WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update.

Journal: Nucleic acids research
Published Date:

Abstract

WEGO (Web Gene Ontology Annotation Plot), created in 2006, is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. Owing largely to the rapid development of high-throughput sequencing and the increasing acceptance of GO, WEGO has benefitted from outstanding performance regarding the number of users and citations in recent years, which motivated us to update to version 2.0. WEGO uses the GO annotation results as input. Based on GO's standardized DAG (Directed Acyclic Graph) structured vocabulary system, the number of genes corresponding to each GO ID is calculated and shown in a graphical format. WEGO 2.0 updates have targeted four aspects, aiming to provide a more efficient and up-to-date approach for comparative genomic analyses. First, the number of input files, previously limited to three, is now unlimited, allowing WEGO to analyze multiple datasets. Also added in this version are the reference datasets of nine model species that can be adopted as baselines in genomic comparative analyses. Furthermore, in the analyzing processes each Chi-square test is carried out for multiple datasets instead of every two samples. At last, WEGO 2.0 provides an additional output graph along with the traditional WEGO histogram, displaying the sorted P-values of GO terms and indicating their significant differences. At the same time, WEGO 2.0 features an entirely new user interface. WEGO is available for free at http://wego.genomics.org.cn.

Authors

  • Jia Ye
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Yong Zhang
    Outpatient Department of Hepatitis, The Sixth Affiliated People's Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Huihai Cui
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Jiawei Liu
    School of Biomedical Engineering, The Sixth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong 511436, China.
  • Yuqing Wu
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Yun Cheng
    Liaocheng Natural Resources and Planning Bureau, Liaocheng, Shandong, China.
  • Huixing Xu
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Xingxin Huang
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Shengting Li
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • An Zhou
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Xiuqing Zhang
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Lars Bolund
    Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao, Shandong, 266555, China.
  • Qiang Chen
    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Huanming Yang
    BGI-Shenzhen, Shenzhen 518083, China.
  • Lin Fang
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Chunmei Shi
    Department of Oncology, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China.