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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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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Updated: Jun 13, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Comparison and benchmark of deep learning methods for non-coding RNA classification.

Constance Creux1,2, Farida Zehraoui1, François Radvanyi2

  • 1Université Paris-Saclay, Univ Evry, IBISC, Evry-Courcouronnes, France.

Plos Computational Biology
|September 12, 2024
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Summary
This summary is machine-generated.

This study compares deep learning methods for classifying non-coding RNAs (ncRNAs). It provides benchmarks and recommendations for future ncRNA functional classification tools.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Non-coding RNAs (ncRNAs) play vital roles in biological processes and diseases.
  • Understanding ncRNA function is crucial, but most ncRNAs remain uncharacterized.
  • Rapid classification methods are needed for large sets of ncRNAs.

Purpose of the Study:

  • To provide an exhaustive comparison of state-of-the-art deep learning methods for ncRNA classification.
  • To establish objective benchmarks for evaluating ncRNA classification tools.
  • To assess the impact of architectural choices on performance, robustness, and computational efficiency.

Main Methods:

  • Systematic review and comparison of existing deep learning architectures for ncRNA classification.
  • Experimental evaluation of various tools on popular datasets.
  • Analysis of method robustness against non-functional sequences and noise.
  • Measurement of computation time and CO2 emissions.

Main Results:

  • Deep learning methods show promise for ncRNA classification.
  • Performance varies significantly across different architectures and datasets.
  • Robustness to noise and computational efficiency are key considerations.
  • Specific architectural choices impact performance and resource utilization.

Conclusions:

  • Recommendations are provided for future ncRNA classification method development.
  • Objective benchmarks are essential for fair tool evaluation.
  • Further research should focus on improving robustness and efficiency while considering environmental impact.