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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

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...

RNA-seq

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Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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RACE - Rapid Amplification of cDNA Ends

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Related Experiment Video

Updated: Apr 3, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.

Yiyan Zhou^1,2,3, Jiaheng Hou^1,2,3, Haoling Xie⁴

¹Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing 100871, China.

Bioinformatics (Oxford, England)

|April 2, 2026

Summary

This summary is machine-generated.

LncADeep 2.0 enhances long non-coding RNA (lncRNA) identification and functional annotation using deep learning. This tool improves accuracy by integrating novel features and leveraging interaction networks for better biological insights.

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Last Updated: Apr 3, 2026

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RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Long non-coding RNAs (lncRNAs) play vital roles in biological processes, but their functions are largely unknown.
Accurate identification and functional annotation of lncRNAs are crucial for understanding their roles.

Purpose of the Study:

To develop an advanced deep learning framework for improved lncRNA identification and functional annotation.
To provide a reliable tool for exploring lncRNA mechanisms in various biological contexts.

Main Methods:

Developed LncADeep 2.0, an integrated deep learning framework.
Incorporated novel peptide, sequence, and structural features for lncRNA identification.
Utilized lncRNA-centric interaction networks and transfer learning for functional annotation.

Main Results:

LncADeep 2.0 demonstrated superior performance in lncRNA identification compared to existing tools.
Achieved robust functional annotation, including prediction of tissue-specific functions from RNA-seq data.
Successfully linked tumor-associated lncRNAs to genomic markers.

Conclusions:

LncADeep 2.0 is an efficient and reliable tool for lncRNA identification and functional annotation.
The framework facilitates a deeper understanding of lncRNA functions across diverse biological processes.
Enables accurate analysis of lncRNA data from various sources, including single-cell RNA-seq.