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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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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

ChimeRScope: a novel alignment-free algorithm for fusion transcript prediction using paired-end RNA-Seq data.

You Li^1,2,3, Tayla B Heavican⁴, Neetha N Vellichirammal¹

¹Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA.

Nucleic Acids Research

|May 5, 2017

Summary

ChimeRScope, an alignment-free method, accurately identifies chimeric fusion transcripts using RNA-Seq data. This novel tool outperforms existing methods and discovers new oncogenic transcripts, improving cancer diagnosis and treatment strategies.

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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

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Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

Area of Science:

Genomics
Transcriptomics
Bioinformatics

Background:

RNA-sequencing (RNA-Seq) enables gene expression quantification and novel transcript discovery, including chimeric fusion transcripts.
Fusion transcripts are crucial biomarkers for cancer diagnosis, prognosis, and targeted therapy development.
Current fusion transcript detection relies on alignment-based software, which struggles with genomic alterations in cancer genomes, limiting accuracy.

Purpose of the Study:

To develop a novel, alignment-free method for accurate fusion transcript detection.
To overcome the limitations of alignment-based methods in complex cancer genomes.
To identify novel fusion transcripts with potential oncogenic functions.

Main Methods:

Developed ChimeRScope, an alignment-free tool utilizing gene "fingerprints" (k-mers) from RNA-Seq paired-end reads.
Applied ChimeRScope to published and in-house cancer datasets.
Validated findings through experimental methods.

Main Results:

ChimeRScope demonstrated superior performance compared to existing methods across various datasets, read lengths, and sequencing depths.
The tool successfully identified novel fusion transcripts with potential oncogenic roles in in-house datasets.
Experimental validation confirmed the accuracy and utility of ChimeRScope.

Conclusions:

ChimeRScope offers a robust and accurate alignment-free approach for fusion transcript detection.
The method enhances the identification of clinically relevant fusion transcripts, including novel ones.
ChimeRScope is a valuable tool for advancing cancer research and personalized medicine.