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Genomics2009; 94(2); 125-131; doi: 10.1016/j.ygeno.2009.04.006

In silico detection and characteristics of novel microRNA genes in the Equus caballus genome using an integrated ab initio and comparative genomic approach.

Abstract: The importance of microRNAs at the post-transcriptional regulation level has recently been recognized in both animals and plants. We used the simple but effective sequential method of first Blasting known animal miRNAs against the horse genome and then using the located candidates to search for novel miRNAs by RNA folding method in the vicinity (+ -500 bp) of the candidates. Here, a total of 407 novel horse miRNA genes including 354 mature miRNAs were identified, of these, 75 miRNAs were grouped into 32 families based on seed sequence identity. MiRNA genes tend to be present as clusters in some chromosomes, and 146 miRNA genes accounted for 36% of the total were observed as part of polycistronic transcripts. Detailed analysis of sequence characteristics in novel horse and all previous known animal miRNAs were carried out. Our study will provide a reference point for further study on miRNAs identification in animals and improve the understanding of genome in horse.
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Publication Date: 2009-05-03 PubMed ID: 19406225DOI: 10.1016/j.ygeno.2009.04.006Google Scholar: Lookup
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  • Comparative Study
  • Journal Article
  • Research Support
  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research abstract discusses the use of an integrated computational method to detect and identify characteristics of 407 new microRNA (miRNA) genes in the genome of Equus caballus, or the horse. The study also provides information about the distribution, families, and sequence characteristics of these miRNAs, which contribute to the regulation of genes in the horse’s genome.

Research Methodology

  • The researchers utilized a sequential computational method that begins with blasting known animal miRNAs against the horse genome. ‘Blasting’ here involves comparing the genetic sequences of known miRNAs from a host of different animals to the complete set of genes in the horse genome. This comparison helped researchers to locate potential matches or candidates for miRNAs in the horse genome.
  • The located candidates were then used as a reference point to further search for new miRNAs. The study specifically focused on areas near these candidate sites (within + or – 500 base pairs).
  • The final step in the process was conducting RNA folding method for prediction of secondary structures of the potential miRNA. This further aids in their identification. The folding and structural alignment occured in RNA programs where the on-screen results were manually inspected.

Key Findings

  • A total of 407 new horse miRNA genes were identified in this study.
  • Out of these, 354 are mature miRNAs and 75 miRNAs are grouped into 32 families based on seed sequence identity. Seed sequences are portions of RNA that interact directly with the target gene and are fundamental in aligning the miRNA to its target sites.
  • The researchers found that miRNA genes tend to be present as clusters in some chromosomes.
  • 146 miRNA genes, accounting for 36% of the total, were observed as part of polycistronic transcripts. Polycistronic transcription involves a single genetic event that leads to the creation of multiple separate proteins.
  • A detailed analysis of sequence characteristics in these newly discovered horse miRNAs and all previously known animal miRNAs was carried out.

Significance of the Study

  • This study contributes to the wider understanding of miRNAs, critical regulators of gene expression after transcription.
  • The sheer number of novel miRNA genes identified in this study demonstrates the vast potential for miRNA discovery yet to be unlocked.
  • The research provides a richer understanding of the horse genome and provides reference material for further study of miRNAs in animals.

Cite This Article

APA
Zhou M, Wang Q, Sun J, Li X, Xu L, Yang H, Shi H, Ning S, Chen L, Li Y, He T, Zheng Y. (2009). In silico detection and characteristics of novel microRNA genes in the Equus caballus genome using an integrated ab initio and comparative genomic approach. Genomics, 94(2), 125-131. https://doi.org/10.1016/j.ygeno.2009.04.006

Publication

Genomics
ISSN: 1089-8646
NlmUniqueID: 8800135
Country: United States
Language: English
Volume: 94
Issue: 2
Pages: 125-131

Researcher Affiliations

Zhou, Meng
  • College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Wang, Qianghu
    Sun, Jie
      Li, Xia
        Xu, Liangde
          Yang, Haixiu
            Shi, Hongbo
              Ning, Shangwei
                Chen, Li
                  Li, Yan
                    He, Taotao
                      Zheng, Yan

                        MeSH Terms

                        • Animals
                        • Chromosomes, Mammalian
                        • Computers
                        • Genome
                        • Horses / genetics
                        • MicroRNAs / genetics
                        • Transcription, Genetic

