; the script of the extracted sequence is a sequence extraction script; the writing program of the sequence extraction script is a python language or a perl language; the sequence extraction script is not particularly limited, and the conventional sequence extraction script in the field can be adopted.

In the invention, the reason for setting the length of the sequences extracted from the two ends of each MITEs element to be 40-60 bp is that the siRNA precursor has a stem-loop structure, so that a corresponding complementary sequence exists on the transposon, and because the two ends of the MITEs element usually have terminal inverted repeat sequences with the length of more than 10nt, the extracted sequences with the length of slightly more than the length of the terminal inverted repeat sequences are selected to increase the accuracy of prediction, and the longer the complementary sequences are, the more compact the pairing is, the more easily the stem-loop structure is formed, so that the siRNA is generated; however, the complementary sequence is too long, which not only increases the amount of calculation but also significantly reduces the screening result, thereby losing the meaning of prediction.

After obtaining a sequence to be analyzed, performing complementary blast analysis on the sequence to be analyzed, and screening a sequence with mismatch of 0 and sequence length of 24-100 nt to obtain candidate siRNA; the sequence with mismatch of 0 is a completely matched sequence, and the sequence can form a tighter stem-loop structure, so that siRNA can be generated more easily, and the prediction accuracy can be greatly improved.

In the invention, the complementary blast analysis of the sequence to be analyzed is used for checking whether the extracted sequences at two ends of a single MITEs element can be complementarily paired, and if so, the siRNA derived from the MITEs is proved to be more easily formed, so that the prediction result is more reliable. The invention can integrate a large amount of data when performing the step, and simultaneously perform the step, so the invention has the characteristic of high flux.

The reason for selecting the sequence with the sequence length of 24-100 nt is that the length of the endogenous siRNA is generally 21-24 nt, the shortest length is set to be 24nt in order to improve the accuracy, and the maximum length range of the selected sequence is set to be 100nt due to the fact that a plurality of siRNAs are closely arranged together to form an siRNA cluster; however, the length of the selected sequence is too large, which increases unnecessary operations and results in an increase in screening time.

In the present invention, the flow chart of the method for predicting the plant endogenous siRNA at the genome-wide level is shown in FIG. 2.

The following examples are provided to illustrate the method of predicting plant endogenous siRNA at genome-wide level, but they should not be construed as limiting the scope of the present invention.

Example 1 prediction of Whole genome endogenous siRNA of hairy fruit poplar

The Populus tomentosa genome sequencing file (found by searching from a database, https:// www.ncbi.nlm.nih.gov/genome/98# opennewwindow) was selected, the MITE-Hunter software was used to identify the MITEs element at the genome wide level, the genome wide MITEs element was identified and the result was used as the input result for the next step, the 24-nt siRNA was predicted, and verified with Pln24 NT.

The operation steps are as follows:

1. MITE-Hunter operation

The MITE-Hunter was run using the following commands, taking the Populus tomentosa genome as an example: perl MITE _ Hunter _ manager. pl-i/iob _ home/srwlab/vehell/scratch/Data _ Room/PTC/Ptc _ b 5-g Ptc-n 5-S12345678.

2. MITE-Hunter output result processing

After the operation result is output, 2306 MITEs elements in the populus tomentosa genome are identified together, the counted number of the MITEs classified families is determined, the identified candidate MITEs elements are classified into 89 families in total, sequences with the length of 50bp at two ends of each MITEs element are extracted, complementary blast analysis is carried out, and completely matched (without any mismatch) sequences are screened, the length range is 24-100 nt, and the sequences are used as candidate siRNA. The results of the genomic mitres screen of populus tomentosa are shown in table 1 and fig. 3 and 4, wherein fig. 3 shows the number of mitella tomentosa mitsung families and fig. 4 shows the partial mitella tomentosa MITEs elements.

