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image of Identification and Analysis of Plant miRNAs: Evolution of In-silicoResources and Future Challenges

Abstract

Endogenous small RNAs (miRNA) are the key regulators of numerous eukaryotic lineages playing an important role in a broad range of plant development. Computational analysis of miRNAs facilitates the understanding of miRNA-based regulations in plants. The discovery of small non-coding RNAs has led to a greater understanding of gene regulation, and the development of bioinformatic tools has enabled the identification of microRNAs (miRNAs) and their targets. The need for comprehensive miRNA analysis is being accomplished by the development of advanced computational tools/algorithms and databases. Each resource has its own specificity and limitations for the analysis. This review provides a comprehensive overview of various algorithms used by computational tools, software, and databases for plant miRNA analysis. However, over a period of about two decades, a lot of knowledge has been added to our understanding of the biogenesis and functioning of miRNAs in other plants. Several parameters were already integrated and others need to be incorporated in order to give more accurate and efficient results. The reassessment of computational recourses (based on old algorithms) is required on the basis of new miRNA research and development. Generally, computational methods, including ab-initio and homology search-based methods, are used for miRNA identification and target prediction. This review presents the new challenges faced by the existing computational methods and the need to develop new tools and advanced algorithms and highlight the limitations of existing computational tools and methods, and emphasizing the need for a comprehensive platform for miRNA gene exploration.

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/content/journals/cbio/10.2174/0115748936341082241015124457
2024-11-04
2025-01-31
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References

  1. Lee R.C. Feinbaum R.L. Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993 75 5 843 854 8252621
    [Google Scholar]
  2. Ruvkun G. Giusto J. The Caenorhabditis elegans heterochronic gene lin-14 encodes a nuclear protein that forms a temporal developmental switch. Nature 1989 338 6213 313 319 2922060
    [Google Scholar]
  3. Moss E.G. Lee R.C. Ambros V. The cold shock domain protein LIN-28 controls developmental timing in C. elegans and is regulated by the lin-4 RNA. Cell 1997 88 5 637 646 10.1016/S0092‑8674(00)81906‑6 9054503
    [Google Scholar]
  4. Reinhart B.J. Slack F.J. Basson M. Pasquinelli A.E. Bettinger J.C. Rougvie A.E. Horvitz H.R. Ruvkun G. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 2000 403 6772 901 906 10.1038/35002607 10706289
    [Google Scholar]
  5. Abrahante J.E. Daul A.L. Li M. Volk M.L. Tennessen J.M. Miller E.A. Rougvie A.E. The Caenorhabditis elegans hunchback-like gene lin-57/hbl-1 controls developmental time and is regulated by microRNAs. Dev. Cell 2003 4 5 625 637 10.1016/S1534‑5807(03)00127‑8 12737799
    [Google Scholar]
  6. Lin S.Y. Johnson S.M. Abraham M. Vella M.C. Pasquinelli A. Gamberi C. Gottlieb E. Slack F.J. The C elegans hunchback homolog, hbl-1, controls temporal patterning and is a probable microRNA target. Dev. Cell 2003 4 5 639 650 10.1016/S1534‑5807(03)00124‑2 12737800
    [Google Scholar]
  7. Slack F.J. Basson M. Liu Z. Ambros V. Horvitz H.R. Ruvkun G. The lin-41 RBCC gene acts in the C. elegans heterochronic pathway between the let-7 regulatory RNA and the LIN-29 transcription factor. Mol. Cell 2000 5 4 659 669 10.1016/S1097‑2765(00)80245‑2 10882102
    [Google Scholar]
  8. Arazi T. Talmor-Neiman M. Stav R. Riese M. Huijser P. Baulcombe D.C. Cloning and characterization of micro‐RNAs from moss. Plant J. 2005 43 6 837 848 10.1111/j.1365‑313X.2005.02499.x 16146523
    [Google Scholar]
  9. He X. Zhang Q. Liu Y. Pan X. Cloning and identification of novel microRNAs from rat hippocampus. Acta Biochim. Biophys. Sin. (Shanghai) 2007 39 9 708 714 10.1111/j.1745‑7270.2007.00324.x 17805466
    [Google Scholar]
  10. Long J.E. Chen H.X. Identification and characteristics of cattle microRNAs by homology searching and small RNA cloning. Biochem. Genet. 2009 47 5-6 329 343 10.1007/s10528‑009‑9234‑6 19267191
    [Google Scholar]
  11. Luo X. Gao Z. Shi T. Cheng Z. Zhang Z. Ni Z. Identification of miRNAs and their target genes in peach (Prunus persica L.) using high-throughput sequencing and degradome analysis. PLoS One 2013 8 11 e79090 10.1371/journal.pone.0079090 24236092
    [Google Scholar]
  12. Prakash P. Ghosliya D. Gupta V. Identification of conserved and novel microRNAs in Catharanthus roseus by deep sequencing and computational prediction of their potential targets. Gene 2015 554 2 181 195 10.1016/j.gene.2014.10.046 25445288
    [Google Scholar]
  13. Wang C. Han J. Liu C. Kibet K.N. Kayesh E. Shangguan L. Li X. Fang J. Identification of microRNAs from Amur grape (vitis amurensis Rupr.) by deep sequencing and analysis of microRNA variations with bioinformatics. BMC Genomics 2012 13 1 122 10.1186/1471‑2164‑13‑122 22455456
