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Cited by 238Machine Learning in BioinformaticsRNA and protein synthesis mechanismsFractal and DNA sequence analysis

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Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach

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Liu, Bin, Fang, Longyun, Liu, Fule, Wang, Xiaolong, Chen, Junjie, Chou, Kuo-Chen·2015·PLOS ONE
DOI 10.1371/journal.pone.0121501PMC PMC4378912
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Abstract

Containing about 22 nucleotides, a micro RNA (abbreviated miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the postgenomic age, it is highly desired to develop computational sequence-based methods in this regard. Here two new predictors, called "iMcRNA-PseSSC" and "iMcRNA-ExPseSSC", were proposed for identifying the human pre-microRNAs by incorporating the global or long-range structure-order information using a way quite similar to the pseudo amino acid composition approach. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the two new predictors (accessible at http://bioinformatics.hitsz.edu.cn/iMcRNA/) outperformed or were highly comparable with the best existing predictors in this area.

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Figures

Fig 1 An illustration to show biogenesis of miRNAs and model of miRNA-mediated translational repression or mRNA degradation. MiRNA genes are transcribed by RNA polymerase II [2,90], resulting in the primary transcripts termed as pri-miRNAs, which are typically 60–70 nucleotides. The pri-miRNAs are processed by the enzyme Drosha to release the hairpin-shaped intermediates (pre-miRNAs) [3], followed by being exported into the cytoplasm by Exportin V and Ran-GTP cofactor [4–6], and then cleaved by the enzyme Dicer to yield miRNA/miRNA* duplexes [7–11].
Fig 1 An illustration to show biogenesis of miRNAs and model of miRNA-mediated translational repression or mRNA degradation. MiRNA genes are transcribed by RNA polymerase II [2,90], resulting in the primary transcripts termed as pri-miRNAs, which are typically 60–70 nucleotides. The pri-miRNAs are processed by the enzyme Drosha to release the hairpin-shaped intermediates (pre-miRNAs) [3], followed by being exported into the cytoplasm by Exportin V and Ran-GTP cofactor [4–6], and then cleaved by the enzyme Dicer to yield miRNA/miRNA* duplexes [7–11].CC BY · PubMed Central
Fig 2 Illustration to show the 6 structure statuses of paired nucleic acid residues. Note that the nucleotide near 5’ end is different with the one near 3’end: (a) the base pairs A-U or U-A has 2 hydrogen bonds; (b) the base pair G-C or C-G has 3 hydrogen bonds; and (c) the wobble base pair G-U or U-G has 2 weaker hydrogen bonds. See the main text for further explanation.
Fig 2 Illustration to show the 6 structure statuses of paired nucleic acid residues. Note that the nucleotide near 5’ end is different with the one near 3’end: (a) the base pairs A-U or U-A has 2 hydrogen bonds; (b) the base pair G-C or C-G has 3 hydrogen bonds; and (c) the wobble base pair G-U or U-G has 2 weaker hydrogen bonds. See the main text for further explanation.CC BY · PubMed Central
Fig 3 A flowchart to show the process of generating the feature vector for a RNA sequence by its structure status composition. Given a RNA sequence R (cf. Equation 2), its secondary structure sequence was derived from Vienna RNA software package, as formulated in Equation 4. According to the definition in that package, there are two types of status for each of the nucleotides: unpaired or paired. The former is denoted by a dot “.” and the latter by the symbol “(“or “)”. The left bracket “(“stands for a nucleotide near the 5'-end while the right bracket for the one near the 3'-end. Since the number of different structure elements in the RNA sequence thus obtained is 10 (cf. Equation 5), its n-tuple element composition will contain 10ncomponents (cf. Equation 6). For simplicity, however, shown here is only for the case of n = 2; i.e., the 2-tuple element composition that contains 102 = 100 components formed by different pairs of the most contiguous secondary structure status elements.
Fig 3 A flowchart to show the process of generating the feature vector for a RNA sequence by its structure status composition. Given a RNA sequence R (cf. Equation 2), its secondary structure sequence was derived from Vienna RNA software package, as formulated in Equation 4. According to the definition in that package, there are two types of status for each of the nucleotides: unpaired or paired. The former is denoted by a dot “.” and the latter by the symbol “(“or “)”. The left bracket “(“stands for a nucleotide near the 5'-end while the right bracket for the one near the 3'-end. Since the number of different structure elements in the RNA sequence thus obtained is 10 (cf. Equation 5), its n-tuple element composition will contain 10ncomponents (cf. Equation 6). For simplicity, however, shown here is only for the case of n = 2; i.e., the 2-tuple element composition that contains 102 = 100 components formed by different pairs of the most contiguous secondary structure status elements.CC BY · PubMed Central