TABLE 1 Whole genome MITEs element identification results (partial)

Name (R)	Position of	Starting position	End position
				test_2_74181	Chr19	6939894	6939876
test_2_234271	Chr15	48	66
				test_1_229785	Chr19	6064277	6064261
test_2_70675	Chr15	3637466	3637448
				test_2_250084	Chr08	6263945	6263961
test_2_39898	Chr09	6000054	6000037
				test_2_111478	Chr04	20399739	20399712
test_2_168739	Chr04	8177420	8177402
				test_1_297918	Chr01	27687553	27687576
test_2_289532	Chr13	2169324	2169344
				test_1_294968	Chr13	4291530	4291550
test_1_191549	Chr16	12104841	12104823
				test_1_197978	Chr11	11186907	11186927
test_1_7257	Chr03	9490164	9490181
				test_2_214385	Chr11	9782531	9782550
test_1_112575	Chr19	2404025	2404041
				test_2_82283	scaffold_63	71949	71967
test_1_101157	Chr01	11601184	11601152
				test_2_148914	Chr05	15681037	15681019
test_1_91711	Chr10	9132317	9132298
				test_2_159756	Chr06	8082520	8082501
test_2_179877	Chr08	3088670	3088650
				test_2_290934	Chr05	2877251	2877272
test_2_888	Chr02	15159899	15159921
				test_1_7055	Chr07	6343690	6343708
test_1_70997	Chr14	309007	308983
				test_1_139882	Chr01	14979254	14979276
test_2_165622	Chr13	7756223	7756244
				test_2_312527	Chr19	1395247	1395225
test_2_123882	Chr06	19103173	19103191
				test_1_182023	Chr18	10810507	10810488
test_2_84564	Chr08	10241083	10241064
				test_2_153321	Chr15	8656417	8656437
test_1_71146	Chr12	3163110	3163088
				test_2_236737	Chr15	10615453	10615436
test_1_38647	Chr06	17382258	17382279
				test_2_195729	Chr10	20189564	20189540
test_2_164798	Chr02	15939345	15939366
				test_1_203741	Chr02	6400198	6400175
test_2_111298	Chr01	37593763	37593744
				test_1_128909	Chr03	21214218	21214198
test_2_193989	Chr17	1317128	1317148
				test_2_62765	Chr01	6327840	6327864
test_2_88754	Chr09	7248055	7248071
				test_1_32349	Chr09	12427276	12427295
test_2_289774	Chr02	800344	800323
				test_2_24478	Chr12	7796366	7796386
test_1_180941	Chr10	1417711	1417689
				test_1_223222	Chr12	11557119	11557102
test_1_245821	Chr1	3645744	3646731

3. Tailoring MITEs results

The screened MITEs results are subjected to preliminary sorting for prediction of the next step, and the main format is shown in fig. 5. Starting with the name of MITEs (name of MITE-Hunter runtime), the name is exclusive by one line, and the second line starts, which is the sequence information of the corresponding MITEs.

4. Extraction of 50bp sequences at both ends of the MITEs element

And extracting the sequences of 50bp at both ends of the MITEs element by using a sequence extraction script.

5. Predicting 24-nt siRNA

And (3) according to the extracted sequences, performing analysis by blast, screening sequences with the length of 24-100 nt of complete match (mismatch is 0), and taking the predicted final result as the predicted result, wherein the result of part of siRNA predicted by the whole genome of the populus trichocarpa is shown in a table 2.

TABLE 2 partial siRNA results predicted for the entire genome of Populus mauritiana