    [Google Scholar]
  14. Ye X. Song T. Liu C. Feng H. Liu Z. Identification of fruit related microRNAs in cucumber ( Cucumis sativus L.) using high-throughput sequencing technology. Hereditas 2014 151 6 220 228 10.1111/hrd2.00057 25588308
    [Google Scholar]
  15. Bonnet E. He Y. Billiau K. Van de Peer Y. TAPIR, a web server for the prediction of plant microRNA targets, including target mimics. Bioinformatics 2010 26 12 1566 1568 10.1093/bioinformatics/btq233 20430753
    [Google Scholar]
  16. Dai X. Zhao P.X. psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res. 2011 39 Web Server issue Suppl. 2 W155 W159 10.1093/nar/gkr319 21622958
    [Google Scholar]
  17. Griffiths-Jones S. miRBase: microRNA sequences and annotation. Current protocols in bioinformatics John Wiley & Sons, Inc 2010 10.1002/0471250953.bi1209s29
    [Google Scholar]
  18. Hackenberg M. Rodríguez-Ezpeleta N. Aransay A.M. miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res. 2011 39 Web Server issue Suppl. W132 W138 10.1093/nar/gkr247 21515631
    [Google Scholar]
  19. Sun X. Dong B. Yin L. Zhang R. Du W. Liu D. Shi N. Li A. Liang Y. Mao L. PMTED: a plant microRNA target expression database. BMC Bioinformatics 2013 14 1 174 10.1186/1471‑2105‑14‑174 23725466
    [Google Scholar]
  20. Yang X. Li L. miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics 2011 27 18 2614 2615 10.1093/bioinformatics/btr430 21775303
    [Google Scholar]
  21. Li J. Reichel M. Li Y. Millar A.A. The functional scope of plant microRNA-mediated silencing. Trends Plant Sci. 2014 19 12 750 756 10.1016/j.tplants.2014.08.006 25242049
    [Google Scholar]
  22. Tong Y. Peng H. Zhan C. Fan L. Ai T. Wang S. Genome-wide analysis reveals diversity of rice intronic miRNAs in sequence structure, biogenesis and function. PLoS One 2013 8 5 e63938 10.1371/journal.pone.0063938 23717514
    [Google Scholar]
  23. Han Y. Hu Z. Zheng D. Gao Y. Analysis of promoters of microRNAs from a Glycine max degradome library. J. Zhejiang Univ. Sci. B 2014 15 2 125 132 10.1631/jzus.B1300179 24510705
    [Google Scholar]
  24. Zhao X. Li L. Comparative analysis of microRNA promoters in Arabidopsis and rice. Genomics Proteomics Bioinformatics 2013 11 1 56 60 10.1016/j.gpb.2012.12.004 23453017
    [Google Scholar]
  25. Zhao X. Zhang H. Li L. Identification and analysis of the proximal promoters of microRNA genes in Arabidopsis. Genomics 2013 101 3 187 194 10.1016/j.ygeno.2012.12.004 23295247
    [Google Scholar]
  26. Kurtoglu K.Y. Kantar M. Lucas S.J. Budak H. Unique and conserved microRNAs in wheat chromosome 5D revealed by next-generation sequencing. PLoS One 2013 8 7 e69801 10.1371/journal.pone.0069801 23936103
    [Google Scholar]
  27. Thiebaut F. Rojas C.A. Grativol C. Motta M.R. Vieira T. Regulski M. Martienssen R.A. Farinelli L. Hemerly A.S. Ferreira P.C.G. Genome-wide identification of microRNA and siRNA responsive to endophytic beneficial diazotrophic bacteria in maize. BMC Genomics 2014 15 1 766 10.1186/1471‑2164‑15‑766 25194793
    [Google Scholar]
  28. Creasey K.M. Zhai J. Borges F. Van Ex F. Regulski M. Meyers B.C. Martienssen R.A. miRNAs trigger widespread epigenetically activated siRNAs from transposons in Arabidopsis. Nature 2014 508 7496 411 415 10.1038/nature13069 24670663
    [Google Scholar]
  29. Zhang B. Pan X. Cannon C.H. Cobb G.P. Anderson T.A. Conservation and divergence of plant microRNA genes. Plant J. 2006 46 2 243 259 10.1111/j.1365‑313X.2006.02697.x 16623887
    [Google Scholar]
  30. Panda D. Dehury B. Sahu J. Barooah M. Sen P. Modi M.K. Computational identification and characterization of conserved miRNAs and their target genes in garlic (Allium sativum L.) expressed sequence tags. Gene 2014 537 2 333 342 10.1016/j.gene.2014.01.010 24434367
    [Google Scholar]
  31. Singh N. Srivastava S. Sharma A. Identification and analysis of miRNAs and their targets in ginger using bioinformatics approach. Gene 2016 575 2 570 576 10.1016/j.gene.2015.09.036 26392033
    [Google Scholar]
  32. Catalano D. Pignone D. Sonnante G. Finetti-Sialer M.M. In-silico and in-vivo analyses of EST databases unveil conserved miRNAs from Carthamus tinctorius and Cynara cardunculus. BMC Bioinformatics 2012 13 S4 Suppl. 4 S12 10.1186/1471‑2105‑13‑S4‑S12 22536958
    [Google Scholar]
  33. Gébelin V. Argout X. Engchuan W. Pitollat B. Duan C. Montoro P. Leclercq J. Identification of novel microRNAs in Hevea brasiliensisand computational prediction of their targets. BMC Plant Biol. 2012 12 1 18 10.1186/1471‑2229‑12‑18 22330773
    [Google Scholar]
  34. Barozai M.Y.K. Baloch I.A. Din M. Identification of MicroRNAs and their targets in Helianthus. Mol. Biol. Rep. 2012 39 3 2523 2532 10.1007/s11033‑011‑1004‑y 21670966
    [Google Scholar]
  35. Pani A. Mahapatra R.K. Behera N. Naik P.K. Computational identification of sweet wormwood (Artemisia annua) microRNA and their mRNA targets. Genomics Proteomics Bioinformatics 2011 9 6 200 210 10.1016/S1672‑0229(11)60023‑5 22289476
