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Computational Analysis of lncRNA from cDNA Sequences

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Part of the Methods in Molecular Biology book series (MIMB,volume 2372)


Based on recent findings, long noncoding (lnc) RNAs represent a potential class of functional molecules within the cell. In this chapter we describe a computational scheme to identify and classify lncRNAs within maize from full-length cDNA sequences to designate subsets of lncRNAs for which biogenesis and regulatory mechanisms may be verified at the bench. We make use of the Coding Potential Calculator and specific Python scripts in our approach.

Key words

  • Long noncoding RNA
  • miRNA
  • siRNA
  • NATs
  • Maize

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  • DOI: 10.1007/978-1-0716-1697-0_20
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  1. Dinger ME, Pang KC, Mercer TR, Mattick JS (2008) Differentiating protein-coding and noncoding RNA: challenges and ambiguities. PLoS Comput Biol 4(11):e1000176

    CrossRef  Google Scholar 

  2. Kong L, Zhang Y, Ye ZQ, Liu XQ, Zhao SQ, Wei L, Gao G (2007) CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res 35:W345–W349

    CrossRef  Google Scholar 

  3. Wang L, Park HJ, Dasari S, Wang S, Kocher J-P, Li W (2013) CPAT: coding-potential assessment tool using an alignment-free logistic regression model. Nucleic Acids Res 41(6):e74

    CrossRef  CAS  Google Scholar 

  4. Li A, Zhang J, Zhou Z (2014) PLEK: a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme. BMC Bioinformatics 15(1):311

    CrossRef  Google Scholar 

  5. Kung JTY, Colognori D, Lee JT (2013) Long noncoding RNAs: past, present, and future. Genetics 193(3):651–669

    CrossRef  CAS  Google Scholar 

  6. Soderlund C, Descour A, Kudrna D, Bomhoff M, Boyd L, Currie J, Angelova A, Collura K, Wissotski M, Ashley E, Morrow D, Fernandes J, Walbot V, Yu Y (2009) Sequencing, mapping, and analysis of 27,455 maize full-length cDNAs. PLoS Genet 5(11):e1000740

    CrossRef  Google Scholar 

  7. Xue C, Li F, He T, Liu G, 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

    CrossRef  Google Scholar 

  8. Gruber AR, Lorenz R, Bernhart SH, Neuböck R, Hofacker IL (2008) The Vienna RNA Websuite. Nucleic Acids Res 36(Web Server issue):W70–W74

    CrossRef  CAS  Google Scholar 

  9. Amaral PP, Clark MB, Gascoigne DK, Dinger ME, Mattick JS (2011) lncRNAdb: a reference database for long noncoding RNAs. Nucleic Acids Res 39:D146–D151

    CrossRef  CAS  Google Scholar 

  10. Wang X, Elling AA, Li X, Li N, Peng Z, He G, Sun H, Qi Y, Liu XS, Deng XW (2009) Genome-wide and organ-specific landscapes of epigenetic modifications and their relationships to mRNA and small RNA transcriptomes in maize. Plant Cell 21:1053–1069

    CrossRef  CAS  Google Scholar 

  11. Jan G, Ruzzo WL (2014) RNA sequence, structure, and function: computational and bioinformatic methods. Methods Mol Biol 1097:437–456

    CrossRef  Google Scholar 

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Correspondence to Karen M. McGinnis .

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Boerner, S., McGinnis, K.M. (2021). Computational Analysis of lncRNA from cDNA Sequences. In: Zhang, L., Hu, X. (eds) Long Non-Coding RNAs. Methods in Molecular Biology, vol 2372. Humana, New York, NY.

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1696-3

  • Online ISBN: 978-1-0716-1697-0

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