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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Related Experiment Video

Updated: Jun 18, 2025

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PLEKv2: predicting lncRNAs and mRNAs based on intrinsic sequence features and the coding-net model.

Aimin Li1, Haotian Zhou2, Siqi Xiong3

  • 1Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, 710048, China. liaiminmail@gmail.com.

BMC Genomics
|August 2, 2024
PubMed
Summary
This summary is machine-generated.

The upgraded PLEK v2 tool accurately distinguishes long non-coding RNAs (lncRNAs) from messenger RNAs (mRNAs) in animals and plants. This new model shows high prediction accuracy across species, improving upon existing methods for RNA classification.

Keywords:
Coding-netDeep learningPLEKlncRNAs

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Long non-coding RNAs (lncRNAs) are RNA transcripts over 200 nucleotides that do not encode proteins.
  • lncRNAs share structural similarities with messenger RNAs (mRNAs).
  • Accurate differentiation between lncRNA and mRNA is crucial for biological research.

Purpose of the Study:

  • To enhance the accuracy and efficiency of distinguishing lncRNAs from mRNAs.
  • To develop an improved computational tool for RNA classification.
  • To create models applicable to both animal and plant species.

Main Methods:

  • Upgraded the PLEK alignment-free tool to PLEKv2.
  • Developed tailored models for animal and plant RNA sequence analysis.
  • Evaluated PLEKv2 performance against existing classification tools.

Main Results:

  • PLEKv2 achieved 98.7% prediction accuracy on human datasets, outperforming other methods.
  • Demonstrated >90% accuracy for cross-species prediction, including primates and plants like Arabidopsis.
  • Showed superior effectiveness and robustness compared to CPC2, CNCI, LncADeep, PLEK, and NcResNet.

Conclusions:

  • PLEKv2 significantly improves the ability to distinguish lncRNAs from mRNAs compared to its predecessor, PLEK.
  • The PLEKv2 software is publicly available for research use.
  • The tool offers robust RNA classification capabilities across diverse species.