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Updated: Mar 26, 2026

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

Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data.

Shailesh Kumar¹, Angie Duy Vo¹, Fujun Qin¹

¹Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908.

Scientific Reports

|February 11, 2016

Summary

This summary is machine-generated.

This study assessed 12 RNA-Seq fusion detection tools, finding performance varies by dataset. Tool selection should align with RNA sequencing data characteristics for accurate chimeric RNA identification.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Genomics and Bioinformatics
Molecular Biology
Computational Biology

Background:

RNA sequencing (RNA-Seq) enables global identification of fusion transcripts, also known as chimeric RNAs.
Numerous software packages exist for fusion detection, but their performance varies across datasets.
An unbiased assessment of these tools is crucial for users and developers.

Purpose of the Study:

To conduct an unbiased comparison of 12 prominent RNA-Seq fusion detection software packages.
To evaluate the performance metrics including sensitivity, false discovery rate, computational time, and memory usage.

Main Methods:

Comparative analysis of 12 fusion detection tools.
Evaluation across four distinct RNA-Seq datasets: positive, negative, mixed, and real-world test data.
Assessment of sensitivity, false discovery rate, processing time, and memory footprint.

Main Results:

Significant variations in performance (sensitivity, positive prediction value, time, memory) were observed among the evaluated tools.
Limited overlap in fusion detection was noted in the real dataset, suggesting potential false discoveries or lack of inclusivity.
Tool performance is demonstrably influenced by RNA-Seq data quality, read length, and read count.

Conclusions:

No single tool excels across all metrics; performance is dataset-dependent.
Users should carefully select fusion detection tools based on their specific RNA-Seq data properties.
Further development may be needed to improve inclusivity and reduce false discoveries in chimeric RNA identification.