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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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Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
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The eukaryotic promoter region is a segment of DNA located upstream of a gene. It contains an RNA polymerase binding site, a transcription start site, and several cis-regulatory sequences.  The proximal promoter region is located in the vicinity of the gene and has cis-regulatory sequences and the core promoter. The core promoter is the binding site for RNA polymerase and is usually located between -35 and +35 nucleotides from the transcription start site. The distal promoter regions are...
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DeepLncPro: an interpretable convolutional neural network model for identifying long non-coding RNA promoters.

Tianyang Zhang1, Qiang Tang2, Fulei Nie1

  • 1School of Life Sciences, North China University of Science and Technology.

Briefings in Bioinformatics
|October 9, 2022
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Summary

DeepLncPro, a novel convolutional neural network model, accurately identifies long non-coding RNA (lncRNA) promoters in humans and mice. This interpretable tool aids in understanding lncRNA regulation and offers superior performance over existing methods.

Keywords:
convolution neural networklong non-coding RNAmodel interpretabilitypromotertranscription factors

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Long non-coding RNAs (lncRNAs) are crucial regulators of biological processes.
  • Understanding lncRNA transcription requires accurate promoter identification.
  • Current experimental methods for genome-wide promoter identification are time-consuming.

Purpose of the Study:

  • To develop an efficient computational tool for identifying lncRNA promoters.
  • To improve the accuracy of lncRNA promoter prediction compared to existing methods.
  • To provide an interpretable model for analyzing transcription factor binding motifs in lncRNAs.

Main Methods:

  • Development of DeepLncPro, a convolutional neural network (CNN)-based model.
  • Application of DeepLncPro for lncRNA promoter identification in human and mouse genomes.
  • Comparative analysis of DeepLncPro against state-of-the-art machine learning methods.

Main Results:

  • DeepLncPro demonstrated superior performance in identifying lncRNA promoters.
  • The model outperformed existing computational methods for lncRNA promoter prediction.
  • DeepLncPro successfully extracted and analyzed transcription factor binding motifs, enhancing model interpretability.

Conclusions:

  • DeepLncPro serves as a powerful and accurate tool for lncRNA promoter identification.
  • The model facilitates a deeper understanding of lncRNA regulatory mechanisms.
  • An open-source version of DeepLncPro is available for broader research application.