513
Views
105
CrossRef citations to date
0
Altmetric
Articles

iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach

, , , &
Pages 223-235 | Received 17 Oct 2014, Accepted 29 Jan 2015, Published online: 03 Mar 2015

References

  • Agarwal, S., Vaz, C., Bhattacharya, A., & Srinivasan, A. (2010). Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM). BMC Bioinformatics, 11, S29.10.1186/1471-2105-11-S1-S29
  • Althaus, I. W., Chou, J. J., Gonzales, A. J., Deibel, M. R., Chou, K. C., Kezdy, F. J., … Palmer, J. R. (1993). Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry, 32, 6548–6554.10.1021/bi00077a008
  • Ambros, V. (2003). A uniform system for microRNA annotation. RNA, 9, 277–279.10.1261/rna.2183803
  • Brameier, M., & Wiuf, C. (2007). Ab initio identification of human microRNAs based on structure motifs. BMC Bioinformatics, 8, 478.10.1186/1471-2105-8-478
  • Cai, Y. D., & Zhou, G. P. (2003). Support vector machines for predicting membrane protein types by using functional domain composition. Biophysical Journal, 84, 3257–3263.10.1016/S0006-3495(03)70050-2
  • Chen, W., Feng, P. M., & Deng, E. Z. (2014). iTIS-PseTNC: A sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Analytical Biochemistry, 462, 76–83.10.1016/j.ab.2014.06.022
  • Chen, W., Feng, P. M., & Lin, H. (2013). iRSpot-PseDNC: Identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Research, 41, e68.10.1093/nar/gks1450
  • Chen, W., Feng, P. M., & Lin, H. (2014). iSS-PseDNC: Identifying splicing sites using pseudo dinucleotide composition. Biomed Research International (BMRI), 2014, 623149.
  • Chen, W., Lei, T. Y., Jin, D. C., & Lin, H. (2014). PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition. Analytical Biochemistry, 456, 53–60.10.1016/j.ab.2014.04.001
  • Chen, J., Liu, H., & Yang, J. (2007). Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids, 33, 423–428.10.1007/s00726-006-0485-9
  • Chen, L., Zeng, W. M., Cai, Y. D., & Feng, K. Y. (2012). Predicting Anatomical Therapeutic Chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities. PLoS ONE, 7, e35254.10.1371/journal.pone.0035254
  • Chen, W., Zhang, X., Brooker, J., & Lin, H. (2015). PseKNC-General: A cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics, 31, 119–120.10.1093/bioinformatics/btu602
  • Chou, K. C. (1996). Review: Prediction of human immunodeficiency virus protease cleavage sites in proteins. Analytical Biochemistry, 233, 1–14.10.1006/abio.1996.0001
  • Chou, K. C. (1999). A key driving force in determination of protein structural classes. Biochemical and Biophysical Research Communications, 264, 216–224.10.1006/bbrc.1999.1325
  • Chou, K. C. (2001a). Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol.44, 60), 43, 246–255.
  • Chou, K. C. (2001b). Using subsite coupling to predict signal peptides. Protein Engineering Design and Selection, 14, 75–79.10.1093/protein/14.2.75
  • Chou, K. C. (2005). Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics, 21, 10–19.10.1093/bioinformatics/bth466
  • Chou, K. C. (2010). Graphic rule for drug metabolism systems. Current Drug Metabolism, 11, 369–378.10.2174/138920010791514261
  • Chou, K. C. (2011). Some remarks on protein attribute prediction and pseudo amino acid composition (50th anniversary year review). Journal of Theoretical Biology, 273, 236–247.10.1016/j.jtbi.2010.12.024
  • Chou, K. C. (2013). Some remarks on predicting multi-label attributes in molecular biosystems. Molecular BioSystems, 9, 1092–1100.10.1039/c3mb25555g
  • Chou, K. C. (2015). Impacts of bioinformatics to medicinal chemistry. Medicinal Chemistry. doi:10.2174/1573406411666141229162834
  • Chou, K. C., & Cai, Y. D. (2002). Using functional domain composition and support vector machines for prediction of protein subcellular location. Journal of Biological Chemistry, 277, 45765–45769.10.1074/jbc.M204161200
  • Chou, K. C., & Cai, Y. D. (2003). Prediction and classification of protein subcellular location: Sequence-order effect and pseudo amino acid composition. Journal of Cellular Biochemistry (Addendum, ibid. 2004, 91, 1085), 90, 1250–1260.
