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Predicting the Organelle Location of Noncoding RNAs Using Pseudo Nucleotide Compositions

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Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

Noncoding RNAs (ncRNAs) are implicated in various biological processes. Recent findings have demonstrated that the function of ncRNAs correlates with their provenance. Therefore, the recognition of ncRNAs from different organelle genomes will be helpful to understand their molecular functions. However, the weakness of experimental techniques limits the progress toward studying organellar ncRNAs and their functional relevance. As a complement of experiments, computational method provides an important choice to identify ncRNA in different organelles. Thus, a computational model was developed to identify ncRNAs from kinetoplast and mitochondrion organelle genomes. In this model, RNA sequences are encoded by “pseudo dinucleotide composition.” It was observed by the jackknife test that the overall success rate achieved by the proposed model was 90.08 %. We hope that the proposed method will be helpful in predicting ncRNA organellar locations.

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References

  1. Xie C, Yuan J, Li H, Li M, Zhao G, Bu D, Zhu W, Wu W, Chen R, Zhao Y (2014) NONCODEv4: exploring the world of long non-coding RNA genes. Nucleic Acids Res 42(Database issue):D98–D103. doi:10.1093/nar/gkt1222

    Article  CAS  PubMed  Google Scholar 

  2. Mattick JS (2011) Long noncoding RNAs in cell and developmental biology. Semin Cell Dev Biol 22(4):327. doi:10.1016/j.semcdb.2011.05.002

    Article  PubMed  Google Scholar 

  3. Clark MB, Mattick JS (2011) Long noncoding RNAs in cell biology. Semin Cell Dev Biol 22(4):366–376. doi:10.1016/j.semcdb.2011.01.001

    Article  CAS  PubMed  Google Scholar 

  4. Ponting CP, Oliver PL, Reik W (2009) Evolution and functions of long noncoding RNAs. Cell 136(4):629–641. doi:10.1016/j.cell.2009.02.006

    Article  CAS  PubMed  Google Scholar 

  5. Ma L, Bajic VB, Zhang Z (2013) On the classification of long non-coding RNAs. RNA Biol 10(6):925–933. doi:10.4161/rna.24604

    Article  PubMed  Google Scholar 

  6. Maass PG, Luft FC, Bahring S (2014) Long non-coding RNA in health and disease. J Mol Med 92(4):337–346. doi:10.1007/s00109-014-1131-8

    Article  CAS  PubMed  Google Scholar 

  7. Wapinski O, Chang HY (2011) Long noncoding RNAs and human disease. Trends Cell Biol 21(6):354–361. doi:10.1016/j.tcb.2011.04.001

    Article  CAS  PubMed  Google Scholar 

  8. Lung B, Zemann A, Madej MJ, Schuelke M, Techritz S, Ruf S, Bock R, Huttenhofer A (2006) Identification of small non-coding RNAs from mitochondria and chloroplasts. Nucleic Acids Res 34(14):3842–3852. doi:10.1093/nar/gkl448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, Regev A, Rinn JL (2011) Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev 25(18):1915–1927. doi:10.1101/gad.17446611

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28(23):3150–3152. doi:10.1093/bioinformatics/bts565

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chen W, Lei TY, Jin DC, Lin H, Chou KC (2014) PseKNC: a flexible web server for generating pseudo k-tuple nucleotide composition. Anal Biochem 456:53–60. doi:10.1016/j.ab.2014.04.001

    Article  CAS  PubMed  Google Scholar 

  12. Chen W, Feng P, Ding H, Lin H, Chou KC (2015) iRNA-Methyl: identifying N(6)-methyladenosine sites using pseudo nucleotide composition. Anal Biochem 490:26–33. doi:10.1016/j.ab.2015.08.021

    Article  CAS  PubMed  Google Scholar 

  13. Chen W, Lin H, Chou KC (2015) Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol BioSyst 11(10):2620–2634. doi:10.1039/c5mb00155b

    Article  CAS  PubMed  Google Scholar 

  14. Chen W, Feng PM, Deng EZ, Lin H, Chou KC (2014) iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem 462:76–83. doi:10.1016/j.ab.2014.06.022

    Article  CAS  PubMed  Google Scholar 

  15. Feng P, Chen W, Lin H (2014) Prediction of CpG island methylation status by integrating DNA physicochemical properties. Genomics 104(4):229–233. doi:10.1016/j.ygeno.2014.08.011

    Article  CAS  PubMed  Google Scholar 

  16. Chen W, Feng PM, Lin H, Chou KC (2014) iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BioMed Res Int 2014:623149. doi:10.1155/2014/623149

    PubMed  PubMed Central  Google Scholar 

  17. Guo SH, Deng EZ, Xu LQ, Ding H, Lin H, Chen W, Chou KC (2014) iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics 30(11):1522–1529. doi:10.1093/bioinformatics/btu083

    Article  CAS  PubMed  Google Scholar 

  18. Chen W, Feng PM, Lin H, Chou KC (2013) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41(6):e68. doi:10.1093/nar/gks1450