    [Google Scholar]
  36. Pani A. Mahapatra R.K. Computational identification of microRNAs and their targets in Catharanthus roseus expressed sequence tags. Genom. Data 2013 1 2 6 10.1016/j.gdata.2013.06.001 26484050
    [Google Scholar]
  37. Unver T. Parmaksız İ. Dündar E. Identification of conserved micro-RNAs and their target transcripts in opium poppy (Papaver somniferum L.). Plant Cell Rep. 2010 29 7 757 769 10.1007/s00299‑010‑0862‑4 20443006
    [Google Scholar]
  38. Wang M. Wang Q. Wang B. Identification and characterization of microRNAs in Asiatic cotton (Gossypium arboreum L.). PLoS One 2012 7 4 e33696 10.1371/journal.pone.0033696 22493671
    [Google Scholar]
  39. Xie F.L. Huang S.Q. Guo K. Xiang A.L. Zhu Y.Y. Nie L. Yang Z.M. Computational identification of novel microRNAs and targets in Brassica napus. FEBS Lett. 2007 581 7 1464 1474 10.1016/j.febslet.2007.02.074 17367786
    [Google Scholar]
  40. Xue C. Li F. He T. Liu G.P. Li Y. Zhang X. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 2005 6 1 310 10.1186/1471‑2105‑6‑310 16381612
    [Google Scholar]
  41. Ajila V. Colley L. Ste-Croix D.T. Nissan N. Golshani A. Cober E.R. Mimee B. Samanfar B. Green J.R. P-TarPmiR accurately predicts plant-specific miRNA targets. Sci. Rep. 2023 13 1 332 10.1038/s41598‑022‑27283‑8 36609461
    [Google Scholar]
  42. Ataei S. Ahmadi J. Marashi S.A. Abolhasani I. AmiR-P3: An AI-based microRNA prediction pipeline in plants. PLoS One 2024 19 8 e0308016 10.1371/journal.pone.0308016 39088479
    [Google Scholar]
  43. Meher P.K. Begam S. Sahu T.K. Gupta A. Kumar A. Kumar U. Rao A.R. Singh K.P. Dhankher O.P. ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features. Int. J. Mol. Sci. 2022 23 3 1612 10.3390/ijms23031612 35163534
    [Google Scholar]
  44. Guo Z. Kuang Z. Zhao Y. Deng Y. He H. Wan M. Tao Y. Wang D. Wei J. Li L. Yang X. PmiREN2.0: from data annotation to functional exploration of plant microRNAs. Nucleic Acids Res. 2022 50 D1 D1475 D1482 10.1093/nar/gkab811 34554254
    [Google Scholar]
  45. Hu L.L. Huang Y. Wang Q.C. Zou Q. Jiang Y. Benchmark comparison of ab initio microRNA identification methods and software. Genet. Mol. Res. 2012 11 4 4525 4538 10.4238/2012.October.17.4 23096922
    [Google Scholar]
  46. Srivastava P.K. Moturu T.R. Pandey P. Baldwin I.T. Pandey S.P. A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction. BMC Genomics 2014 15 1 348 10.1186/1471‑2164‑15‑348 24885295
    [Google Scholar]
  47. Lei J. Sun Y. miR-PREFeR: an accurate, fast and easy-to-use plant miRNA prediction tool using small RNA-Seq data. Bioinformatics 2014 30 19 2837 2839 10.1093/bioinformatics/btu380 24930140
    [Google Scholar]
  48. Guan D.G. Liao J.Y. Qu Z.H. Zhang Y. Qu L.H. mirExplorer: Detecting microRNAs from genome and next generation sequencing data using the AdaBoost method with transition probability matrix and combined features. RNA Biol. 2011 8 5 922 934 10.4161/rna.8.5.16026 21881406
    [Google Scholar]
  49. Wang W.C. Lin F.M. Chang W.C. Lin K.Y. Huang H.D. Lin N.S. miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinformatics 2009 10 1 328 10.1186/1471‑2105‑10‑328 19821977
    [Google Scholar]
  50. Kadri S. Hinman V. Benos P.V. HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models. BMC Bioinformatics 2009 10 S1 Suppl. 1 S35 10.1186/1471‑2105‑10‑S1‑S35 19208136
    [Google Scholar]
  51. Mathelier A. Carbone A. MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics 2010 26 18 2226 2234 10.1093/bioinformatics/btq329 20591903
    [Google Scholar]
  52. Tempel S. Tahi F. A fast ab-initio method for predicting miRNA precursors in genomes. Nucleic Acids Res. 2012 40 11 e80 10.1093/nar/gks146 22362754
    [Google Scholar]
  53. Bajczyk M. Jarmolowski A. Jozwiak M. Pacak A. Pietrykowska H. Sierocka I. Swida-Barteczka A. Szewc L. Szweykowska-Kulinska Z. Recent Insights into Plant miRNA Biogenesis: Multiple Layers of miRNA Level Regulation. Plants 2023 12 2 342 10.3390/plants12020342 36679055
    [Google Scholar]
  54. Yang J.S. Lai E.C. Alternative miRNA biogenesis pathways and the interpretation of core miRNA pathway mutants. Mol. Cell 2011 43 6 892 903 10.1016/j.molcel.2011.07.024 21925378
    [Google Scholar]
  55. Sato K. Akiyama M. Sakakibara Y. RNA secondary structure prediction using deep learning with thermodynamic integration. Nat. Commun. 2021 12 1 941 10.1038/s41467‑021‑21194‑4 33574226
    [Google Scholar]
  56. Colaiacovo M. Bernardo L. Centomani I. Crosatti C. Giusti L. Orrù L. Tacconi G. Lamontanara A. Cattivelli L. Faccioli P. A Survey of MicroRNA Length Variants Contributing to miRNome Complexity in Peach (Prunus Persica L.). Front. Plant Sci. 2012 3 165 10.3389/fpls.2012.00165 22855688
    [Google Scholar]
  57. Jeong D.H. Green P.J. Methods for validation of miRNA sequence variants and the cleavage of their targets. Methods 2012 58 2 135 143 10.1016/j.ymeth.2012.08.005 22922269
    [Google Scholar]
  58. Schmartz G.P. Kern F. Fehlmann T. Wagner V. Fromm B. Keller A. Encyclopedia of tools for the analysis of miRNA isoforms. Brief. Bioinform. 2021 22 4 bbaa346 10.1093/bib/bbaa346 33313643