                        Citations

                        This article has been cited 24 times.
                        1. Carver C, Bruemmer J, Coleman S, Landolt G, Hess T. Effects of corn supplementation on serum and muscle microRNA profiles in horses. Food Sci Nutr 2023 Jun;11(6):2811-2822.
                          doi: 10.1002/fsn3.3259pubmed: 37324886google scholar: lookup
                        2. Szmatoła T, Gurgul A, Jasielczuk I, Oclon E, Ropka-Molik K, Stefaniuk-Szmukier M, Polak G, Tomczyk-Wrona I, Bugno-Poniewierska M. Assessment and Distribution of Runs of Homozygosity in Horse Breeds Representing Different Utility Types. Animals (Basel) 2022 Nov 25;12(23).
                          doi: 10.3390/ani12233293pubmed: 36496815google scholar: lookup
                        3. Lee W, Mun S, Choi SY, Oh DY, Park YS, Han K. Comparative Analysis for Genetic Characterization in Korean Native Jeju Horse. Animals (Basel) 2021 Jun 28;11(7).
                          doi: 10.3390/ani11071924pubmed: 34203473google scholar: lookup
                        4. Chu Y, Liu Z, Liu J, Yu L, Zhang D, Pei F. Characterization of lncRNA-Perturbed TLR-Signaling Network Identifies Novel lncRNA Prognostic Biomarkers in Colorectal Cancer. Front Cell Dev Biol 2020;8:503.
                          doi: 10.3389/fcell.2020.00503pubmed: 32626715google scholar: lookup
                        5. Yang H, Li Y, Peng Z, Wang Y. Overexpression of miR-20a promotes the progression of osteosarcoma by directly targeting QKI2. Oncol Lett 2019 Jul;18(1):87-94.
                          doi: 10.3892/ol.2019.10313pubmed: 31289476google scholar: lookup
                        6. Khade KA, Panigrahi M, Ahmad SF, Chauhan A, Kumar P, Bhushan B. Cloning and characterization of Bubaline mammary miRNAs: An in silico approach. Mol Biol Rep 2019 Feb;46(1):1257-1262.
                          doi: 10.1007/s11033-019-04594-0pubmed: 30788763google scholar: lookup
                        7. Soula A, Valere M, López-González MJ, Ury-Thiery V, Groppi A, Landry M, Nikolski M, Favereaux A. Small RNA-Seq reveals novel miRNAs shaping the transcriptomic identity of rat brain structures. Life Sci Alliance 2018 Oct;1(5):e201800018.
                          doi: 10.26508/lsa.201800018pubmed: 30456375google scholar: lookup
                        8. Yuan X, Deng X, Zhou X, Zhang A, Xing Y, Zhang Z, Zhang H, Li J. MiR-126-3p promotes the cell proliferation and inhibits the cell apoptosis by targeting TSC1 in the porcine granulosa cells. In Vitro Cell Dev Biol Anim 2018 Dec;54(10):715-724.
                          doi: 10.1007/s11626-018-0292-0pubmed: 30341633google scholar: lookup
                        9. Cowled C, Foo CH, Deffrasnes C, Rootes CL, Williams DT, Middleton D, Wang LF, Bean AGD, Stewart CR. Circulating microRNA profiles of Hendra virus infection in horses. Sci Rep 2017 Aug 7;7(1):7431.
                          doi: 10.1038/s41598-017-06939-wpubmed: 28785041google scholar: lookup
                        10. Parkinson NJ, Buechner-Maxwell VA, Witonsky SG, Pleasant RS, Werre SR, Ahmed SA. Characterization of basal and lipopolysaccharide-induced microRNA expression in equine peripheral blood mononuclear cells using Next-Generation Sequencing. PLoS One 2017;12(5):e0177664.
                          doi: 10.1371/journal.pone.0177664pubmed: 28552958google scholar: lookup
                        11. Gim JA, Kim HS. Identification and Expression of Equine MER-Derived miRNAs. Mol Cells 2017 Apr;40(4):262-270.
                          doi: 10.14348/molcells.2017.2295pubmed: 28320202google scholar: lookup
                        12. Qu B, Qiu Y, Zhen Z, Zhao F, Wang C, Cui Y, Li Q, Zhang L. Computational identification and characterization of novel microRNA in the mammary gland of dairy goat (Capra hircus). J Genet 2016 Sep;95(3):625-37.
                          doi: 10.1007/s12041-016-0674-6pubmed: 27659334google scholar: lookup
                        13. Stefaniuk M, Ropka-Molik K. RNA sequencing as a powerful tool in searching for genes influencing health and performance traits of horses. J Appl Genet 2016 May;57(2):199-206.
                          doi: 10.1007/s13353-015-0320-7pubmed: 26446669google scholar: lookup