  • Chou, K. C., & Cai, Y. D. (2005). Prediction of membrane protein types by incorporating amphipathic effects. Journal of Chemical Information and Modeling, 45, 407–413.10.1021/ci049686v
  • Chou, K. C., & Shen, H. B. (2007). Review: Recent progresses in protein subcellular location prediction. Analytical Biochemistry, 370, 1–16.10.1016/j.ab.2007.07.006
  • Chou, K. C., & Shen, H. B. (2009). Review: Recent advances in developing web-servers for predicting protein attributes. Natural Science, 1, 63–92.10.4236/ns.2009.12011
  • Cortess, C., & Vapnik, V. (1995). Support-vector networks. Machine Leaming, 20, 273–297.
  • Cristianini, N., & Shawe-Taylor, J. (2000). An introduction of support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press.10.1017/CBO9780511801389
  • Du, P., Gu, S., & Jiao, Y. (2014). PseAAC-General: Fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets. International Journal of Molecular Sciences, 15, 3495–3506.10.3390/ijms15033495
  • Du, Q. S., Jiang, Z. Q., He, W. Z., & Li, D. P. (2006). Amino acid principal component analysis (AAPCA) and its applications in protein structural class prediction. Journal of Biomolecular Structure and Dynamics, 23, 635–640.10.1080/07391102.2006.10507088
  • Fan, G. L., & Li, Q. Z. (2013). Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou's pseudo amino acid composition. Journal of Theoretical Biology, 334, 45–51.10.1016/j.jtbi.2013.06.003
  • Fan, Y. N., Xiao, X., & Min, J. L. (2014). iNR-Drug: Predicting the interaction of drugs with nuclear receptors in cellular networking. International Journal of Molecular Sciences, 15, 4915–4937.10.3390/ijms15034915
  • Fawcett, J. A. (2005). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874.
  • Georgiou, D. N., Karakasidis, T. E., & Megaritis, A. C. (2013). A short survey on genetic sequences, Chou's pseudo amino acid composition and its combination with fuzzy set theory. The Open Bioinformatics Journal, 7, 41–48.10.2174/1875036201307010041
  • Guo, S. H., Deng, E. Z., Xu, L. Q., Ding, H., & Lin, H. (2014). iNuc-PseKNC: A sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics, 30, 1522–1529.10.1093/bioinformatics/btu083
  • Hajisharifi, Z., Piryaiee, M., Mohammad Beigi, M., Behbahani, M., & Mohabatkar, H. (2014). Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test. Journal of Theoretical Biology, 341, 34–40.10.1016/j.jtbi.2013.08.037
  • Hayat, M., & Iqbal, N. (2014). Discriminating protein structure classes by incorporating Pseudo average chemical shift to Chou's general PseAAC and support vector machine. Computer Methods and Programs in Biomedicine, 116, 184–192.10.1016/j.cmpb.2014.06.007
  • Helvik, S. A., Snove, O., Jr., & Saetrom, P. (2007). Reliable prediction of Drosha processing sites improves microRNA gene prediction. Bioinformatics, 23, 142–149.10.1093/bioinformatics/btl570
  • Hofacker, I. L. (2003). Vienna RNA secondary structure server. Nucleic Acids Research, 31, 3429–3431.10.1093/nar/gkg599
  • Huang, T. H., Fan, B., Rothschild, M. F., Hu, Z. L., Li, K., & Zhao, S. H. (2007). MiRFinder: An improved approach and software implementation for genome-wide fast microRNA precursor scans. BMC Bioinformatics, 8, 341.10.1186/1471-2105-8-341
  • Jiang, P., Wu, H., Wang, W., Ma, W., Sun, X., & Lu, Z. (2007). MiPred: Classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Research, 35, W339–W344.10.1093/nar/gkm368
  • Kong, L., Zhang, L., & Lv, J. (2014). Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou’s pseudo amino acid composition. Journal of Theoretical Biology, 344, 12–18.10.1016/j.jtbi.2013.11.021
  • Kozomara, A., & Griffiths-Jones, S. (2011). miRBase: Integrating microRNA annotation and deep-sequencing data. Nucleic Acids Research, 39, D152–D157.10.1093/nar/gkq1027
  • Li, C., Feng, Y., Coukos, G., & Zhang, L. (2009). Therapeutic microRNA strategies in human cancer. The AAPS Journal, 11, 747–757.10.1208/s12248-009-9145-9
  • Li, W., & Godzik, A. (2006). Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 22, 1658–1659.10.1093/bioinformatics/btl158
