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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

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.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jun 13, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Comparison and benchmark of deep learning methods for non-coding RNA classification.

Constance Creux^1,2, Farida Zehraoui¹, François Radvanyi²

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

Plos Computational Biology

|September 12, 2024

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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Published on: September 25, 2021

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.