Clinical feature-related single-base substitution sequence signatures identified with an unsupervised machine learning approach.

Journal: BMC medical genomics
Published Date: Dec 20, 2021

Abstract

BACKGROUND: Mutation processes leave different signatures in genes. For single-base substitutions, previous studies have suggested that mutation signatures are not only reflected in mutation bases but also in neighboring bases. However, because of the lack of a method to identify features of long sequences next to mutation bases, the understanding of how flanking sequences influence mutation signatures is limited.

Authors

  • Hongchen Ji
    Department of Oncology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi'an, 710032, China.
  • Junjie Li
    Department of Emergency, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi'an, China.
  • Qiong Zhang
    Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, Engineering Research Center of Beijing, Beijing University of Technology, Beijing 10024, China.
  • Jingyue Yang
    Department of Oncology, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi'an, 710032, China.
  • Juanli Duan
    Department of Hepatoxbiliary Surgery, Xijing Hospital, Fourth Military Medical University, No. 127 West Changle Road, Xi'an, China.
  • Xiaowen Wang
    Bioresources Green Transformation Collaborative Innovation Center of Hubei Province, College of Life Sciences, Hubei University, Wuhan 430062, Hubei, China.
  • Ben Ma
    School of Political Science and Public Administration, Shandong University, Qingdao, China.
  • Zhuochao Zhang
    Faculty of Hepatopancreatobiliary Surgery, Chinese PLA General Hospital, No. 28 Fuxing Road, Beijing, China.
  • Wei Pan
  • Hongmei Zhang
    School of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, Henan, China.

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