  • Lin, H., Deng, E. Z., & Ding, H. (2014). iPro54-PseKNC: A sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Research, 42, 12961–12972.10.1093/nar/gku1019
  • Lin, W. Z., Fang, J. A., & Xiao, X. (2013). iLoc-Animal: A multi-label learning classifier for predicting subcellular localization of animal proteins. Molecular BioSystems, 9, 634–644.10.1039/c3mb25466f
  • Lin, S. X., & Lapointe, J. (2013). Theoretical and experimental biology in one. Journal of Biomedical Science and Engineering, 6, 435–442.10.4236/jbise.2013.64054
  • Liu, W., & Chou, K. C. (1999). Prediction of protein secondary structure content. Protein Engineering Design and Selection, 12, 1041–1050.10.1093/protein/12.12.1041
  • Liu, B., Fang, L., Jie, C., Liu, F., Wang, X. (2015). miRNA-dis: microRNA precursor identification based on distance structure status pairs. Molecular BioSystems. doi:10.1039/C1035MB00050E
  • Liu, B., Liu, F., Fang, L., & Wang, X. (2015). repDNA: A python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics. doi:10.1093/bioinformatics/btu1820
  • Liu, B., Xu, J., Fan, S., Xu, R., Zhou, J., & Wang, X. (2015). PseDNA-pro: DNA-binding protein identification by combining Chou's PseAAC and physicochemical distance. Molecular Informatics, 34, 8–17.
  • Liu, B., Xu, J., Lan, X., Xu, R., & Zhou, J. (2014). iDNA-Prot|dis: Identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS ONE, 9, e106691.
  • Liu, Z., Xiao, X., & Qiu, W. R. (2015). iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition. Analytical Biochemistry. doi:10.1016/j.ab.2014.12.009
  • Liu, B., Wang, X., Chen, Q., Dong, Q., & Lan, X. (2012). Using amino acid physicochemical distance transformation for fast protein remote homology detection. PLoS ONE, 7, e46633.
  • Liu, B., Wang, X., Zou, Q., Dong, Q., & Chen, Q. (2013). Protein remote homology detection by combining Chou's pseudo amino acid composition and profile-based protein representation. Molecular Informatics, 32, 775–782.
  • Liu, B., Zhang, D., Xu, R., Xu, J., & Wang, X. (2014). Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics, 30, 472–479.10.1093/bioinformatics/btt709
  • Lopes, I. O., Schliep, A., & Carvalho, A. C. (2014). The discriminant power of RNA features for pre-miRNA recognition. BMC Bioinformatics, 15, 124.10.1186/1471-2105-15-124
  • Mohabatkar, H., Beigi, M. M., Abdolahi, K., & Mohsenzadeh, S. (2013). Prediction of allergenic proteins by means of the concept of Chou's pseudo amino acid composition and a machine learning approach. Medicinal Chemistry, 9, 133–137.10.2174/157340613804488341
  • Mondal, S., & Pai, P. P. (2014). Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction. Journal of Theoretical Biology, 356, 30–35.10.1016/j.jtbi.2014.04.006
  • Nam, J. W., Shin, K. R., Han, J., Lee, Y., Kim, V. N., & Zhang, B. T. (2005). Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucleic Acids Research, 33, 3570–3581.10.1093/nar/gki668
  • Nanni, L., Brahnam, S., & Lumini, A. (2014). Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition. Journal of Theoretical Biology, 360, 109–116.10.1016/j.jtbi.2014.07.003
  • Qiu, W. R., & Xiao, X. (2014). iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components. International Journal of Molecular Sciences, 15, 1746–1766.10.3390/ijms15021746
  • Qiu, W. R., Xiao, X., & Lin, W. Z. (2014). iUbiq-Lys: Prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a grey system model. Journal of Biomolecular Structure and Dynamics (JBSD). doi:10.1080/07391102.2014.968875
  • Shen, H. B., & Chou, K. C. (2010). Virus-mPLoc: A fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. Journal of Biomolecular Structure and Dynamics, 28, 175–186.10.1080/07391102.2010.10507351
  • Shen, H. B., & Yang, J. (2007). Euk-PLoc: An ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids, 33, 57–67.10.1007/s00726-006-0478-8
  • Wang, Y., Chen, X., Jiang, W., Li, L., Li, W., Yang, L., … Li, X. (2011). Predicting human microRNA precursors based on an optimized feature subset generated by GA-SVM. Genomics, 98, 73–78.10.1016/j.ygeno.2011.04.011
  • Wang, T., Yang, J., & Shen, H. B. (2008). Predicting membrane protein types by the LLDA algorithm. Protein & Peptide Letters, 15, 915–921.
