Prediction and In Silico Validation of Micro-RNAs in Different Tissues Originated from Ovine Chromosome 20

Document Type : Genetics & breeding

Authors

• Vahideh Abbasi ¹
• Mohammad Reza Nassiry ²
• Ali Javadmannesh ¹

¹ Department Animal Science, Faculty of agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.

² Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

10.22067/ijasr.v1397i1.74619

Abstract

Introduction MicroRNAs (miRNAs) are small noncoding RNA molecules that are found in plants, animals and some viruses and play important role in regulation of transcription (3). Recently, importance of miRNA roles in biology of living organisms has been discovered, thus miRNAs identification became more important (1). Experimental detection of miRNAs can be obtained using different miRNA profiling methods, such as quantitative real-time PCR, microarray, and high-throughput RNA sequencing technologies. Since the identification and verification of miRNA by laboratory methods is time-consuming and costly (4), for improving miRNA identification and lowering costs, it is more reasonable that miRNA loci predicted by reliable bioinformatic approaches then experimental methods used for confirmation. This may decrease false positive results. Recently, several hundreds of miRNAs reported in variety of species including human and mouse, as well as domestic animals such as cattle, pig, chicken and goat, however there are relatively less information about sheep miRNAs. In this study, we developed a method for prediction and in silico validation of miRNAs located on ovine chromosome twenty.
Materials and Methods In this study, an ab initio approach was used. The sequence of ovine chromosome 20 was used as input for EMBOSS and Sequence-Structure Motif Base: Pre-miRNA Prediction Webservers applications, then all predicted stem and loop structures were entered into mfold software. Pre-miRNA features for them were calculated and sequences that had these features were selected. Since the probability of miRNA presence in the coding region is very low, miRNAs that were predicted in the coding regions were removed. To confirm the prediction of miRNAs, selected sequences were homology searched within all registered miRNAs in miRBase. In order to evaluate the in silico expression of miRNAs, predicted miRNAs were BLASTed against expression data from Sequence Read Archive (SRA) of ovine muscle tissue (Accession: PRJNA223213), liver tissue (Accession: GSM1366318) and mixture of tissues including heart, kidney, brain, liver, ovary, lung, skin, and adipose (Accession: GSE56643). In order to evaluate the accuracy of this method, a positive control region including a cluster of validated miRNAs from ovine chromosome 18 were analyzed by the same method and sensitivity and selectivity of this method were calculated based on this region from chromosome 18.
Results and discussion After prediction by softwares and investigation of pre-miRNAs features by mfold, 400 stem and loop structures that had pre-miRNA features were chosen. Fifty miRNAs from those miRNAs contained conserved mature miRNAs sequence and 350 of them were recognized as novel miRNAs based on registered miRNAs in the mirBase. None of the novel miRNAs were located in the coding regions. In silico validation of these novel miRNAs in SRA data was indicated that 81 miRNAs are expressed in different ovine tissues including 33 in muscle and 10 in liver. Results on the positive control data showed that 40 miRNAs were predicted which majority of them (36 miRNAs) were already validated by experiments. This indicates a high reliability for this method. With putting in sensitivity and selectivity formulas, both of two factors were calculated and it was observed that the sensitivity and selectivity values for our method were 67% and 90%, respectively. Fewer studies accomplished in detection of ovine miRNAs in compare to other farm animals. In previous studies to identify miRNAs in ovine species, mostly laboratory-based or comparative methods were used. This was the first study that used SRA database to check miRNA expression in RNAseq data in order to decrease the discovery of false positive results. Comparing this method with others including CID-miRNA (19), miRPara (20), VMir (6) and miRNAFold (18) methods, we may conclude that this method’s sensitivity is less than CID-miRNA, miRPara, miRNAFold and srnaloop. Although selectivity for this method is higher than all above methods because the false positive in this method is less than other method. This method showed high selectivity and low FP that due to improved prediction method for identify miRNAs.  
Conclusion In the current study, predicted ovine miRNAs were validated by an in silico method using SRA database that resulted in a higher reliability than other ab initio approaches. Although this method is not very fast and fully automated. With running this method on ovine chromosome 20, 81 novel miRNA were predicted which were expressed in different tissues of sheep. This method could be applied on other ovine chromosomes as well as other mammalian species, although future validation by experimental approaches is required

Keywords

• Ab initio
• Bioinformatics
• miRNA
• Sheep

20.1001.1.20083106.1398.11.2.8.0

---

References

1- Abba, M., N. Patil, and H. Allgayer. 2014. MicroRNAs in the Regulation of MMPs and Metastasis. Cancers, 6: 625 645.