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Feng P, Jiang N, Liu N (2014) Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions. Sci World J 2014:740506. doi:10.1155/2014/740506

    Google Scholar 

  20. Chen W, Zhang X, Brooker J, Lin H, Zhang L, Chou KC (2015) PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 31(1):119–120. doi:10.1093/bioinformatics/btu602

    Article  CAS  PubMed  Google Scholar 

  21. Novikova IV, Hennelly SP, Sanbonmatsu KY (2012) Structural architecture of the human long non-coding RNA, steroid receptor RNA activator. Nucleic Acids Res 40(11):5034–5051. doi:10.1093/nar/gks071

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Maenner S, Blaud M, Fouillen L, Savoye A, Marchand V, Dubois A, Sanglier-Cianferani S, Van Dorsselaer A, Clerc P, Avner P, Visvikis A, Branlant C (2010) 2-D structure of the A region of Xist RNA and its implication for PRC2 association. PLoS Biol 8(1):e1000276. doi:10.1371/journal.pbio.1000276

    Article  PubMed  PubMed Central  Google Scholar 

  23. Xu XJ, Chen SJ (2015) Physics-based RNA structure prediction. Biophys Rep 1(1):2–13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Perez A, Noy A, Lankas F, Luque FJ, Orozco M (2004) The relative flexibility of B-DNA and A-RNA duplexes: database analysis. Nucleic Acids Res 32(20):6144–6151. doi:10.1093/nar/gkh954

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lin H, Liu WX, He J, Liu XH, Ding H, Chen W (2015) Predicting cancerlectins by the optimal g-gap dipeptides. Sci Rep 5:16964. doi:10.1038/srep16964

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ding H, Li D (2015) Identification of mitochondrial proteins of malaria parasite using analysis of variance. Amino Acids 47(2):329–333. doi:10.1007/s00726-014-1862-4

    Article  CAS  PubMed  Google Scholar 

  27. Feng P, Lin H, Chen W, Zuo Y (2014) Predicting the types of J-proteins using clustered amino acids. BioMed Res Int 2014:935719. doi:10.1155/2014/935719

    PubMed  PubMed Central  Google Scholar 

  28. Feng PM, Chen W, Lin H, Chou KC (2013) iHSP-PseRAAAC: identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal Biochem 442(1):118–125. doi:10.1016/j.ab.2013.05.024

    Article  CAS  PubMed  Google Scholar 

  29. Chen W, Feng P, Lin H (2012) Prediction of replication origins by calculating DNA structural properties. FEBS Lett 586(6):934–938. doi:10.1016/j.febslet.2012.02.034

    Article  CAS  PubMed  Google Scholar 

  30. Liu WX, Deng EZ, Chen W, Lin H (2014) Identifying the subfamilies of voltage-gated potassium channels using feature selection technique. Int J Mol Sci 15(7):12940–12951. doi:10.3390/ijms150712940

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lin H, Chen W, Ding H (2013) AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes. PLoS ONE 8(10):e75726. doi:10.1371/journal.pone.0075726

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chen W, Lin H, Feng PM, Ding C, Zuo YC, Chou KC (2012) iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties. PLoS ONE 7(10):e47843. doi:10.1371/journal.pone.0047843

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Liu B, Fang L, Wang S, Wang X, Li H, Chou KC (2015) Identification of microRNA precursor with the degenerate k-tuple or Kmer strategy. J Theor Biol 385:153–159. doi:10.1016/j.jtbi.2015.08.025

    Article  CAS  PubMed  Google Scholar 

  34. Chou KC (2011) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273(1):236–247. doi:10.1016/j.jtbi.2010.12.024

    Article  CAS  PubMed  Google Scholar 

  35. Wang T, Yang J, Shen HB, Chou KC (2008) Predicting membrane protein types by the LLDA algorithm. Protein Pept Lett 15(9):915–921

    Article  CAS  PubMed  Google Scholar 

  36. Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining in bioinformatics using Weka. Bioinformatics 20(15):2479–2481. doi:10.1093/bioinformatics/bth261

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. School of Public Health, North China University of Science and Technology, Tangshan, 063000, China

    Pengmian Feng

  2. Department of Immunology, Zunyi Medical College, Zunyi, 563000, China

    Jidong Zhang

  3. Department of Pathophysiology, Sichuan Medical University, Luzhou, 646000, China

    Hua Tang

  4. Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, 063000, China

    Wei Chen

  5. Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics and Center for Information in Biomedicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Hao Lin

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  1. Pengmian Feng
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  2. Jidong Zhang
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  3. Hua Tang
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  4. Wei Chen
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Correspondence to Wei Chen or Hao Lin.

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Feng, P., Zhang, J., Tang, H. et al. Predicting the Organelle Location of Noncoding RNAs Using Pseudo Nucleotide Compositions. Interdiscip Sci Comput Life Sci 9, 540–544 (2017). https://doi.org/10.1007/s12539-016-0193-4

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  • DOI: https://doi.org/10.1007/s12539-016-0193-4

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