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RNA-seq

RNA-seq

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 24, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

RNA-clique: a method for computing genetic distances from RNA-seq data.

Andrew C Tapia¹, Jerzy W Jaromczyk², Neil Moore²

¹Department of Computer Science, University of Kentucky, 329 Rose St, Lexington, KY, 40508, USA. andrew.tapia@uky.edu.

BMC Bioinformatics

|June 4, 2024

Summary

This summary is machine-generated.

RNA-clique is a new computational method that calculates genetic distances from RNA-sequencing (RNA-seq) data. This approach integrates functional and genetic diversity studies by reliably distinguishing genotypes and individuals across species.

Keywords:

Genetic distance Graph algorithms Phylogenetics RNA-seq

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Last Updated: Jun 24, 2025

Identification of Circular RNAs using RNA Sequencing

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Published on: November 14, 2019

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Published on: February 2, 2024

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

RNA-sequencing (RNA-seq) data traditionally quantifies gene expression.
Challenges exist in using mRNA sequences for genetic distance computation due to conserved coding regions and gene duplication.
Integrating RNA-seq for genetic distance offers a novel approach to study genetic diversity.

Purpose of the Study:

To develop and assess a new computational method, RNA-clique, for calculating genetic distances from RNA-seq data.
To evaluate the method's efficacy using both biological and simulated datasets.
To demonstrate the integration of functional and genetic diversity studies.

Main Methods:

Developed RNA-clique, a graph-based computational method using reciprocal BLASTn.
Filtered for orthologous genes by constructing graphs where vertices represent genes and edges represent best reciprocal matches.
Computed genetic distances based on BLAST alignment statistics and graph components containing orthologous genes.

Main Results:

RNA-clique successfully distinguished individual genotypes in tall fescue (Lolium arundinaceum) and individuals in bluehead wrasse (Thalassoma bifasciatum).
Simulated RNA-seq data analysis accurately recovered the ground truth phylogeny.
The method retained sufficient data for distance computation even with stringent ortholog filtering, showing high reliability, especially for complex genomes.

Conclusions:

RNA-clique effectively derives genetic distances from RNA-seq data.
This method provides a valuable tool for integrating functional and genetic diversity analyses.
RNA-clique demonstrates robust performance across different species and data types.