StarSeeker: an Automated Tool for Mature Duplex MicroRNA Sequence Identification Based on Secondary Structure Modeling of Precursor Molecule

Overview

Journal J Biol Res (Thessalon)

Specialty Biology

Date 2018 Jun 28

PMID 29946534

Citations 1

Authors

Paschalis Natsidis

Ilias Kappas

Wojciech M Karlowski

Affiliations

Soon will be listed here.

Abstract

Background: MicroRNAs (miRNAs) are small, non-coding RNA molecules that play a key role in gene regulation in both plants and animals. MicroRNA biogenesis involves the enzymatic processing of a primary RNA transcript. The final step is the production of a duplex molecule, often designated as miRNA:miRNA*, that will yield a functional miRNA by separation of the two strands. This miRNA will be incorporated into the RNA-induced silencing complex, which subsequently will bind to its target mRNA in order to suppress its expression. The analysis of miRNAs is still a developing area for computational biology with many open questions regarding the structure and function of this important class of molecules. Here, we present StarSeeker, a simple tool that outputs the putative miRNA* sequence given the precursor and the mature sequences.

Results: We evaluated StarSeeker using a dataset consisting of all plant sequences available in miRBase (6992 precursor sequences and 8496 mature sequences). The program returned a total of 15,468 predicted miRNA* sequences. Of these, 2650 sequences were matched to annotated miRNAs (~ 90% of the miRBase-annotated sequences). The remaining predictions could not be verified, mainly because they do not comply with the rule requiring the two overhanging nucleotides in the duplex molecule.

Conclusions: The expression pattern of some miRNAs in plants can be altered under various abiotic stress conditions. Potential miRNA* molecules that do not degrade can thus be detected and also discovered in high-throughput sequencing data, helping us to understand their role in gene regulation.

Citing Articles

The Multiverse of Plant Small RNAs: How Can We Explore It?.

Ivanova Z, Minkov G, Gisel A, Yahubyan G, Minkov I, Toneva V Int J Mol Sci. 2022; 23(7).

PMID: 35409340 PMC: 8999349. DOI: 10.3390/ijms23073979.

References

Leclercq M, Diallo A, Blanchette M . Computational prediction of the localization of microRNAs within their pre-miRNA. Nucleic Acids Res. 2013; 41(15):7200-11. PMC: 3753617. DOI: 10.1093/nar/gkt466. View

Kruszka K, Pacak A, Swida-Barteczka A, Nuc P, Alaba S, Wroblewska Z . Transcriptionally and post-transcriptionally regulated microRNAs in heat stress response in barley. J Exp Bot. 2014; 65(20):6123-35. PMC: 4203144. DOI: 10.1093/jxb/eru353. View

Barciszewska-Pacak M, Milanowska K, Knop K, Bielewicz D, Nuc P, Plewka P . Arabidopsis microRNA expression regulation in a wide range of abiotic stress responses. Front Plant Sci. 2015; 6:410. PMC: 4454879. DOI: 10.3389/fpls.2015.00410. View

Karathanasis N, Tsamardinos I, Poirazi P . MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology. PLoS One. 2015; 10(5):e0126151. PMC: 4427487. DOI: 10.1371/journal.pone.0126151. View

Jazdzewski K, Liyanarachchi S, Swierniak M, Pachucki J, Ringel M, Jarzab B . Polymorphic mature microRNAs from passenger strand of pre-miR-146a contribute to thyroid cancer. Proc Natl Acad Sci U S A. 2009; 106(5):1502-5. PMC: 2635764. DOI: 10.1073/pnas.0812591106. View

Mattei E, Ausiello G, Ferre F, Helmer-Citterich M . A novel approach to represent and compare RNA secondary structures. Nucleic Acids Res. 2014; 42(10):6146-57. PMC: 4041456. DOI: 10.1093/nar/gku283. View

Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R . Fast and effective prediction of microRNA/target duplexes. RNA. 2004; 10(10):1507-17. PMC: 1370637. DOI: 10.1261/rna.5248604. View

Kurihara Y, Watanabe Y . Arabidopsis micro-RNA biogenesis through Dicer-like 1 protein functions. Proc Natl Acad Sci U S A. 2004; 101(34):12753-8. PMC: 515125. DOI: 10.1073/pnas.0403115101. View

Friedman R, Farh K, Burge C, Bartel D . Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2008; 19(1):92-105. PMC: 2612969. DOI: 10.1101/gr.082701.108. View

10.

Gkirtzou K, Tsamardinos I, Tsakalides P, Poirazi P . MatureBayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors. PLoS One. 2010; 5(8):e11843. PMC: 2917354. DOI: 10.1371/journal.pone.0011843. View

11.

Bottino M, Rosario S, Grativol C, Thiebaut F, Rojas C, Farrineli L . High-throughput sequencing of small RNA transcriptome reveals salt stress regulated microRNAs in sugarcane. PLoS One. 2013; 8(3):e59423. PMC: 3609749. DOI: 10.1371/journal.pone.0059423. View

12.

Lund E, Dahlberg J . Substrate selectivity of exportin 5 and Dicer in the biogenesis of microRNAs. Cold Spring Harb Symp Quant Biol. 2007; 71:59-66. DOI: 10.1101/sqb.2006.71.050. View

13.

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; 12:107. PMC: 3110143. DOI: 10.1186/1471-2105-12-107. View

14.

Rauhut R, Lendeckel W, Tuschl T . Identification of novel genes coding for small expressed RNAs. Science. 2001; 294(5543):853-8. DOI: 10.1126/science.1064921. View

15.

Bartel D . MicroRNAs: target recognition and regulatory functions. Cell. 2009; 136(2):215-33. PMC: 3794896. DOI: 10.1016/j.cell.2009.01.002. View

16.

Kozomara A, Griffiths-Jones S . miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2010; 39(Database issue):D152-7. PMC: 3013655. DOI: 10.1093/nar/gkq1027. View