C: Consensus Cancer Driver Gene Caller.
10.1016/j.gpb.2018.10.004
- Author:
Chen-Yu ZHU
1
;
Chi ZHOU
1
;
Yun-Qin CHEN
2
;
Ai-Zong SHEN
3
;
Zong-Ming GUO
2
;
Zhao-Yi YANG
3
;
Xiang-Yun YE
4
;
Shen QU
5
;
Jia WEI
6
;
Qi LIU
7
,
8
Author Information
1. Department of Endocrinology & Metabolism, Shanghai Tenth People's Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
2. R&D Information, Innovation Center China, AstraZeneca, Shanghai 201203, China.
3. Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China, Hefei 230036, China.
4. Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200240, China. Electronic address: 56485747@qq.com.
5. Department of Endocrinology & Metabolism, Shanghai Tenth People's Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China. Electronic address: qushencn@tongji.edu.cn.
6. R&D Information, Innovation Center China, AstraZeneca, Shanghai 201203, China. Electronic address: Jenny.Wei@astrazeneca.com.
7. Department of Endocrinology & Metabolism, Shanghai Tenth People's Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
8. Department of Ophthalmology, Ninghai First Hospital, Ninghai 315600, China. Electronic address: qiliu@tongji.edu.cn.
- Publication Type:Journal Article
- Keywords:
Cancer driver genes;
Consensus;
Data integration;
Somatic mutation;
Web server
- From:
Genomics, Proteomics & Bioinformatics
2019;17(3):311-318
- CountryChina
- Language:English
-
Abstract:
Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells. A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations. To address this issue, we present the first web-based application, consensus cancer driver gene caller (C), to identify the consensus driver genes using six different complementary strategies, i.e., frequency-based, machine learning-based, functional bias-based, clustering-based, statistics model-based, and network-based strategies. This application allows users to specify customized operations when calling driver genes, and provides solid statistical evaluations and interpretable visualizations on the integration results. C is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3.
