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http://dx.doi.org/10.3745/JIPS.04.0051

miRNA Pattern Discovery from Sequence Alignment  

Sun, Xiaohan (School of Computer Science and Technology, Xidian University)
Zhang, Junying (School of Computer Science and Technology, Xidian University)
Publication Information
Journal of Information Processing Systems / v.13, no.6, 2017 , pp. 1527-1543 More about this Journal
Abstract
MiRNA is a biological short sequence, which plays a crucial role in almost all important biological process. MiRNA patterns are common sequence segments of multiple mature miRNA sequences, and they are of significance in identifying miRNAs due to the functional implication in miRNA patterns. In the proposed approach, the primary miRNA patterns are produced from sequence alignment, and they are then cut into short segment miRNA patterns. From the segment miRNA patterns, the candidate miRNA patterns are selected based on estimated probability, and from which, the potential miRNA patterns are further selected according to the classification performance between authentic and artificial miRNA sequences. Three parameters are suggested that bi-nucleotides are employed to compute the estimated probability of segment miRNA patterns, and top 1% segment miRNA patterns of length four in the order of estimated probabilities are selected as potential miRNA patterns.
Keywords
Deep Sequencing Data; miRNA; Pattern Discovery;
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1 R. Jackups and J. Liang, "Combinatorial analysis for sequence and spatial motif discovery in short sequence fragments," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 3, pp. 524-536, 2010.   DOI
2 H. Zheng, H. Wang, and F. Azuaje, "Improving pattern discovery and visualization of SAGE data through poisson-based self-adaptive neural networks," IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 4, pp. 459-469, 2008.   DOI
3 O. Westesson, L. Barquist, and I. Holmes, "HandAlign: bayesian multiple sequence alignment, phylogeny and ancestral reconstruction," Bioinformatics, vol. 28, no. 8, pp. 1170-1171, 2012.   DOI
4 A. Kawrykow, G. Roumanis, A. Kam, D. Kwak, C. Leung, C. Wu, et al., "Phylo: a citizen science approach for improving multiple sequence alignment," PLoS One, vol. 7, no. 3, article no. e31362, 2012.
5 E. Pruesse, J. Peplies, and F. O. Glockner, "SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes," Bioinformatics, vol. 28, pp. 1823-1829, 2012.   DOI
6 K. C. Miranda, T. Huynh, Y. Tay, Y. S. Ang, W. L. Tam, A. M. Thomson, B. Lim, and I. Rigoutsos, "A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes," Cell, vol. 126, no. 6, pp. 1203-1217, 2006.   DOI
7 M. Hafner, P. Landgraf, J. Ludwig, A. Rice, T. Ojo, C. Lin, D. Holoch, C. Lim, and T. Tuschl, "Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing," Methods, vol. 44, no. 1, pp. 3-12, 2008.   DOI
8 N. Lavrac, P. Flach, and B. Zupan, "Rule evaluation measures: a unifying view," in Proceedings of 9th International Workshop on Inductive Logic Programming (ILP-99), Bled, Slovenia, 1999, pp. 174-185.
9 L. Geng and H. J. Hamilton, "Interestingness measures for data mining: a survey," ACM Computing Surveys (CSUR), vol. 38, no. 3, article no. 9, 2006.
10 I. Bentwich, "Prediction and validation of microRNAs and their targets," FEBS Letters, vol. 579, no. 26, pp. 5904-5910, 2005.   DOI
11 M. R. Friedlander, W. Chen, C. Adamidi, J. Maaskola, R. Einspanier, S. Knespel, and N. Rajewsky, "Discovering microRNAs from deep sequencing data using miRDeep," Nature Biotechnology, vol. 26, no. 4, pp. 407-415, 2008.   DOI
12 V. Williamson, A. Kim, B. Xie, G. O. McMichael, Y. Gao, and V. Vladimirov, "Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation," Briefings in Bioinformatics, vol. 14, no. 1, pp. 36-45, 2013.   DOI
13 Y. Saeys, I. Inza, and P. Larranaga, "A review of feature selection techniques in bioinformatics," Bioinformatics, vol. 23, no. 19, pp. 2507-2517, 2007.   DOI
14 A. K. C. Wong, D. Zhuang, G. C. L. Li, and E. S. A. Lee, "Discovery of non-induced patterns from sequences," in Proceedings of 5th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2010), Nijmegen, The Netherlands, 2010, pp. 149-160.
15 M. R. Friedlander, S. D. Mackowiak, N. Li, W. Chen, and N. Rajewsky, "miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades," Nucleic Acids Research, vol. 40, no. 1, pp. 37-52, 2012.   DOI
16 W. Shen, M. Chen, G. Wei, and Y. Li, "MicroRNA prediction using a fixed-order Markov model based on the secondary structure pattern," PLoS One, vol. 7, no. 10, article no. e48236, 2012.
17 X. Ji, J. Bailey, and G. Dong, "Mining minimal distinguishing subsequence patterns with gap constraints," Knowledge and Information Systems, vol. 11, no. 3, pp. 259-286, 2007.   DOI
18 G. Dong and J. Bailey, Contrast Data Mining: Concepts, Algorithms, and Applications. Boca Raton, FL: CRC Press, 2013.
19 R. C. de Amorim, "Computational methods of feature selection," Information Processing & Management, vol. 45, no. 4, pp. 490-493, 2009.   DOI
20 G. Nuel, L. Regad, J. Martin, and A. C. Camproux, "Exact distribution of a pattern in a set of random sequences generated by a Markov source: applications to biological data," Algorithms for Molecular Biology, vol. 5, article no. 15, 2010.
21 B. Liu, J. Li, and M. J. Cairns, "Identifying miRNAs, targets and functions," Briefings in Bioinformatics, vol. 15, no. 1, pp. 1-19, 2014.   DOI
22 R. M. Marin, M. Sulc, and J. Vanicek, "Searching the coding region for microRNA targets," RNA, vol. 19, no. 4, pp. 467-474, 2013.   DOI
23 S. T. Kalinowski, T. M. Andrews, M. J. Leonard, and M. Snodgrass, "Are Africans, Europeans, and Asians different 'races'? A guided-inquiry lab for introducing undergraduate students to genetic diversity and preparing them to study natural selection," CBE Life Sciences Education, vol. 11, no. 2, pp. 142-151, 2012.   DOI