Wang Chunyan, Xu Yiqing, Wang Xuelin, Zhang Li, Wei Suyun, Ye Qiaolin, Zhu Youxiang, Yin Hengfu, Nainwal Manoj, Tanon-Reyes Luis, Cheng Feng, Yin Tongming, Ye Ning
College of Information Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu, China.
Key Laboratory of Forest genetics and breeding, Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, Zhejiang, China.
PeerJ. 2018 Jun 20;6:e4927. doi: 10.7717/peerj.4927. eCollection 2018.
Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a 'desirable' or 'user-defined' pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users' requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.
基因表达谱数据为生物学功能和过程的研究提供了有用信息。然而,从大量时间序列基因表达数据中识别特定的表达模式并非易事。聚类是一种常用方法,常用于对相似表达基因进行分类,但是,聚类方法无法有效检测出具有“理想”或“用户定义”模式的基因。为了解决这些局限性,我们开发了一个名为GEsture的在线工具。用户可以使用鼠标绘制或描绘曲线,而无需输入聚类方法的抽象参数。GEsture以时间序列数据集的基因表达曲线作为输入,探索显示相似、相反和时间延迟表达模式的基因。我们给出了三个例子,说明了GEsture在满足用户需求的同时进行基因搜寻的能力。GEsture还提供可视化工具(如表达模式图、热图和相关网络)来展示搜索结果。结果输出可能为研究人员了解所涉及基因的靶点、功能和生物学过程提供有用信息。