                        14. Cowled C, Stewart CR, Likic VA, Friedländer MR, Tachedjian M, Jenkins KA, Tizard ML, Cottee P, Marsh GA, Zhou P, Baker ML, Bean AG, Wang LF. Characterisation of novel microRNAs in the Black flying fox (Pteropus alecto) by deep sequencing. BMC Genomics 2014 Aug 15;15(1):682.
                          doi: 10.1186/1471-2164-15-682pubmed: 25128405google scholar: lookup
                        15. Anthon C, Tafer H, Havgaard JH, Thomsen B, Hedegaard J, Seemann SE, Pundhir S, Kehr S, Bartschat S, Nielsen M, Nielsen RO, Fredholm M, Stadler PF, Gorodkin J. Structured RNAs and synteny regions in the pig genome. BMC Genomics 2014 Jun 10;15(1):459.
                          doi: 10.1186/1471-2164-15-459pubmed: 24917120google scholar: lookup
                        16. Maghuly F, Ramkat RC, Laimer M. Virus versus host plant microRNAs: who determines the outcome of the interaction?. PLoS One 2014;9(6):e98263.
                          doi: 10.1371/journal.pone.0098263pubmed: 24896088google scholar: lookup
                        17. Kim MC, Lee SW, Ryu DY, Cui FJ, Bhak J, Kim Y. Identification and characterization of microRNAs in normal equine tissues by Next Generation Sequencing. PLoS One 2014;9(4):e93662.
                          doi: 10.1371/journal.pone.0093662pubmed: 24695583google scholar: lookup
                        18. Fang W, Zhou N, Li D, Chen Z, Jiang P, Zhang D. An integrative bioinformatics pipeline for the genomewide identification of novel porcine microRNA genes. J Genet 2013 Dec;92(3):587-93.
                          doi: 10.1007/s12041-013-0294-3pubmed: 24371181google scholar: lookup
                        19. Yuan Z, Liu H, Nie Y, Ding S, Yan M, Tan S, Jin Y, Sun X. Identification of novel microRNAs in primates by using the synteny information and small RNA deep sequencing data. Int J Mol Sci 2013 Oct 16;14(10):20820-32.
                          doi: 10.3390/ijms141020820pubmed: 24135875google scholar: lookup
                        20. Das PJ, McCarthy F, Vishnoi M, Paria N, Gresham C, Li G, Kachroo P, Sudderth AK, Teague S, Love CC, Varner DD, Chowdhary BP, Raudsepp T. Stallion sperm transcriptome comprises functionally coherent coding and regulatory RNAs as revealed by microarray analysis and RNA-seq. PLoS One 2013;8(2):e56535.
                          doi: 10.1371/journal.pone.0056535pubmed: 23409192google scholar: lookup
                        21. Sun J, Liu HP, Deng JE, Zhou M. Systematic analysis of genomic organization and heterogeneities of miRNA cluster in vertebrates. Mol Biol Rep 2012 May;39(5):5143-9.
                          doi: 10.1007/s11033-011-1310-4pubmed: 22160431google scholar: lookup
                        22. Yuan Z, Sun X, Jiang D, Ding Y, Lu Z, Gong L, Liu H, Xie J. Origin and evolution of a placental-specific microRNA family in the human genome. BMC Evol Biol 2010 Nov 10;10:346.
                          doi: 10.1186/1471-2148-10-346pubmed: 21067568google scholar: lookup
                        23. Hill EW, McGivney BA, Gu J, Whiston R, Machugh DE. A genome-wide SNP-association study confirms a sequence variant (g.66493737C>T) in the equine myostatin (MSTN) gene as the most powerful predictor of optimum racing distance for Thoroughbred racehorses. BMC Genomics 2010 Oct 11;11:552.
                          doi: 10.1186/1471-2164-11-552pubmed: 20932346google scholar: lookup
                        24. Cullen JN, Cieslak J, Petersen JL, Bellone RR, Finno CJ, Kalbfleisch TS, Calloe K, Capomaccio S, Cappelli K, Coleman SJ, Distl O, Durward-Akhurst SA, Giulotto E, Hamilton NA, Hill EW, Katz LM, Klaerke DA, Lindgren G, MacHugh DE, Mackowski M, MacLeod JN, Metzger J, Murphy BA, Orlando L, Raudsepp T, Silvestrelli M, Strand E, Tozaki T, Trachsel DS, Valderrama Figueroa LS, Velie BD, Wade CM, Waud B, Mickelson JR, McCue ME. Charting the equine miRNA landscape: An integrated pipeline and browser for annotating, quantifying, and visualizing expression. PLoS Genet 2025 Sep;21(9):e1011835.
                          doi: 10.1371/journal.pgen.1011835pubmed: 40911641google scholar: lookup
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