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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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LncRNA Subcellular Localization Across Diverse Cell Lines: An Exploration Using Deep Learning with Inexact q-mers.

Weijun Yi¹, Jason R Miller², Gangqing Hu³

¹Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.

|July 23, 2025

Summary

Predicting long non-coding RNA (lncRNA) localization is improved using inexact sequence profiles. Most lncRNAs localize consistently across cell types, but some

Keywords:

1D-CNN inexact q-mers lncRNA localization switching genes

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Long non-coding RNAs (lncRNAs) exhibit diverse subcellular localizations (nuclear, cytoplasmic) impacting their functions.
Limited annotations exist for lncRNA subcellular localization, necessitating predictive models.
Leveraging annotated lncRNAs can aid in predicting localization for unannotated ones.

Purpose of the Study:

To enhance lncRNA subcellular localization prediction using inexact sequence profiles.
To investigate the cell-type specificity of lncRNA localization.
To identify and characterize 'switching' lncRNAs that alter localization across cell types.

Main Methods:

Utilized machine learning and deep learning techniques with inexact q-mer profiles.
Developed predictive models for lncRNA subcellular localization.
Analyzed lncRNA localization data across 15 distinct cell lines.

Main Results:

Inexact q-mers (q=6) significantly improved lncRNA localization prediction accuracy over exact q-mers.
lncRNA localization is generally not cell-line specific.
Identified 'switching' lncRNAs that change cellular compartments between cell lines, complicating prediction.

Conclusions:

Inexact q-mer profiles offer a more robust approach for lncRNA localization prediction.
While generally conserved, a subset of lncRNAs exhibit dynamic localization, posing a challenge for predictive models.
Accurate lncRNA subcellular localization prediction remains a significant area of research.