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

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Published on: July 5, 2019

iFUSE: integrated fusion gene explorer.

Saskia Hiltemann¹, Elizabeth A McClellan, Jos van Nijnatten

¹Department of Urology, Erasmus MC, 3015 GE Rotterdam, The Netherlands. s.hiltemann@erasmusmc.nl

Bioinformatics (Oxford, England)

|May 11, 2013

Summary

This summary is machine-generated.

iFUSE is a new tool for exploring structural variation data and identifying potential fusion genes. It analyzes genomic data to predict RNA and protein sequences for these fusion events.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Structural variations (SVs) in the genome can lead to the formation of fusion genes, which are implicated in various diseases.
Accurate identification and characterization of fusion genes are crucial for understanding disease mechanisms and developing targeted therapies.

Purpose of the Study:

To introduce iFUSE (integrated fusion gene explorer), a novel online visualization tool designed for the rapid and informative analysis of structural variation data.
To prioritize genomic breakpoints that are likely to represent fusion genes.
To annotate structural variation events with predicted RNA and protein sequences.

Main Methods:

iFUSE utilizes calculated breakpoints to identify fusion genes based on current genomic sequence annotations.
The tool accepts input from Complete Genomics (CG) junction files, FusionMap fusion detection reports, or previously analyzed iFUSE files.
Structural variation events are annotated with predicted RNA and protein sequences where applicable.

Main Results:

The effectiveness of iFUSE was demonstrated through case studies involving tumor-normal structural variation detection.
These case studies utilized whole-genome sequencing data generated by Complete Genomics.

Conclusions:

iFUSE provides a fast and informative method for visualizing structural variation data and prioritizing potential fusion genes.
The tool aids in the characterization of fusion genes by predicting associated RNA and protein sequences, facilitating further research and clinical applications.