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RNA Secondary Structure Prediction Using High-throughput SHAPE
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Predicting functional long non-coding RNAs validated by low throughput experiments.

Bailing Zhou1,2, Yuedong Yang1,3,4, Jian Zhan4

  • 1Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University , Dezhou , China.

RNA Biology
|July 27, 2019
PubMed
Summary

A new computational method distinguishes functional long non-coding RNAs (lncRNAs) from non-functional ones. This approach prioritizes lncRNAs for experimental validation, accelerating the discovery of novel, biologically significant molecules.

Keywords:
Long non-coding RNAsfunctional lncRNAslow throughput experimentsprediction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • High-throughput screening has identified numerous long non-coding RNAs (lncRNAs), but only a small subset has experimentally validated functions (EVlncRNAs).
  • Distinguishing functional lncRNAs from the vast number of high-throughput lncRNAs (HTlncRNAs) and messenger RNAs (mRNAs) remains a significant challenge.

Purpose of the Study:

  • To develop and validate a computational method for accurately classifying experimentally validated lncRNAs (EVlncRNAs) from high-throughput lncRNAs (HTlncRNAs) and messenger RNAs (mRNAs).
  • To identify key sequence features that differentiate functional lncRNAs from non-functional ones and mRNAs.
  • To assess the cross-species applicability and utility of the developed method for prioritizing lncRNAs for functional studies.

Main Methods:

  • Development of a Support Vector Machine (SVM) model using sequence conservation features at RNA and protein levels.
  • Training and independent testing on human RNA datasets to evaluate classification performance (Matthews correlation coefficient, sensitivity, precision).
  • Cross-species validation using mouse and plant RNA datasets, and application to a large set of human HTlncRNAs.

Main Results:

  • The SVM model achieved high accuracy in distinguishing EVlncRNAs from HTlncRNAs and mRNAs, with a Matthews correlation coefficient of 0.6, 64% sensitivity, and 81% precision on the human test set.
  • Sequence conservation at the RNA level was crucial for separating EVlncRNAs from HTlncRNAs, while protein-level conservation aided separation from mRNAs.
  • The model demonstrated robustness across species, showing similar accuracy for mouse RNAs and applicability to plant RNAs, and successfully identified potential functional lncRNAs within a large HTlncRNA set.

Conclusions:

  • The developed computational method effectively differentiates functional lncRNAs from non-functional ones and mRNAs, utilizing sequence conservation as key features.
  • The method is robust and applicable across different species, offering a valuable tool for prioritizing lncRNAs for experimental validation.
  • This approach significantly accelerates the discovery of novel functional lncRNAs by reducing the cost and effort of experimental screening, highlighting a substantial pool of undiscovered lncRNA functions.