    [Google Scholar]
  59. Yang K. Wen X. Mudunuri S. Varma G.P.S. Sablok G. Diff isomiRs: Large-scale detection of differential isomiRs for understanding non-coding regulated stress omics in plants. Sci. Rep. 2019 9 1 1406 10.1038/s41598‑019‑38932‑w 30723229
    [Google Scholar]
  60. Yang K. Wen X. Mudunuri S.B. Sablok G. Plant IsomiR Atlas: large scale detection, profiling, and target repertoire of IsomiRs in plants. Front. Plant Sci. 2019 9 1881 10.3389/fpls.2018.01881 30723486
    [Google Scholar]
  61. Yang K. Sablok G. Qiao G. Nie Q. Wen X. isomiR2Function: an integrated workflow for identifying microRNA variants in plants. Front. Plant Sci. 2017 8 322 10.3389/fpls.2017.00322 28377776
    [Google Scholar]
  62. Zhang Y. Zang Q. Xu B. Zheng W. Ban R. Zhang H. Yang Y. Hao Q. Iqbal F. Li A. Shi Q. IsomiR Bank: a research resource for tracking IsomiRs. Bioinformatics 2016 32 13 2069 2071 10.1093/bioinformatics/btw070 27153728
    [Google Scholar]
  63. Devers E.A. Branscheid A. May P. Krajinski F. Stars and symbiosis: microRNA- and microRNA*-mediated transcript cleavage involved in arbuscular mycorrhizal symbiosis. Plant Physiol. 2011 156 4 1990 2010 10.1104/pp.111.172627 21571671
    [Google Scholar]
  64. Zhang X. Zhao H. Gao S. Wang W.C. Katiyar-Agarwal S. Huang H.D. Raikhel N. Jin H. Arabidopsis Argonaute 2 regulates innate immunity via miRNA393(∗)-mediated silencing of a Golgi-localized SNARE gene, MEMB12. Mol. Cell 2011 42 3 356 366 10.1016/j.molcel.2011.04.010 21549312
    [Google Scholar]
  65. Meng Y. Gou L. Chen D. Mao C. Jin Y. Wu P. Chen M. PmiRKB: a plant microRNA knowledge base. Nucleic Acids Res. 2011 39 Database D181 D187 10.1093/nar/gkq721 20719744
    [Google Scholar]
  66. Platt R.N. II Vandewege M.W. Kern C. Schmidt C.J. Hoffmann F.G. Ray D.A. Large numbers of novel miRNAs originate from DNA transposons and are coincident with a large species radiation in bats. Mol. Biol. Evol. 2014 31 6 1536 1545 10.1093/molbev/msu112 24692655
    [Google Scholar]
  67. Franco-Zorrilla J.M. Valli A. Todesco M. Mateos I. Puga M.I. Rubio-Somoza I. Leyva A. Weigel D. García J.A. Paz-Ares J. Target mimicry provides a new mechanism for regulation of microRNA activity. Nat. Genet. 2007 39 8 1033 1037 10.1038/ng2079 17643101
    [Google Scholar]
  68. Wu H.J. Wang Z.M. Wang M. Wang X.J. Widespread long noncoding RNAs as endogenous target mimics for microRNAs in plants. Plant Physiol. 2013 161 4 1875 1884 10.1104/pp.113.215962 23429259
    [Google Scholar]
  69. Wu H.J. Ma Y.K. Chen T. Wang M. Wang X.J. PsRobot: a web-based plant small RNA meta-analysis toolbox. Nucleic Acids Res. 2012 40 W1 W22 W28 10.1093/nar/gks554 22693224
    [Google Scholar]
  70. Numnark S. Mhuantong W. Ingsriswang S. Wichadakul D. C-mii: a tool for plant miRNA and target identification. BMC Genomics 2012 13 S7 Suppl. 7 S16 10.1186/1471‑2164‑13‑S7‑S16 23281648
    [Google Scholar]
  71. Singh N. Sharma A. In-silico identification of miRNAs and their regulating target functions in Ocimum basilicum. Gene 2014 552 2 277 282 10.1016/j.gene.2014.09.040 25256277
    [Google Scholar]
  72. Xie F. Xiao P. Chen D. Xu L. Zhang B. miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs. Plant Mol. Biol. 2012 80 1 75 84 Advance online publication 10.1007/s11103‑012‑9885‑2 22290409
    [Google Scholar]
  73. Ma X. Liu C. Gu L. Mo B. Cao X. Chen X. TarHunter, a tool for predicting conserved microRNA targets and target mimics in plants. Bioinformatics 2018 34 9 1574 1576 10.1093/bioinformatics/btx797 29236948
    [Google Scholar]
  74. Guigon I. Legrand S. Berthelot J.F. Bini S. Lanselle D. Benmounah M. Touzet H. miRkwood: a tool for the reliable identification of microRNAs in plant genomes. BMC Genomics 2019 20 1 532 10.1186/s12864‑019‑5913‑9 31253093
    [Google Scholar]
  75. Gao T. Meng X. Zhang W. Jin W. miR-Island: an ultrafast and memory-efficient tool for plant miRNA annotation and expression analysishttps Research Square 2019 10.21203/rs.2.19370/v1
    [Google Scholar]
  76. Hammond R.K. Gupta P. Patel P. Meyers B.C. miRador: a fast and precise tool for the prediction of plant miRNAs. Plant Physiol. 2023 191 2 894 903 10.1093/plphys/kiac538 36437740
    [Google Scholar]
  77. Deng Y. Qin Y. Yang P. Du J. Kuang Z. Zhao Y. Wang Y. Li D. Wei J. Guo X. Li L. Yang X. Comprehensive annotation and functional exploration of microRNAs in lettuce. Front. Plant Sci. 2021 12 781836 10.3389/fpls.2021.781836 35003165
    [Google Scholar]
  78. Mitchell D.A. MicroRNAs provide a novel pathway toward combinatorial immune checkpoint blockade. Neuro-oncol. 2016 18 5 601 602 10.1093/neuonc/now003 26980424
    [Google Scholar]
  79. Somarelli J.A. Shetler S. Jolly M.K. Wang X. Bartholf Dewitt S. Hish A.J. Gilja S. Eward W.C. Ware K.E. Levine H. Armstrong A.J. Garcia-Blanco M.A. Mesenchymal-Epithelial Transition in Sarcomas Is Controlled by the Combinatorial Expression of MicroRNA 200s and GRHL2. Mol. Cell. Biol. 2016 36 19 2503 2513 10.1128/MCB.00373‑16 27402864
