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RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

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A Methodology to Study Pseudogenized lincRNAs.

Sweta Talyan^1,2, Miguel A Andrade-Navarro³, Enrique M Muro⁴

¹Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Heidelberg, Germany.

Methods in Molecular Biology (Clifton, N.J.)

|June 24, 2021

Summary

This summary is machine-generated.

Researchers developed a computational method to identify protein-coding sequence remnants within long noncoding RNAs (lincRNAs). This tool aids in studying lincRNA evolution and their potential pseudogenization from protein-coding genes.

Keywords:

Protein homology Protein-coding gene Pseudogene Rost curve Sequence alignment ceRNA lincRNA gene lncRNA gene microRNA

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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

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Published on: July 9, 2021

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Long intergenic noncoding RNAs (lincRNAs) are key regulators of gene expression and can function as competing endogenous RNAs (ceRNAs).
Some lincRNAs exhibit sequence similarities to protein-coding genes, suggesting a possible origin through pseudogenization.
Studying this phenomenon is hindered by the lack of specialized computational tools for analyzing noncoding and protein-coding sequences together.

Purpose of the Study:

To present a novel computational method for detecting remnants of protein-coding sequences within lincRNAs.
To identify corresponding sequences in parental proteins for evolutionary analysis.
To introduce a visualization platform for analyzing sequence variations like frameshifts and point mutations.

Main Methods:

Development of a computational method to align and analyze protein-coding and noncoding sequences.
Application of the method to identify protein-coding sequence fragments within lincRNAs.
Creation of a visualization tool to trace mutations (frameshifts, point mutations) in these sequences.

Main Results:

The developed method successfully identifies protein-coding sequence remnants in lincRNAs.
The tool facilitates the mapping of these remnants to their parental protein sequences.
The visualization platform aids in understanding the evolutionary alterations within these sequences.

Conclusions:

The new computational method overcomes limitations in studying lincRNA evolution and pseudogenization.
This approach enables deeper insights into the relationship between lincRNAs and protein-coding genes.
The toolset supports the investigation of sequence divergence and functional implications in lincRNA evolution.