Name (R)	Sequence numbering	Sequence of
			test_1_191549	SEQ ID NO.1	CTCCCTCCATCCCAAAATATAAGGCATAACCACT
test_1_7257	SEQ ID NO.2	ATGAATGTGGGAAATGCTAGAATGA
			test_2_214385	SEQ ID NO.3	AATATGATTTTAATGGAAAATCGCAAAACTA
test_1_101157	SEQ ID NO.4	CTCCCTCCTTCCCAAATTGATCATCATATA
			test_2_148914	SEQ ID NO.5	CCCAATCCTGGGTTTGAATCTGGACAT
test_1_91711	SEQ ID NO.6	AGAGTAAATTTCACAAAACTACAT
			test_2_159756	SEQ ID NO.7	AATATAAGGGATTTTGGGTGGATGTG
test_2_179877	SEQ ID NO.8	CTGGATTTTTCACATTTTGGTCCTTTT
			test_2_290934	SEQ ID NO.9	CTCCCTCCGTCCCAATATATAGCAACCTAGGATGGG
test_2_888	SEQ ID NO.10	CCTAGGATGGGACCCATCCTAGGTT
			test_1_7055	SEQ ID NO.11	CTACCTCCGTCCCAAAATAATTGTA
test_1_70997	SEQ ID NO.12	CTCCCTCCGTCCCAAAATATAAGCATTTTTAGCTAT
			test_2_195729	SEQ ID NO.13	AATGATAATATGTACTAAAGGACT
test_2_164798	SEQ ID NO.14	TTAGCTCTATATAGGAGTCAAGGAGACG
			test_1_203741	SEQ ID NO.15	ATTCCCCATCCCCATCCCACCAAAATTCCC
test_2_111298	SEQ ID NO.16	AATATGTGTAGAAAACTAGAAATTGA
			test_2_193989	SEQ ID NO.17	CCTTCAATATACCTTTATAGATTTTAATAGTA
test_2_62765	SEQ ID NO.18	AATTATACCTCATTTTATATAAAATGAGCTAATTA
			test_2_289774	SEQ ID NO.19	GTATCTATTATAAATTTCTTGTTATACTTATCATTCC
test_1_223222	SEQ ID NO.20	CATAAGAATTTAACGGTCAACTAACGGTCAACTA

6. Authentication

The position information of the predicted siRNA is compared by using Pln24NT and the position information of the siRNA in the published database (rice and corn siRNA database: http:// sundarlab. ucdavis. edu/smrnas /), and the existing prediction result is verified. Of the 50 randomly selected siRNA predictors, 34 were found in the known database, and another 16 predicted sirnas were not in the known database, with a predicted accuracy of 68%. The results are shown in FIG. 6.

Example 2: arabidopsis whole genome siRNA prediction

Selecting an existing arabidopsis genome file (TAIR-arabidopsis information resource website https:// www.arabidopsis.org /), adopting the method disclosed by the invention, utilizing MITE-Hunter software to screen MITEs elements, processing the result, utilizing the extracted 50bp sequences at two ends to perform complementary blast analysis, and predicting endogenous siRNA. Finally, the verification is carried out by using Pln24 NT.

The operation steps are as follows:

1. MITE-Hunter operation

The MITE-Hunter is run with the following commands:

perl MITE_Hunter_manager.pl -i /iob_home/srwlab/vehell/scratch/Data/ref/genome/ath.genome -g ATH -n 5 -S 12345678。

2. MITE-Hunter output result processing

After the operation result is output, counting the number of MITEs classified families, extracting sequences with the length of 50bp at two ends of each MITEs element, carrying out complementary blast analysis, and screening completely matched (without any mismatch) sequences with the length ranging from 24nt to 100nt to serve as candidate siRNA.

3. Tailoring MITEs results

The screened MITEs results are subjected to preliminary sorting for prediction in the next step, in a format consistent with that shown in the previous embodiment.

4. Extraction of 50bp sequences at both ends of the MITEs element

And extracting 50bp sequences at two ends of the MITEs element by using a foot sequence extraction script.

5. Predicting 24-nt siRNA

And (3) performing analysis by blast according to the extracted sequences, and screening sequences with the length of 24-100 nt of complete match (mismatch is 0) as a predicted final result.

6. Authentication

Comparing the predicted siRNA position information by using Pln24NT and the position information of siRNA in a published database, and verifying the existing prediction result. The predicted siRNA results for the arabidopsis genome are shown in table 3 (50 random selections). Out of 50 predicted results of randomly selected siRNAs, 38 existing siRNAs were detected, and another 12 predicted siRNAs were not in the range of the known database, with a prediction accuracy of about 76%. The results are shown in FIG. 7.