    [Google Scholar]
  80. Wu W.K.K. Yu J. Chan M.T.V. To K.F. Cheng A.S.L. Combinatorial epigenetic deregulation by Helicobacter pylori and Epstein-Barr virus infections in gastric tumourigenesis. J. Pathol. 2016 239 3 245 249 10.1002/path.4731 27102722
    [Google Scholar]
  81. Coronnello C. Benos P.V. ComiR: Combinatorial microRNA target prediction tool. Nucleic Acids Res. 2013 41 Web Server issue W159-64 23703208
    [Google Scholar]
  82. Vlachos I.S. Kostoulas N. Vergoulis T. Georgakilas G. Reczko M. Maragkakis M. Paraskevopoulou M.D. Prionidis K. Dalamagas T. Hatzigeorgiou A.G. DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res. 2012 40 W1 W498 W504 10.1093/nar/gks494 22649059
    [Google Scholar]
  83. Friedman Y. Naamati G. Linial M. MiRror: a combinatorial analysis web tool for ensembles of microRNAs and their targets. Bioinformatics 2010 26 15 1920 1921 10.1093/bioinformatics/btq298 20529892
    [Google Scholar]
  84. Aftabuddin M. Mal C. Deb A. Kundu S. C2Analyzer: Co-Target–Co-Function Analyzer. Genomics Proteomics Bioinformatics 2014 12 3 133 136 10.1016/j.gpb.2014.03.003 24862384
    [Google Scholar]
  85. Singh N. Srivastava S. Shasany A.K. Sharma A. Identification of miRNAs and their targets involved in the secondary metabolic pathways of Mentha spp. Comput. Biol. Chem. 2016 64 154 162 10.1016/j.compbiolchem.2016.06.004 27376499
    [Google Scholar]
  86. Lopez-Gomollon S. Mohorianu I. Szittya G. Moulton V. Dalmay T. Diverse correlation patterns between microRNAs and their targets during tomato fruit development indicates different modes of microRNA actions. Planta 2012 236 6 1875 1887 10.1007/s00425‑012‑1734‑7 22922939
    [Google Scholar]
  87. Pérez-Quintero Á.L. Sablok G. Tatarinova T.V. Conesa A. Kuo J. López C. Mining of miRNAs and potential targets from gene oriented clusters of transcripts sequences of the anti-malarial plant, Artemisia annua. Biotechnol. Lett. 2012 34 4 737 745 10.1007/s10529‑011‑0808‑0 22160362
    [Google Scholar]
  88. Thirugnanasambantham K. Saravanan S. Karikalan K. Bharanidharan R. Lalitha P. Ilango S. HairulIslam V.I. Identification of evolutionarily conserved Momordica charantia microRNAs using computational approach and its utility in phylogeny analysis. Comput. Biol. Chem. 2015 58 25 39 10.1016/j.compbiolchem.2015.04.011 25988220
    [Google Scholar]
  89. Tarver J.E. Cormier A. Pinzón N. Taylor R.S. Carré W. Strittmatter M. Seitz H. Coelho S.M. Cock J.M. microRNAs and the evolution of complex multicellularity: identification of a large, diverse complement of microRNAs in the brown alga Ectocarpus. Nucleic Acids Res. 2015 43 13 6384 6398 10.1093/nar/gkv578 26101255
    [Google Scholar]
  90. Tamura K. Stecher G. Peterson D. Filipski A. Kumar S. MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0. Mol. Biol. Evol. 2013 30 12 2725 2729 10.1093/molbev/mst197 24132122
    [Google Scholar]
  91. Gustafson A.M. Allen E. Givan S. Smith D. Carrington J.C. Kasschau K.D. ASRP: the Arabidopsis Small RNA Project Database. Nucleic Acids Res. 2004 33 Database issue D637 D640 10.1093/nar/gki127 15608278
    [Google Scholar]
  92. Johnson C. Bowman L. Adai A.T. Vance V. Sundaresan V. CSRDB: a small RNA integrated database and browser resource for cereals. Nucleic Acids Res. 2007 35 Database D829 D833 10.1093/nar/gkl991 17169981
    [Google Scholar]
  93. Griffiths-Jones S. Bateman A. Marshall M. Khanna A. Eddy S.R. Rfam: an RNA family database. Nucleic Acids Res. 2003 31 1 439 441 10.1093/nar/gkg006 12520045
    [Google Scholar]
  94. Lazzari B. Caprera A. Cestaro A. Merelli I. Del Corvo M. Fontana P. Milanesi L. Velasco R. Stella A. Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs. BMC Plant Biol. 2009 9 1 82 10.1186/1471‑2229‑9‑82 19563653
    [Google Scholar]
  95. Zhang S. Yue Y. Sheng L. Wu Y. Fan G. Li A. Hu X. ShangGuan M. Wei C. PASmiR: a literature-curated database for miRNA molecular regulation in plant response to abiotic stress. BMC Plant Biol. 2013 13 1 33 10.1186/1471‑2229‑13‑33 23448274
    [Google Scholar]
  96. Chiang K. Shu J. Zempleni J. Cui J. Dietary MicroRNA Database (DMD): An Archive Database and Analytic Tool for Food-Borne microRNAs. PLoS One 2015 10 6 e0128089 10.1371/journal.pone.0128089 26030752
    [Google Scholar]
  97. Yi X. Zhang Z. Ling Y. Xu W. Su Z. PNRD: a plant non-coding RNA database. Nucleic Acids Res. 2015 43 D1 D982 D989 10.1093/nar/gku1162 25398903
    [Google Scholar]
  98. Hsu S.D. Lin F.M. Wu W.Y. Liang C. Huang W.C. Chan W.L. Tsai W.T. Chen G.Z. Lee C.J. Chiu C.M. Chien C.H. Wu M.C. Huang C.Y. Tsou A.P. Huang H.D. miRTarBase: a database curates experimentally validated microRNA–target interactions. Nucleic Acids Res. 2011 39 Database issue Suppl. 1 D163 D169 10.1093/nar/gkq1107 21071411
    [Google Scholar]
  99. Yang J.H. Li J.H. Shao P. Zhou H. Chen Y.Q. Qu L.H. starBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res. 2011 39 Database issue Suppl. 1 D202 D209 10.1093/nar/gkq1056 21037263
    [Google Scholar]
