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DGAT-onco : A differential analysis method to detect oncogenes by integrating functional information of mutations

Zhang, Haoyang LU orcid ; Wei, Junkang ; Liu, Zifeng ; Liu, Xun ; Chong, Yutian ; Lu, Yutong ; Zhao, Huiying and Yang, Yuedong (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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Please use this url to cite or link to this publication:
author
; ; ; ; ; ; and
publishing date
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}},
}