RT Journal Article
SR Electronic
T1 DGAT-onco: A powerful method to detect oncogenes by integrating differential mutational analysis and functional impacts of somatic mutations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.02.15.947085
DO 10.1101/2020.02.15.947085
A1 Haoyang Zhang
A1 Junkang Wei
A1 Zifeng Liu
A1 Xun Liu
A1 Yutian Chong
A1 Yutong Lu
A1 Huiying Zhao
A1 Yuedong Yang
YR 2020
UL http://biorxiv.org/content/early/2020/02/16/2020.02.15.947085.abstract
AB Motivation Oncogenes are genes whose malfunctions play critical roles in cancer development, and their discovery is a major aim of cancer mechanisms study. By counting the mutation frequency, oncogenes have been identified with frequent mutations, while it is believed that many more oncogenes could be discovered by differential mutational profile analysis. However, it is common that current methods only utilize mutations in the cancer population, which have an obvious bias in background mutation modelling.Methods To predict oncogenes efficiently, we developed a method, DGAT-onco that analyzed the frequency distribution and functional impacts of mutations in both cancer and natural population. Our method can capture the mutational difference of two population, and provide a comprehensive view of genomics basis underlying cancer development. DGAT-onco was constructed by germline mutations from the 1000 Genomes project and somatic mutations of 33 cancer types from the Cancer Genome Atlas (TCGA) dataset. Its reliability was verified on an independent test set including 19 cancers from other sources.Results We demonstrated that our method is more effective than alternative methods in oncogenes discovering. Using this approach achieves higher classification performance in oncogene discovery than 6 alternative methods, and 22.8% significant genes identified by our method were verified as oncogenes by the Cancer Gene Census (CGC).Availability DGAT-onco is available at https://github.com/zhanghaoyang0/DGAT-onco.Contact yangyd25{at}mail.sysu.edu.cn or zhaohy8{at}mail.sysu.edu.cn