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TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining
Genomics, Proteomics & Bioinformatics ( IF 9.5 ) Pub Date : 2021-03-08 , DOI: 10.1016/j.gpb.2019.05.006
Zhi Huang 1 , Zhi Han 2 , Tongxin Wang 3 , Wei Shao 2 , Shunian Xiang 4 , Paul Salama 5 , Maher Rizkalla 5 , Kun Huang 2 , Jie Zhang 4
Affiliation  

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.



中文翻译:

TSUNAMI:用于网络分析和挖掘的转化生物信息学工具套件

基因共表达网络(GCN) 挖掘可识别具有跨样本/条件的高度相关表达谱的基因模块。它使研究人员能够发现潜在的基因/分子相互作用,识别新的基因功能,并从某些疾病/病症组中提取分子特征,从而帮助识别疾病生物标志物。然而,目前缺乏一个易于使用的工具包供用户挖掘GCN模块,这些模块体积较小,基因紧密连接,便于下游基因集富集分析,以及可能共享共同成员的模块。为了满足这一需求,我们开发了一个在线 GCN 挖掘工具包:TSUNAMI(用于网络分析和挖掘的工具套件)。TSUNAMI 采用了我们最先进的lmQCM算法为公共和用户输入数据(微阵列、RNA-seq 或任何其他数值组学数据)挖掘 GCN 模块,然后对识别的模块执行下游基因集富集分析。它有几个特点和优点:1)用户友好的界面和通过Web服务器进行的实时共表达网络挖掘; 2)直接访问和搜索NCBI基因表达综合(GEO)和癌症基因组图谱(TCGA)数据库,以及用于GCN模块挖掘的用户输入基因表达矩阵;3)多种共表达分析工具可供选择,在参数选择选项方面都具有高度的灵活性;4) 将识别出的GCN模块汇总为特征基因,方便用户查看其与其他临床特征的相关性;5)集成下游Enrichr富集分析和其他基因集富集工具的链接;和 6) 在过程的任何步骤中通过 Circos 图对基因位点进行可视化。该网络服务可通过 URL 免费访问:https://biolearns.medicine.iu.edu/。源码在 https://github.com/huangzhii/TSUNAMI/。

更新日期:2021-03-08
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