  • Wei, L. Y., Liao, M. H., Gao, Y., Ji, R. R., He, Z. Y., & Zou, Q. (2014). Improved and promising identification of human micrornas by incorporating a high-quality negative set. Computational Biology and Bioinformatics, 11, 192–201.
  • Wu, Y., Wei, B., Liu, H., Li, T., & Rayner, S. (2011). MiRPara: A SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinformatics, 12, 107.10.1186/1471-2105-12-107
  • Xiao, X., Min, J. L., Lin, W. Z., & Liu, Z. (2014). iDrug-Target: Predicting the interactions between drug compounds and target proteins in cellular networking via the benchmark dataset optimization approach. Journal of Biomolecular Structure & Dynamics (JBSD). doi:10.1080/07391102.2014.998710
  • Xiao, X., Wang, P., Lin, W. Z., & Jia, J. H. (2013). iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Analytical Biochemistry, 436, 168–177.10.1016/j.ab.2013.01.019
  • Xiao, X., & Wu, Z. C. (2011). iLoc-Virus: A multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. Journal of Theoretical Biology, 284, 42–51.10.1016/j.jtbi.2011.06.005
  • Xu, Y., Ding, J., & Wu, L. Y. (2013). iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS ONE, 8, e55844.10.1371/journal.pone.0055844
  • Xu, Y., Shao, X. J., Wu, L. Y., & Deng, N. Y. (2013). iSNO-AAPair: Incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ, 1, e171.10.7717/peerj.171
  • Xu, Y., Wen, X., Wen, L. S., & Wu, L. Y. (2014). iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS ONE, 9, e105018.10.1371/journal.pone.0105018
  • Xu, R., Zhou, J., Liu, B., He, Y. A., Zou, Q., & Wang, X. (2014). Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach. Journal of Biomolecular Structure & Dynamics (JBSD). doi:10.1080/07391102.2014.968624
  • Xuan, P., Guo, M., Liu, X., Huang, Y., Li, W., & Huang, Y. (2011). PlantMiRNAPred: Efficient classification of real and pseudo plant pre-miRNAs. Bioinformatics, 27, 1368–1376.10.1093/bioinformatics/btr153
  • Xue, C., Li, F., He, T., Liu, G. P., Li, Y., & Zhang, X. (2005). Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics, 6, 310.10.1186/1471-2105-6-310
  • Yousef, M., Nebozhyn, M., Shatkay, H., Kanterakis, S., Showe, L. C., & Showe, M. K. (2006). Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier. Bioinformatics, 22, 1325–1334.10.1093/bioinformatics/btl094
  • Zhong, L., Wang, J. T., Wen, D., Aris, V., Soteropoulos, P., & Shapiro, B. A. (2013). Effective classification of microRNA precursors using feature mining and AdaBoost algorithms. OMICS: A Journal of Integrative Biology, 17, 486–493.10.1089/omi.2013.0011
  • Zhong, W. Z., & Zhou, S. F. (2014). Molecular science for drug development and biomedicine. International Journal of Molecular Sciences, 15, 20072–20078.10.3390/ijms151120072
  • Zhou, G. P. (2011). The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. Journal of Theoretical Biology, 284, 142–148.10.1016/j.jtbi.2011.06.006

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.