  100. Sethupathy P. Corda B. Hatzigeorgiou A.G. TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA 2006 12 2 192 197 10.1261/rna.2239606 16373484
    [Google Scholar]
  101. Yuan C. Meng X. Li X. Illing N. Ingle R.A. Wang J. Chen M. PceRBase: a database of plant competing endogenous RNA. Nucleic Acids Res. 2017 45 D1 D1009 D1014 10.1093/nar/gkw916 28053167
    [Google Scholar]
  102. Zhang Z. Yu J. Li D. Zhang Z. Liu F. Zhou X. Wang T. Ling Y. Su Z. PMRD: plant microRNA database. Nucleic Acids Res. 2010 38 Database issue Suppl. 1 D806 D813 10.1093/nar/gkp818 19808935
    [Google Scholar]
  103. Lai L. Liberzon A. Hennessey J. Jiang G. Qi J. Mesirov J.P. Ge S.X. AraPath: a knowledgebase for pathway analysis in Arabidopsis. Bioinformatics 2012 28 17 2291 2292 10.1093/bioinformatics/bts421 22760305
    [Google Scholar]
  104. Jiang Z. Liu X. Peng Z. Wan Y. Ji Y. He W. Wan W. Luo J. Guo H. AHD2.0: an update version of Arabidopsis Hormone Database for plant systematic studies. Nucleic Acids Res. 2011 39 Database D1123 D1129 10.1093/nar/gkq1066 21045062
    [Google Scholar]
  105. Bazzini A.A. Asís R. González V. Bassi S. Conte M. Soria M. Fernie A.R. Asurmendi S. Carrari F. miSolRNA: A tomato micro RNA relational database. BMC Plant Biol. 2010 10 1 240 10.1186/1471‑2229‑10‑240 21059227
    [Google Scholar]
  106. Zielezinski A. Dolata J. Alaba S. Kruszka K. Pacak A. Swida-Barteczka A. Knop K. Stepien A. Bielewicz D. Pietrykowska H. Sierocka I. Sobkowiak L. Lakomiak A. Jarmolowski A. Szweykowska-Kulinska Z. Karlowski W.M. mirEX 2.0 - an integrated environment for expression profiling of plant microRNAs. BMC Plant Biol. 2015 15 1 144 10.1186/s12870‑015‑0533‑2 26141515
    [Google Scholar]
  107. Joshi T. Fitzpatrick M.R. Chen S. Liu Y. Zhang H. Endacott R.Z. Gaudiello E.C. Stacey G. Nguyen H.T. Xu D. Soybean knowledge base (SoyKB): a web resource for integration of soybean translational genomics and molecular breeding. Nucleic Acids Res. 2014 42 D1 D1245 D1252 10.1093/nar/gkt905 24136998
    [Google Scholar]
  108. Deng J. Li Q. Huang L. Tang W. Zheng K. Yan J. Wu W. PendoTMBase: A Database for Plant Endogenous Target Mimics. Interdiscip. Sci. 2020 12 4 526 529 10.1007/s12539‑020‑00396‑2 32997234
    [Google Scholar]
  109. Naithani S. Gupta P. Preece J. D’Eustachio P. Elser J.L. Garg P. Dikeman D.A. Kiff J. Cook J. Olson A. Wei S. Tello-Ruiz M.K. Mundo A.F. Munoz-Pomer A. Mohammed S. Cheng T. Bolton E. Papatheodorou I. Stein L. Ware D. Jaiswal P. Plant Reactome: a knowledgebase and resource for comparative pathway analysis. Nucleic Acids Res. 2019 48 D1 gkz996 10.1093/nar/gkz996 31680153
    [Google Scholar]
  110. Yang J.H. Shao P. Zhou H. Chen Y.Q. Qu L.H. deepBase: a database for deeply annotating and mining deep sequencing data. Nucleic Acids Res. 2010 38 Database issue Suppl. 1 D123 D130 10.1093/nar/gkp943 19966272
    [Google Scholar]
  111. Nascimento L.C. Costa G.G.L. Binneck E. Pereira G.A.G. Carazzolle M.F. A web-based bioinformatics interface applied to the GENOSOJA project: databases and pipelines. Genet. Mol. Biol. 2012 35 1 suppl 1 Suppl. 203 211 10.1590/S1415‑47572012000200002 22802706
    [Google Scholar]
  112. Nakano M. Nobuta K. Vemaraju K. Tej S.S. Skogen J.W. Meyers B.C. Plant MPSS databases: signature-based transcriptional resources for analyses of mRNA and small RNA. Nucleic Acids Res. 2006 34 90001 D731 D735 10.1093/nar/gkj077 16381968
    [Google Scholar]
  113. Yu J. Zhang Z. Wei J. Ling Y. Xu W. Su Z. SFGD: a comprehensive platform for mining functional information from soybean transcriptome data and its use in identifying acyl-lipid metabolism pathways. BMC Genomics 2014 15 1 271 10.1186/1471‑2164‑15‑271 24712981
    [Google Scholar]
  114. Gurjar A. K. Panwar A. S. Gupta R. Mantri S. S. PmiRExAt: plant miRNA expression atlas database and web applications. J. Biolog. Databases. Curat. 2016 2016 baw060 10.1093/database/baw060
    [Google Scholar]
  115. Yu D. Lu J. Shao W. Ma X. Xie T. Ito H. Wang T. Xu M. Wang H. Meng Y. MepmiRDB: a medicinal plant microRNA database. J. Biolog. Databases. Curat. 2019 2019 baz070 10.1093/database/baz070
    [Google Scholar]
  116. Ouyang S. Zhu W. Hamilton J. Lin H. Campbell M. Childs K. Thibaud-Nissen F. Malek R.L. Lee Y. Zheng L. Orvis J. Haas B. Wortman J. Buell C.R. The TIGR Rice Genome Annotation Resource: improvements and new features. Nucleic Acids Res. 2007 35 Database D883 D887 10.1093/nar/gkl976 17145706
    [Google Scholar]
  117. Lamesch P. Berardini T.Z. Li D. Swarbreck D. Wilks C. Sasidharan R. Muller R. Dreher K. Alexander D.L. Garcia-Hernandez M. Karthikeyan A.S. Lee C.H. Nelson W.D. Ploetz L. Singh S. Wensel A. Huala E. The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 2012 40 D1 D1202 D1210 10.1093/nar/gkr1090 22140109
    [Google Scholar]
  118. Li Z. Zhao Y. Liu X. Peng J. Guo H. Luo J. LSD 2.0: an update of the leaf senescence database. Nucleic Acids Res. 2014 42 D1 D1200 D1205 10.1093/nar/gkt1061 24185698
    [Google Scholar]
  119. Szcześniak M.W. Makałowska I. miRNEST 2.0: a database of plant and animal microRNAs. Nucleic Acids Res. 2014 42 D1 D74 D77 10.1093/nar/gkt1156 24243848
