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Published on: July 5, 2019
GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data
Jian Zhao1, Qi Chen1, Jing Wu1
1Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
Abstract:
Fusion gene derived from genomic rearrangement plays a key role in cancer initiation. The discovery of novel gene fusions may be of significant importance in cancer diagnosis and treatment. Meanwhile, next generation sequencing technology provide a sensitive and efficient way to identify gene fusions in genomic levels. However, there are still many challenges and limitations remaining in the existing methods which only rely on unmapped reads or discordant alignment fragments. In this work we have developed GFusion, a novel method using RNA-Seq data, to identify the fusion genes. This pipeline performs multiple alignments and strict filtering algorithm to improve sensitivity and reduce the false positive rate. GFusion successfully detected 34 from 43 previously reported fusions in four cancer datasets. We also demonstrated the effectiveness of GFusion using 24 million 76 bp paired-end reads simulation data which contains 42 artificial fusion genes, among which GFusion successfully discovered 37 fusion genes. Compared with existing methods, GFusion presented higher sensitivity and lower false positive rate. The GFusion pipeline can be accessed freely for non-commercial purposes at: https://github.com/xiaofengsong/GFusion .
Insights
GFusion, a novel RNA-Seq method, accurately identifies cancer-causing fusion genes. This tool offers higher sensitivity and fewer false positives than existing methods for cancer diagnosis and treatment.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Genomic rearrangements leading to fusion genes are crucial in cancer initiation.
- Identifying novel gene fusions is vital for accurate cancer diagnosis and effective treatment strategies.
- Next-generation sequencing (NGS) offers powerful tools for fusion gene detection, but current methods face limitations.
Purpose of the Study:
- To develop GFusion, a novel computational method utilizing RNA-Seq data for sensitive and accurate identification of fusion genes.
- To enhance the accuracy of fusion gene detection by employing multiple alignments and a strict filtering algorithm.
- To provide a freely accessible tool for non-commercial research purposes.
Main Methods:
- Development of the GFusion pipeline utilizing RNA-Seq data.
- Implementation of multiple alignment strategies and a stringent filtering algorithm.
- Validation using both real cancer datasets and simulated genomic data.
Main Results:
- GFusion successfully identified 34 out of 43 known fusion genes in four cancer datasets.
- In simulation data, GFusion detected 37 out of 42 artificial fusion genes.
- GFusion demonstrated superior sensitivity and a reduced false positive rate compared to existing methods.
Conclusions:
- GFusion is an effective and reliable method for identifying fusion genes from RNA-Seq data.
- The GFusion pipeline offers improved performance in terms of sensitivity and specificity for cancer gene fusion detection.
- GFusion provides a valuable resource for cancer research, diagnosis, and the development of targeted therapies.
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