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  • Accurate prediction of genome-wide RNA secondary structure profile based on extreme gradient boosting

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    Accepted Manuscript (AM)
    Author(s)
    Ke, Y
    Rao, J
    Zhao, H
    Lu, Y
    Xiao, N
    Yang, Y
    Griffith University Author(s)
    Yang, Yuedong
    Year published
    2020
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    Abstract
    For permissions, please e-mail: journals.permissions@oup.com Motivation: RNA secondary structure plays a vital role in fundamental cellular processes, and identification of RNA secondary structure is a key step to understand RNA functions. Recently, a few experimental methods were developed to profile genome-wide RNA secondary structure, i.e. the pairing probability of each nucleotide, through high-throughput sequencing techniques. However, these high-throughput methods have low precision and cannot cover all nucleotides due to limited sequencing coverage. Results: Here, we have developed a new method for the prediction of ...
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    For permissions, please e-mail: journals.permissions@oup.com Motivation: RNA secondary structure plays a vital role in fundamental cellular processes, and identification of RNA secondary structure is a key step to understand RNA functions. Recently, a few experimental methods were developed to profile genome-wide RNA secondary structure, i.e. the pairing probability of each nucleotide, through high-throughput sequencing techniques. However, these high-throughput methods have low precision and cannot cover all nucleotides due to limited sequencing coverage. Results: Here, we have developed a new method for the prediction of genome-wide RNA secondary structure profile from RNA sequence based on the extreme gradient boosting technique. The method achieves predictions with areas under the receiver operating characteristic curve (AUC) >0.9 on three different datasets, and AUC of 0.888 by another independent test on the recently released Zika virus data. These AUCs are consistently >5% greater than those by the CROSS method recently developed based on a shallow neural network. Further analysis on the 1000 Genome Project data showed that our predicted unpaired probabilities are highly correlated (>0.8) with the minor allele frequencies at synonymous, non-synonymous mutations, and mutations in untranslated regions, which were higher than those generated by RNAplfold. Moreover, the prediction over all human mRNA indicated a consistent result with previous observation that there is a periodic distribution of unpaired probability on codons. The accurate predictions by our method indicate that such model trained on genome-wide experimental data might be an alternative for analytical methods. Availability and implementation: The GRASP is available for academic use at https://github.com/sysu-yanglab/ GRASP.
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    Journal Title
    Bioinformatics
    Volume
    36
    Issue
    17
    DOI
    https://doi.org/10.1093/bioinformatics/btaa534
    Copyright Statement
    © 2020 Oxford University Press. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Accurate prediction of genome-wide RNA secondary structure profile based on extreme gradient boosting, Bioinformatics, 2020, 36 (17), pp. 4576-4582 is available online at: https://doi.org/10.1093/bioinformatics/btaa534.
    Subject
    Mathematical sciences
    Biological sciences
    Publication URI
    http://hdl.handle.net/10072/400287
    Collection
    • Journal articles

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