    [Google Scholar]
  120. Bülow L. Steffens N.O. Galuschka C. Schindler M. Hehl R. AthaMap: from in silico data to real transcription factor binding sites. In Silico Biol. 2006 6 3 243 252 16922688
    [Google Scholar]
  121. Xu Y. Guo M. Liu X. Wang C. Liu Y. SoyFN: a knowledge database of soybean functional networks. J. Biolog. Databases Curat. 2014 2014 bau019 10.1093/database/bau019
    [Google Scholar]
  122. Zhang T. Zhai J. Zhang X. Ling L. Li M. Xie S. Song M. Ma C. Interactive Web-Based Annotation of Plant MicroRNAs with iwa-miRNA. Genomics Proteomics Bioinformatics 2022 20 3 557 567 10.1016/j.gpb.2021.02.010 34332120
    [Google Scholar]
  123. Liu H. Jin T. Liao R. Wan L. Xu B. Zhou S. Guan J. miRFANs: an integrated database for Arabidopsis thalianamicroRNA function annotations. BMC Plant Biol. 2012 12 1 68 10.1186/1471‑2229‑12‑68 22583976
    [Google Scholar]
  124. Chen D. Yuan C. Zhang J. Zhang Z. Bai L. Meng Y. Chen L.L. Chen M. PlantNATsDB: a comprehensive database of plant natural antisense transcripts. Nucleic Acids Res. 2012 40 D1 D1187 D1193 10.1093/nar/gkr823 22058132
    [Google Scholar]
  125. Mhuantong W. Wichadakul D. MicroPC (μPC): A comprehensive resource for predicting and comparing plant microRNAs. BMC Genomics 2009 10 1 366 10.1186/1471‑2164‑10‑366 19660144
    [Google Scholar]
  126. Fei Z. Joung J.G. Tang X. Zheng Y. Huang M. Lee J.M. McQuinn R. Tieman D.M. Alba R. Klee H.J. Giovannoni J.J. Tomato Functional Genomics Database: a comprehensive resource and analysis package for tomato functional genomics. Nucleic Acids Res. 2011 39 Database D1156 D1163 10.1093/nar/gkq991 20965973
    [Google Scholar]
  127. Qiu L. Luo H. Zhou H. Yan H. Fan Y. Zhou Z. Chen R. Liu J. Luo T. Deng Y. Xiong F. Wu J. MicroSugar: A database of comprehensive miRNA target prediction framework for sugarcane (Saccharum officinarum L.). Genomics 2022 114 4 110420 10.1016/j.ygeno.2022.110420 35760231
    [Google Scholar]
  128. Dai E. Yu X. Zhang Y. Meng F. Wang S. Liu X. Liu D. Wang J. Li X. Jiang W. EpimiR: a database of curated mutual regulation between miRNAs and epigenetic modifications. J. Biolog. Databases Curat. 2014 2014 bau023 10.1093/database/bau023
    [Google Scholar]
  129. Szcześniak M.W. Kabza M. Pokrzywa R. Gudyś A. Makałowska I. ERISdb: a database of plant splice sites and splicing signals. Plant Cell Physiol. 2013 54 2 e10 10.1093/pcp/pct001 23299413
    [Google Scholar]
  130. Kurubanjerdjit N. Huang C.H. Lee Y. Tsai J.J.P. Ng K.L. Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms. Comput. Biol. Med. 2013 43 11 1645 1652 10.1016/j.compbiomed.2013.08.010 24209909
    [Google Scholar]
  131. Zhang P. Meng X. Chen H. Liu Y. Xue J. Zhou Y. Chen M. PlantCircNet: a database for plant circRNA-miRNA-mRNA regulatory networks. J. Biolog. Databases Curat. 2017 2017 bax089 10.1093/database/bax089
    [Google Scholar]
  132. Fei Y. Wang R. Li H. Liu S. Zhang H. Huang J. DPMIND: degradome-based plant miRNA–target interaction and network database. Bioinformatics 2018 34 9 1618 1620 10.1093/bioinformatics/btx824 29280990
    [Google Scholar]
  133. Rashid T. Abdulkadir A. Nasrallah I.M. Ware J.B. Liu H. Spincemaille P. Romero J.R. Bryan R.N. Heckbert S.R. Habes M. DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI. Sci. Rep. 2021 11 1 14124 10.1038/s41598‑021‑93427‑x 34238951
    [Google Scholar]
  134. Zhang J. Hao Z. Yin S. Li G. GreenCircRNA: a database for plant circRNAs that act as miRNA decoys. J. Biolog. Databases Curat. 2020 2020 baaa039 10.1093/database/baaa039
    [Google Scholar]
  135. Jin J. Lu P. Xu Y. Li Z. Yu S. Liu J. Wang H. Chua N.H. Cao P. PLncDB V2.0: a comprehensive encyclopedia of plant long noncoding RNAs. Nucleic Acids Res. 2021 49 D1 D1489 D1495 10.1093/nar/gkaa910 33079992
    [Google Scholar]
  136. Zahra S. Bhardwaj R. Sharma S. Singh A. Kumar S. PtncRNAdb: plant transfer RNA-derived non-coding RNAs (tncRNAs) database. 3 Biotech 2022 12 5 105 10.1007/s13205‑022‑03174‑7 35462956
    [Google Scholar]
  137. Liu B. Fang L. Chen J. Liu F. Wang X. miRNA-dis: microRNA precursor identification based on distance structure status pairs. Mol. Biosyst. 2015 11 4 1194 1204 10.1039/C5MB00050E 25715848
    [Google Scholar]
  138. Karathanasis N. Tsamardinos I. Poirazi P. MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology. PLoS One 2015 10 5 e0126151 10.1371/journal.pone.0126151 25961860
    [Google Scholar]
  139. An J. Lai J. Sajjanhar A. Lehman M.L. Nelson C.C. miRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data. BMC Bioinformatics 2014 15 1 275 10.1186/1471‑2105‑15‑275 25117656
    [Google Scholar]
  140. Wu Y. Wei B. Liu H. Li T. Rayner S. MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinformatics 2011 12 1 107 10.1186/1471‑2105‑12‑107 21504621
    [Google Scholar]
  141. Teune J.H. Steger G. novoMIR : De Novo Prediction of MicroRNA‐Coding Regions in a Single Plant‐Genome. J. Nucleic Acids 2010 2010 1 495904 10.4061/2010/495904 20871826
    [Google Scholar]
