OUGene 2.0:An updated diseases-associated over-under-expressed gene database by mining full-text articles

Introduction

Gene overexpression or underexpression is always closely related to some diseases, it contributes to phenotypic mutation and diversity causing human diseases. To provide an overview knowledge in this field, OUGene is developed to compile a comprehensive disease-associated overexpressed and underexpressed genes, including protein, miRNA, lncRNA and other genes, using text mining pipeline and some public curated database. OUGene can be queried through single or multiple gene and disease names, and it can show gene-centric and disease-centric association netowrk, and their shared relations via Cytoscape Web plugin, which can give insights to the complex relationships between over- and under-expressed genes and diseases at a system level.


OUGene V1

You can access the old version OUGene v1


Reference

[1] Erdi Qin, Xiaoyong Pan, Hong-Bin Shen. OUGene 2.0: An updated diseases-associated over-under-expressed gene database by mining full-text articles. In preparation.
[2] Xiaoyong Pan and Hong-Bin Shen, OUGene: A diseases associated over-expressed and under-expressed gene database. Science Bulletin, 2016, 61: 752-754.


Change History



  • 2022-04-10
    ---an updated OUGene 2.0 is online .
  • 2015-11-27
    ---OUGene add tissue or cell line information for entries .
  • 2015-11-23
    ---OUGene add link to ENSEMBLE and correct some spelling mistakes .
  • 2014-12-03
    ---OUGene has better score for associations, mapped to uniq ensemble id, provides more eveidence for associations with many literatures support.
  • 2014-11-13
    ---OUGene support multiple gene and disease names search.
  • 2014-11-12
    ---OUGene add edge color for overexpression and underexpression for network visulization.
  • 2014-08-11
    ---OUGene was improved with better web interface. It support single gene and disease name search, download, submit new associations
  • 2014-07-25
    ---OUGene was available online for preliminary testing.

© 2014 Computational Systems Biology/Shen Group.