Title |
FusionCancer: a database of cancer fusion genes derived from RNA-seq data
|
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Published in |
Diagnostic Pathology, July 2015
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DOI | 10.1186/s13000-015-0310-4 |
Pubmed ID | |
Authors |
Yunjin Wang, Nan Wu, Jiaqi Liu, Zhihong Wu, Dong Dong |
Abstract |
Fusion genes are chimeric results originated from previous separate genes with aberrant functions. The resulting protein products may lead to abnormal status of expression levels, functions and action sites, which in return may cause the abnormal proliferation of cells and cancer development. With the emergence of next-generation sequencing technology, RNA-seq has spurred gene fusion discovery in various cancer types. In this work, we compiled 591 recently published RNA-seq datasets in 15 kinds of human cancer, and the gene fusion events were comprehensively identified. Based on the results, a database was developed for gene fusion in cancers (FusionCancer), with the attempt to provide a user-friendly utility for the cancer research community. A flexible query engine has been developed for the acquisition of annotated information of cancer fusion genes, which would help users to determine the chimera events leading to functional changes. FusionCancer can be accessible at the following hyperlink website: http://donglab.ecnu.edu.cn/databases/FusionCancer/ To the best of our knowledge, FusionCancer is the first comprehensive fusion gene database derived only from cancer RNA-seq data. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 2 | 18% |
Brazil | 1 | 9% |
Unknown | 8 | 73% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 6 | 55% |
Scientists | 4 | 36% |
Practitioners (doctors, other healthcare professionals) | 1 | 9% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Germany | 1 | 1% |
France | 1 | 1% |
United Kingdom | 1 | 1% |
Denmark | 1 | 1% |
Japan | 1 | 1% |
United States | 1 | 1% |
Luxembourg | 1 | 1% |
Unknown | 69 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 18 | 24% |
Student > Ph. D. Student | 15 | 20% |
Student > Doctoral Student | 8 | 11% |
Student > Master | 6 | 8% |
Student > Bachelor | 5 | 7% |
Other | 13 | 17% |
Unknown | 11 | 14% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 33 | 43% |
Biochemistry, Genetics and Molecular Biology | 19 | 25% |
Medicine and Dentistry | 9 | 12% |
Computer Science | 2 | 3% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 1% |
Other | 1 | 1% |
Unknown | 11 | 14% |