  142. Gudyś A. Szcześniak M.W. Sikora M. Makałowska I. HuntMi: an efficient and taxon-specific approach in pre-miRNA identification. BMC Bioinformatics 2013 14 1 83 10.1186/1471‑2105‑14‑83 23497112
    [Google Scholar]
  143. Stocks M.B. Moxon S. Mapleson D. Woolfenden H.C. Mohorianu I. Folkes L. Schwach F. Dalmay T. Moulton V. The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets. Bioinformatics 2012 28 15 2059 2061 10.1093/bioinformatics/bts311 22628521
    [Google Scholar]
  144. Evers M. Huttner M. Dueck A. Meister G. Engelmann J.C. miRA: adaptable novel miRNA identification in plants using small RNA sequencing data. BMC Bioinformatics 2015 16 1 370 10.1186/s12859‑015‑0798‑3 26542525
    [Google Scholar]
  145. Fan D. Yao Y. Yi M. PlantMirP2: An Accurate, Fast and Easy-To-Use Program for Plant Pre-miRNA and miRNA Prediction. Genes (Basel) 2021 12 8 1280 10.3390/genes12081280 34440454
    [Google Scholar]
  146. Tang X. Sun Y. Fast and accurate microRNA search using CNN. BMC Bioinformatics 2019 20 S23 Suppl. 23 646 10.1186/s12859‑019‑3279‑2 31881831
    [Google Scholar]
  147. Vasconcelos A.M. Carmo M.B. Ferreira B. Viegas I. Gama-Carvalho M. Ferreira A. Amaral A.J. IsomiR_Window: a system for analyzing small-RNA-seq data in an integrative and user-friendly manner. BMC Bioinformatics 2021 22 1 37 10.1186/s12859‑021‑03955‑6 33522913
    [Google Scholar]
  148. Zhang Y. miRU: an automated plant miRNA target prediction server. Nucleic Acids Res. 2005 33 Web Server W701 W704 10.1093/nar/gki383 15980567
    [Google Scholar]
  149. Xie F. Zhang B. Target-align: a tool for plant microRNA target identification. Bioinformatics 2010 26 23 3002 3003 10.1093/bioinformatics/btq568 20934992
    [Google Scholar]
  150. Zhang Z. Jiang L. Wang J. Gu P. Chen M. MTide: an integrated tool for the identification of miRNA–target interaction in plants. Bioinformatics 2015 31 2 290 291 10.1093/bioinformatics/btu633 25256573
    [Google Scholar]
  151. Jha A. Shankar R. Employing machine learning for reliable miRNA target identification in plants. BMC Genomics 2011 12 1 636 10.1186/1471‑2164‑12‑636 22206472
    [Google Scholar]
  152. Fahlgren N. Howell M.D. Kasschau K.D. Chapman E.J. Sullivan C.M. Cumbie J.S. Givan S.A. Law T.F. Grant S.R. Dangl J.L. Carrington J.C. High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One 2007 2 2 e219 10.1371/journal.pone.0000219 17299599
    [Google Scholar]
  153. Chorostecki U. Palatnik J.F. comTAR: a web tool for the prediction and characterization of conserved microRNA targets in plants. Bioinformatics 2014 30 14 2066 2067 10.1093/bioinformatics/btu147 24632500
    [Google Scholar]
  154. Tseng K.C. Chiang-Hsieh Y.F. Pai H. Chow C.N. Lee S.C. Zheng H.Q. Kuo P.L. Li G.Z. Hung Y.C. Lin N.S. Chang W.C. microRPM: a microRNA prediction model based only on plant small RNA sequencing data. Bioinformatics 2018 34 7 1108 1115 10.1093/bioinformatics/btx725 29136092
    [Google Scholar]
  155. Dai X. Zhao P.X. pssRNAMiner: a plant short small RNA regulatory cascade analysis server. Nucleic Acids Res. 2008 36 Web Server issue Suppl. 2 W114 W118 10.1093/nar/gkn297 18474525
    [Google Scholar]
  156. Li F. Orban R. Baker B. SoMART: a web server for plant miRNA, tasiRNA and target gene analysis. Plant J. 2012 70 5 891 901 10.1111/j.1365‑313X.2012.04922.x 22268718
    [Google Scholar]
  157. Cai B. Yang X. Tuskan G.A. Cheng Z.M. MicroSyn: A user friendly tool for detection of microsynteny in a gene family. BMC Bioinformatics 2011 12 1 79 10.1186/1471‑2105‑12‑79 21418570
    [Google Scholar]
  158. Wen M. Shen Y. Shi S. Tang T. miREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments. BMC Bioinformatics 2012 13 1 140 10.1186/1471‑2105‑13‑140 22720726
    [Google Scholar]
  159. Thieme C.J. Gramzow L. Lobbes D. Theißen G. SplamiR—prediction of spliced miRNAs in plants. Bioinformatics 2011 27 9 1215 1223 10.1093/bioinformatics/btr132 21421552
    [Google Scholar]
  160. Dezulian T. Remmert M. Palatnik J.F. Weigel D. Huson D.H. Identification of plant microRNA homologs. Bioinformatics 2006 22 3 359 360 10.1093/bioinformatics/bti802 16317073
    [Google Scholar]
  161. Ding J. Yu S. Ohler U. Guan J. Zhou S. imiRTP: An Integrated Method to Identifying miRNA-target Interactions in Arabidopsis thaliana. IEEE International Conference on Bioinformatics and Biomedicine 12-15 Nov, 2011, Atlanta, GA, USA, 2011, pp. 100-104. 10.1109/BIBM.2011.13
    [Google Scholar]
  162. Rhee S. Chae H. Kim S. PlantMirnaT: miRNA and mRNA integrated analysis fully utilizing characteristics of plant sequencing data. Methods 2015 83 80 87 10.1016/j.ymeth.2015.04.003 25863133
    [Google Scholar]
  163. Fei Y. Mao Y. Shen C. Wang R. Zhang H. Huang J. WPMIAS: Whole-degradome-based Plant MicroRNA-Target Interaction Analysis Server. Bioinformatics 2019 2019 btz820 10.1093/bioinformatics/btz820 31693067
    [Google Scholar]
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  • Article Type:
    Review Article
Keywords: computational methods ; software ; databases ; microRNA ; plant ; ncRNA ; algorithms
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