In our original article published in Nucleic Acids Research (1), we developed a machine learning algorithm for identifying driver genes (i.e., DriverML), and benchmarked it with 20 other existing tools (including ExInAtor) in 31 TCGA exome mutation datasets. It was brought to our attention by Dr. Rory Johnson, the developer of ExInAtor (University of Bern, Switzerland), that the use of whole-genome sequencing data is critical for the ExInAtor as mutations in intronic and intergenic regions are required to estimate background mutation rates. In our article, the exome mutation data were used for ExInAtor analysis, which resulted in a significant underestimation of background mutation rates. The results of ExinAtor analysis presented in our article didn’t represent its optimal performance. The requirement to use only whole-genome SNVs has now been clarified in the ExInAtor Github page (https://github.com/alanzos/ExInAtor/). The corrections do not affect the conclusions of the article. We deeply regret this oversight and any confusions over this issue.
Contributor Information
Yi Han, Center for Uterine Cancer Diagnosis and Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, China.
Juze Yang, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China.
Xinyi Qian, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China.
Wei-Chung Cheng, Graduate Institute of Biomedical Sciences, Research Center for Tumor Medical Science, and Drug Development Center, China Medical University, Taichung 40402, Taiwan.
Shu-Hsuan Liu, Graduate Institute of Biomedical Sciences, Research Center for Tumor Medical Science, and Drug Development Center, China Medical University, Taichung 40402, Taiwan.
Xing Hua, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA.
Liyuan Zhou, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China.
Yaning Yang, Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui 230026, China.
Qingbiao Wu, Department of Mathematics, Zhejiang University, Hangzhou, Zhejiang 310027, China.
Pengyuan Liu, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China.
Yan Lu, Center for Uterine Cancer Diagnosis and Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, China.
REFERENCES
- 1. Han Y., Yang J., Qian X., Cheng W.C., Liu S.H., Hua X., Zhou L., Yang Y., Wu Q., Liu P.et al.. DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies. Nucleic Acids Res. 2019; 47:e45.doi: 10.1093/nar/gkz096. [DOI] [PMC free article] [PubMed] [Google Scholar]