Fig 4 A schematic illustration to show the correlation of structure statuses along a RNA sequence. (a) The first-tier correlation reflects the structure-order mode between all the most contiguous nucleotides. (b) The 2nd-tier correlation reflects the structure-order mode between all the second-most contiguous nucleotides. (c) The 3rd-tier correlation reflects the structure-order mode between all the third-most contiguous nucleotides. As we can see, the global or long-range sequence order information of RNA can thus be approximately and indirectly incorporated into the current prediction model as done by the PseAAC approach for proteins [30].
Fig 4 A schematic illustration to show the correlation of structure statuses along a RNA sequence. (a) The first-tier correlation reflects the structure-order mode between all the most contiguous nucleotides. (b) The 2nd-tier correlation reflects the structure-order mode between all the second-most contiguous nucleotides. (c) The 3rd-tier correlation reflects the structure-order mode between all the third-most contiguous nucleotides. As we can see, the global or long-range sequence order information of RNA can thus be approximately and indirectly incorporated into the current prediction model as done by the PseAAC approach for proteins [30].CC BY · PubMed Central
Fig 5 A graphical illustration to show the performance of different methods by means of the receiver operating characteristic (ROC) curves. The areas under the ROC curves, or AUC are 0.93, 0.96, 0.90, and 0.94 for iMcRNA-PseSSC, iMcRNA-ExPseSSC, Triplet-SVM, and MiPred, respectively. See section “Comparison with Other Methods” for further explanation.
Fig 5 A graphical illustration to show the performance of different methods by means of the receiver operating characteristic (ROC) curves. The areas under the ROC curves, or AUC are 0.93, 0.96, 0.90, and 0.94 for iMcRNA-PseSSC, iMcRNA-ExPseSSC, Triplet-SVM, and MiPred, respectively. See section “Comparison with Other Methods” for further explanation.CC BY · PubMed Central
Fig 6 Visualizing the discriminative power with a heat map. (a) The discriminative power of the 100 local structure status compositions. The structure statuses marked on the vertical and horizontal axes indicate the first structure status and the second structure status in the local structure status compositions. (b) The discriminative power of the 13 features incorporating the structure-order effect. The λ values are marked on horizontal axis.
Fig 6 Visualizing the discriminative power with a heat map. (a) The discriminative power of the 100 local structure status compositions. The structure statuses marked on the vertical and horizontal axes indicate the first structure status and the second structure status in the local structure status compositions. (b) The discriminative power of the 13 features incorporating the structure-order effect. The λ values are marked on horizontal axis.CC BY · PubMed Central
Fig 7 A semi-screenshot to show the top page of the web-server iMcRNA. Its website address is at http://bioinformatics.hitsz.edu.cn/iMcRNA/.
Fig 7 A semi-screenshot to show the top page of the web-server iMcRNA. Its website address is at http://bioinformatics.hitsz.edu.cn/iMcRNA/.CC BY · PubMed Central
Fig 8 A semi-screenshot to show the output obtained by the web-server. See the text for further explanation.
Fig 8 A semi-screenshot to show the output obtained by the web-server. See the text for further explanation.CC BY · PubMed Central

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  1. SX Lin, 2013, Theoretical and experimental biology in one, J Biomedical Science and Engineering, 6, 435

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Metadata

Authors
Liu, B., Fang, L., Liu, F., Wang, X., Chen, J., & Chou, K.-C.
Affiliations
  • Harbin Institute of Technology (CN)
  • King Abdulaziz University (SA)
Corresponding author
Liu, Bin
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2015-03-30
Volume
10
Issue
3
Pages
e0121501
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0121501
PMC
PMC4378912
OpenAlex
W2001168095
Cited by
238
References
93
Citations by year
2026: 1 · 2025: 4 · 2024: 1 · 2023: 3 · 2022: 6 · 2021: 9 · 2020: 19 · 2019: 29 · 2018: 46 · 2017: 26 · 2016: 48 · 2015: 46
FWCI
24.01
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (HIT.NSRIF.2013103, 61272383, 61300112)
  • Harbin Institute of Technology
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
Keywords
pseudo amino acid composition, RNA secondary structure, human miRNAs, sequence-based prediction, benchmark dataset
MeSH terms
Amino Acids, Base Sequence, Humans, Nucleic Acid Conformation, Nucleotides, RNA Precursors, Genome, Human, Computational Biology, MicroRNAs, RNA, Small Untranslated

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