TABLE 3 partial siRNA results predicted from Arabidopsis entire genome

Tag_name		Tag_sequence
			ATH__18_621	SEQ ID NO.21	ATTCTGAACTAAAGCAAAGACTGA
ATH__22213_2	SEQ ID NO.22	TTATAGTCACGGCTCTGGGTGAAG
			ATH__22392_2	SEQ ID NO.23	AGCTTTTCCACCATCTTTCAC
ATH__24_530	SEQ ID NO.24	AGCAGAGGGCAGAGAATCAATCAG
			ATH__24801_2	SEQ ID NO.25	AAGATAACTAGCAAAAGCTAGCAT
ATH__25_525	SEQ ID NO.26	AACAGAAGACTTACAAACATGATA
			ATH__33512_2	SEQ ID NO.27	TTCTCCAACGGAACCAGCTTGTGAGAGTCCAATCATCAC
ATH__36745_2	SEQ ID NO.28	AGAGAACAAGGCTAGCTAGAAAGA
			ATH__382_86	SEQ ID NO.29	ACCCATCCCACCGGTTATTTCCTACGAAGAAGAA
ATH__388_85	SEQ ID NO.30	CAAGAAAGACTACGACGAAGAAAA
			ATH__392_85	SEQ ID NO.31	AGAACGAACCAGAAGAAAATGAAG
ATH__393_85	SEQ ID NO.32	ATGGAAGACTCTCATGGAAGACGA
			ATH__395_85	SEQ ID NO.33	CTATAAGAAGAAGTAACGGAGAAG
ATH__39806_2	SEQ ID NO.34	ACAGTTTTTCATATTTATATCAATCA
			ATH__401_84	SEQ ID NO.35	GACGAACGGAAAAGACGGTAATTT
ATH__41788_2	SEQ ID NO.36	AGGCTAGACAGAAGATTACAAAAC
			ATH__501_72	SEQ ID NO.37	ATCGACGAACACGGATGATAAAAA
ATH__504_72	SEQ ID NO.38	GAAGATCCTGTCTTGCTCTTCCTCCATAAG
			ATH__523_69	SEQ ID NO.39	ACAAATATTGTTGTAGAAGATGGA
ATH__524_69	SEQ ID NO.40	AGCAGGACGTTCTTCAATCTTTAG
			ATH__702_54	SEQ ID NO.41	GAAGGAAAGACTTATACAAAACAC
ATH__714_53	SEQ ID NO.42	AATCCGGGCTAGAAGCGACGCATG
			ATH__715_53	SEQ ID NO.43	ACAGATCAACAGAAAACTCGGCAT

As can be seen from the above examples, the present invention provides a method for predicting endogenous siRNA in plants at the whole genome level, which enables rapid prediction of siRNA on a large data volume basis, and which is a method generally applicable to plants.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Sequence listing

