Title |
SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data
|
---|---|
Published in |
Genome Biology, February 2013
|
DOI | 10.1186/gb-2013-14-2-r12 |
Pubmed ID | |
Authors |
Wenlong Jia, Kunlong Qiu, Minghui He, Pengfei Song, Quan Zhou, Feng Zhou, Yuan Yu, Dandan Zhu, Michael L Nickerson, Shengqing Wan, Xiangke Liao, Xiaoqian Zhu, Shaoliang Peng, Yingrui Li, Jun Wang, Guangwu Guo |
Abstract |
We have developed a new method, SOAPfuse, to identify fusion transcripts from paired-end RNA-Seq data. SOAPfuse applies an improved partial exhaustion algorithm to construct a library of fusion junction sequences, which can be used to efficiently identify fusion events, and employs a series of filters to nominate high-confidence fusion transcripts. Compared with other released tools, SOAPfuse achieves higher detection efficiency and consumed less computing resources. We applied SOAPfuse to RNA-Seq data from two bladder cancer cell lines, and confirmed 15 fusion transcripts, including several novel events common to both cell lines. SOAPfuse is available at http://soap.genomics.org.cn/soapfuse.html. |
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India | 2 | 13% |
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France | 2 | 13% |
China | 1 | 6% |
Canada | 1 | 6% |
Germany | 1 | 6% |
Unknown | 4 | 25% |
Demographic breakdown
Type | Count | As % |
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Scientists | 10 | 63% |
Members of the public | 4 | 25% |
Science communicators (journalists, bloggers, editors) | 2 | 13% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
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United States | 3 | 1% |
Germany | 2 | <1% |
Norway | 2 | <1% |
Sweden | 1 | <1% |
Singapore | 1 | <1% |
Korea, Republic of | 1 | <1% |
Belgium | 1 | <1% |
Argentina | 1 | <1% |
Other | 2 | <1% |
Unknown | 190 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 56 | 27% |
Student > Ph. D. Student | 44 | 21% |
Student > Master | 25 | 12% |
Student > Bachelor | 15 | 7% |
Student > Doctoral Student | 9 | 4% |
Other | 30 | 14% |
Unknown | 30 | 14% |
Readers by discipline | Count | As % |
---|---|---|
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Biochemistry, Genetics and Molecular Biology | 52 | 25% |
Computer Science | 23 | 11% |
Medicine and Dentistry | 12 | 6% |
Engineering | 2 | <1% |
Other | 7 | 3% |
Unknown | 31 | 15% |