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Xu H, Wu X, Sun D, Li S, Zhang S, Teng M, Bu J, Zhang X, Meng B, Wang W, Tian G, Lin H, Yuan D, Lang J, Xu S. SEGF: A Novel Method for Gene Fusion Detection from Single-End Next-Generation Sequencing Data. Genes (Basel) 2018; 9:genes9070331. [PMID: 30004447 PMCID: PMC6070977 DOI: 10.3390/genes9070331] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 06/21/2018] [Accepted: 06/27/2018] [Indexed: 11/23/2022] Open
Abstract
With the development and application of next-generation sequencing (NGS) and target capture technology, the demand for an effective analysis method to accurately detect gene fusion from high-throughput data is growing. Hence, we developed a novel fusion gene analyzing method called single-end gene fusion (SEGF) by starting with single-end DNA-seq data. This approach takes raw sequencing data as input, and integrates the commonly used alignment approach basic local alignment search tool (BLAST) and short oligonucleotide analysis package (SOAP) with stringent passing filters to achieve successful fusion gene detection. To evaluate SEGF, we compared it with four other fusion gene discovery analysis methods by analyzing sequencing results of 23 standard DNA samples and DNA extracted from 286 lung cancer formalin fixed paraffin embedded (FFPE) samples. The results generated by SEGF indicated that it not only detected the fusion genes from standard samples and clinical samples, but also had the highest accuracy and sensitivity among the five compared methods. In addition, SEGF was capable of detecting complex gene fusion types from single-end NGS sequencing data compared with other methods. By using SEGF to acquire gene fusion information at DNA level, more useful information can be retrieved from the DNA panel or other DNA sequencing methods without generating RNA sequencing information to benefit clinical diagnosis or medication instruction. It was a timely and cost-effective measure with regard to research or diagnosis. Considering all the above, SEGF is a straightforward method without manipulating complicated arguments, providing a useful approach for the precise detection of gene fusion variation.
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Affiliation(s)
- Hai Xu
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin 150049, China.
| | - Xiaojin Wu
- Department of Radiation Oncology, The First People's Hospital of Xuzhou, Xuzhou 221002, China.
| | - Dawei Sun
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin 150049, China.
| | - Shijun Li
- Department of Pathology, Chifeng Municiple Hospital, Chifeng 024000, China.
| | - Siwen Zhang
- Geneis Beijing Co., Ltd., Beijing 100102, China.
| | - Miao Teng
- Geneis Beijing Co., Ltd., Beijing 100102, China.
| | - Jianlong Bu
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin 150049, China.
| | - Xizhe Zhang
- Department of Anesthesiology, Chifeng Municiple Hospital, Chifeng 024000, China.
| | - Bo Meng
- Geneis Beijing Co., Ltd., Beijing 100102, China.
| | - Weitao Wang
- Geneis Beijing Co., Ltd., Beijing 100102, China.
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing 100102, China.
| | - Huixin Lin
- Geneis Beijing Co., Ltd., Beijing 100102, China.
| | - Dawei Yuan
- Geneis Beijing Co., Ltd., Beijing 100102, China.
| | - Jidong Lang
- Geneis Beijing Co., Ltd., Beijing 100102, China.
| | - Shidong Xu
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin 150049, China.
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