<110> Beijing university of forestry

<120> a method for predicting plant endogenous siRNAs at the whole genome level

<160>43

<170>SIPOSequenceListing 1.0

<210>1

<211>34

<212>DNA

<213>Chinese white poplar

<400>1

ctccctccat cccaaaatat aaggcataac cact 34

<210>2

<211>25

<212>DNA

<213>Chinese white poplar

<400>2

atgaatgtgg gaaatgctag aatga 25

<210>3

<211>31

<212>DNA

<213>Chinese white poplar

<400>3

aatatgattt taatggaaaa tcgcaaaact a 31

<210>4

<211>30

<212>DNA

<213>Chinese white poplar

<400>4

ctccctcctt cccaaattga tcatcatata 30

<210>5

<211>27

<212>DNA

<213>Chinese white poplar

<400>5

cccaatcctg ggtttgaatc tggacat 27

<210>6

<211>24

<212>DNA

<213>Chinese white poplar

<400>6

agagtaaatt tcacaaaact acat 24

<210>7

<211>26

<212>DNA

<213>Chinese white poplar

<400>7

aatataaggg attttgggtg gatgtg 26

<210>8

<211>27

<212>DNA

<213>Chinese white poplar

<400>8

ctggattttt cacattttgg tcctttt 27

<210>9

<211>36

<212>DNA

<213>Chinese white poplar

<400>9

ctccctccgt cccaatatat agcaacctag gatggg 36

<210>10

<211>25

<212>DNA

<213>Chinese white poplar

<400>10

cctaggatgg gacccatcct aggtt 25

<210>11

<211>25

<212>DNA

<213>Chinese white poplar

<400>11

ctacctccgt cccaaaataa ttgta 25

<210>12

<211>36

<212>DNA

<213>Chinese white poplar

<400>12

ctccctccgt cccaaaatat aagcattttt agctat 36

<210>13

<211>24

<212>DNA

<213>Chinese white poplar

<400>13

aatgataata tgtactaaag gact 24

<210>14

<211>28

<212>DNA

<213>Chinese white poplar

<400>14

ttagctctat ataggagtca aggagacg 28

<210>15

<211>30

<212>DNA

<213>Chinese white poplar

<400>15

attccccatc cccatcccac caaaattccc 30

<210>16

<211>26

<212>DNA

<213>Chinese white poplar

<400>16

aatatgtgta gaaaactaga aattga 26

<210>17

<211>32

<212>DNA

<213>Chinese white poplar

<400>17

ccttcaatat acctttatag attttaatag ta 32

<210>18

<211>35

<212>DNA

<213>Chinese white poplar

<400>18

aattatacct cattttatat aaaatgagct aatta 35

<210>19

<211>37

<212>DNA

<213>Chinese white poplar

<400>19

gtatctatta taaatttctt gttatactta tcattcc 37

<210>20

<211>34

<212>DNA

<213>Chinese white poplar

<400>20

cataagaatt taacggtcaa ctaacggtca acta 34

<210>21

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>21

attctgaact aaagcaaaga ctga 24

<210>22

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>22

ttatagtcac ggctctgggt gaag 24

<210>23

<211>21

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>23

agcttttcca ccatctttca c 21

<210>24

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>24

agcagagggc agagaatcaa tcag 24

<210>25

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>25

aagataacta gcaaaagcta gcat 24

<210>26

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>26

aacagaagac ttacaaacat gata 24

<210>27

<211>39

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>27

ttctccaacg gaaccagctt gtgagagtcc aatcatcac 39

<210>28

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>28

agagaacaag gctagctaga aaga 24

<210>29

<211>34

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>29

acccatccca ccggttattt cctacgaaga agaa 34

<210>30

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>30

caagaaagac tacgacgaag aaaa 24

<210>31

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>31

agaacgaacc agaagaaaat gaag 24

<210>32

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>32

atggaagact ctcatggaag acga 24

<210>33

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>33

ctataagaag aagtaacgga gaag 24

<210>34

<211>26

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>34

acagtttttc atatttatat caatca 26

<210>35

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>35

gacgaacgga aaagacggta attt 24

<210>36

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>36

aggctagaca gaagattaca aaac 24

<210>37

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>37

atcgacgaac acggatgata aaaa 24

<210>38

<211>30

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>38

gaagatcctg tcttgctctt cctccataag 30

<210>39

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>39

acaaatattg ttgtagaaga tgga 24

<210>40

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>40

agcaggacgt tcttcaatct ttag 24

<210>41

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>41

gaaggaaaga cttatacaaa acac 24

<210>42

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>42

aatccgggct agaagcgacg catg 24

<210>43

<211>24

<212>DNA

<213> Arabidopsis thaliana (Arabidopsis thaliana)

<400>43

acagatcaac agaaaactcg gcat 24

Claims

1. A method of predicting endogenous siRNA in a plant at the genome-wide level, comprising the steps of:

2) analyzing the candidate MITEs element in the step 1) by using a multi-sequence comparison method, and filtering false positive results to obtain an MITEs element sample;

5) comparing the candidate siRNA in the step 4) with the existing siRNA in the database by using Pln24NT software, wherein the siRNA with consistent comparison result is plant endogenous siRNA;

the screening process in step 1) is to obtain transposons with flanking sequences by identifying TIR and TSD structural features, and to filter structurally similar false positive results to obtain candidate MITEs elements.

2. The method of claim 1, wherein the length of the sequence extracted across each MITEs element in step 3) is 50 bp.

3. The method according to claim 1, wherein the whole genome sequence data in step 1) are gene fragment data, and each gene fragment has a length of 1.8-2.2 kb.

4. The method of claim 1, wherein after obtaining the MITEs element sample, further comprising identifying the MITEs element sample as classified into a different family.