DGAT-onco : A differential analysis method to detect oncogenes by integrating functional information of mutations
(2021) 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 In Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 p.793-796- Abstract
It is a common strategy to predict oncogenes by differential analysis between somatic mutations and background mutations. Most previous methods only utilize mutations in the cancer population to model its background mutation, which have an obvious bias. A recent method, DiffMut, improves this issue by conducting differential mutational analysis with both mutations in the cancer population and the natural population. However, it assumes the impacts of all mutations are equal, neglecting their functional difference. Thus, we developed a method, DGAT-onco that integrated the functional impacts of mutations to the differential mutational analysis framework of DiffMut. We performed DGAT-onco analysis with 33 cancer types from the Cancer... (More)
It is a common strategy to predict oncogenes by differential analysis between somatic mutations and background mutations. Most previous methods only utilize mutations in the cancer population to model its background mutation, which have an obvious bias. A recent method, DiffMut, improves this issue by conducting differential mutational analysis with both mutations in the cancer population and the natural population. However, it assumes the impacts of all mutations are equal, neglecting their functional difference. Thus, we developed a method, DGAT-onco that integrated the functional impacts of mutations to the differential mutational analysis framework of DiffMut. We performed DGAT-onco analysis with 33 cancer types from the Cancer Genome Atlas (TCGA) dataset. Its reliability was further evaluated on an independent test set including 22 cancers from other sources (TS22). Using oncogenes from the Cancer Gene Census (CGC) as the gold standard, our method achieves higher classification performance in oncogene discovery than five alternative methods (i.e., DiffMut, WITER, OncodriveCLUSTL, OncodriveFML, and MutSigCV) with an average AUPRC of 0.197 and 0.187 in TCGA and TS22 respectively. The source code and supplementary materials of DGAT-onco are available at https://github.com/zhanghaoyang0/DGAT-onco.
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- author
- Zhang, Haoyang LU ; Wei, Junkang ; Liu, Zifeng ; Liu, Xun ; Chong, Yutian ; Lu, Yutong ; Zhao, Huiying and Yang, Yuedong
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- keywords
- differential analysis, mutation, oncogene
- host publication
- Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
- series title
- Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
- editor
- Huang, Yufei ; Kurgan, Lukasz ; Luo, Feng ; Hu, Xiaohua Tony ; Chen, Yidong ; Dougherty, Edward ; Kloczkowski, Andrzej and Li, Yaohang
- pages
- 793 - 796
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
- conference location
- Virtual, Online, United States
- conference dates
- 2021-12-09 - 2021-12-12
- external identifiers
-
- scopus:85125177317
- ISBN
- 9781665401265
- DOI
- 10.1109/BIBM52615.2021.9669388
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2021 IEEE.
- id
- 057fc895-8fa6-4629-8529-26224b8bd07d
- date added to LUP
- 2024-02-05 14:58:24
- date last changed
- 2024-02-06 08:19:44
@inproceedings{057fc895-8fa6-4629-8529-26224b8bd07d, abstract = {{<p>It is a common strategy to predict oncogenes by differential analysis between somatic mutations and background mutations. Most previous methods only utilize mutations in the cancer population to model its background mutation, which have an obvious bias. A recent method, DiffMut, improves this issue by conducting differential mutational analysis with both mutations in the cancer population and the natural population. However, it assumes the impacts of all mutations are equal, neglecting their functional difference. Thus, we developed a method, DGAT-onco that integrated the functional impacts of mutations to the differential mutational analysis framework of DiffMut. We performed DGAT-onco analysis with 33 cancer types from the Cancer Genome Atlas (TCGA) dataset. Its reliability was further evaluated on an independent test set including 22 cancers from other sources (TS22). Using oncogenes from the Cancer Gene Census (CGC) as the gold standard, our method achieves higher classification performance in oncogene discovery than five alternative methods (i.e., DiffMut, WITER, OncodriveCLUSTL, OncodriveFML, and MutSigCV) with an average AUPRC of 0.197 and 0.187 in TCGA and TS22 respectively. The source code and supplementary materials of DGAT-onco are available at https://github.com/zhanghaoyang0/DGAT-onco.</p>}}, author = {{Zhang, Haoyang and Wei, Junkang and Liu, Zifeng and Liu, Xun and Chong, Yutian and Lu, Yutong and Zhao, Huiying and Yang, Yuedong}}, booktitle = {{Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021}}, editor = {{Huang, Yufei and Kurgan, Lukasz and Luo, Feng and Hu, Xiaohua Tony and Chen, Yidong and Dougherty, Edward and Kloczkowski, Andrzej and Li, Yaohang}}, isbn = {{9781665401265}}, keywords = {{differential analysis; mutation; oncogene}}, language = {{eng}}, pages = {{793--796}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021}}, title = {{DGAT-onco : A differential analysis method to detect oncogenes by integrating functional information of mutations}}, url = {{http://dx.doi.org/10.1109/BIBM52615.2021.9669388}}, doi = {{10.1109/BIBM52615.2021.9669388}}, year = {{2021}}, }