2- Barozai, M. Y. Kh. 2012. The novel 172 sheep (Ovis aries) microRNAs and their targets. Molecular Biology Reproduction, 39:6259–6266.

3- Bartel, D. P. 2004. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116: 281-297.

4- Berezikov, E., E. Cuppen, and R. H. Plasterk. 2006. Approaches to microRNA discovery. Nature Genetics, 38 (Suppl: S2) 7.

5- Chen, C. Z., L. Li, H. F. Lodish, and D. P. Bartel. 2004. MicroRNAs modulate hematopoietic lineage differentiation. Science, 303: 83–86.

6- Grundhoff, A., C. S. Sullivan, and D. Ganem. 2006. A combined computational and microarray-based approach identifies novel microRNAs encoded by human gamma-herpesviruses. RNA, 12: 733–750.

7- Hansen, T., T. V. Morten, K. Jorgen, and K. D. Christian. 2014. miRdentify: high stringency miRNA predictor identifies several novel animal miRNAs. Nucleic Acids Research, 42(16): e124.

8- Hertel, Ja., Stadler, and P. 2006. Hairpins in a Haystack: Recognizing microRNA Precursors in Comparative Genomics Data. 22(14): e197-202.

9- Huang, T.H., Fan, B., Rothschild, M.F., Hu, Z.L., Li, K., Zhao, and Sh.H. 2007. MiRFinder: an improved approach and software implementation for genome wide fast microRNA precursor scans. BMC Bioinformatics, 8: 341.

10- Li, S. C., Pan, C. Y., Lin, and W. Ch. 2006. Bioinformatic discovery of microRNA precursors from human ESTs and introns. BMC Genomics, 7:164.

11- Miska, E. A., Alvarez Saavedra, E., Townsend, M., Yoshii, A., Sestan, N., Rakic, P., Constantine Paton, M., and Horvitz, H. R. 2004. Microarray analysis of microRNA expression in the developing mammalian brain. Genome Biol, 5: R68.

12- Rodriguez, A., Griffiths Jones, S., Ashurst, J. L., and Bradley, A. 2004. Identification of mammalian micro RNA host genes and transcription units. Genome Res, 14: 1902 1910.

13- Sadeghi, B., Ahmadi, H., Azimzadeh Jamalkandi, S., Nassiri, M. R., and Masoudi Nejad, A. 2014. BosFinder: a novell pre microRNA gene prediction algorithm in Bos taurus. Animal Genetics, 45(4): 479-84.

14- SantaLucia, J. Jr. 1998. A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proceedings of National Academy of Science USA, 95: 1460-1465.

15- Sewer, A., N. Paul, P. Landgraf, A. Aravin, S. Pfeffer, M. J. Brownstein, Th. Tuschl, E. Nimwegen, and M. Zavolan. 2005. Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics, 6: 267.

16- Sheng, X., X. Song, Y. Yu, L. Niu, Sh. Li, H. Li, C. Wei, T. Liu, L. Zhang, and L. Du. 2011. Characterization of microRNAs from sheep (Ovis aries) using computational and experimental analyses. Molecular Biology of Reproduction, 38(5): 3161–3171.

17- Stark, A., P. Kheradpour, L. Parts, J. Brennecke, E. Hodges, G. J. Hannon, and M. Kellis. (2007). Systematic discovery and characterization of fly microRNAs using 12 Drosophila genomes. Genome Research, 17: 1865–1879.

18- Tempel, S., and F. Tahi. 2012. A fast ab initio method for predicting miRNA precursors in genomes. Nucleic Acids Research, 40(11): e80.

19- Tyagi, S., C. Vaz, V. Gupta, R. Bhatia, S. Maheshwari, A. Srinivasan, and A. Bhattacharya. 2008. CID-miRNA: a web server for prediction of novel miRNA precursors in human genome. Biochemical and Biophysical Research Communications, 372: 831–834.

20- Wu, Y., B. Wei, H. Liu, and T. Li, and S. Rayner. 2011. MiRPara: a SVM based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinformatics, 12: 107.

21- Zhao, Y., J. F. Ransom, A. Li, V. Vedantham, M. Drehle, A. N. Muth, T. Tsuchihashi, MT. McManus, R. J. Schwartz, and D. Srivastava. 2007. Dysregulation of cardiogenesis, cardiac conduction, and cell cycle in mice lacking miRNA 1–2. Cell, 129: 303–317.