Krasnov George S, Dmitriev Alexey A, Melnikova Nataliya V, Zaretsky Andrew R, Nasedkina Tatiana V, Zasedatelev Alexander S, Senchenko Vera N, Kudryavtseva Anna V
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia N.N. Blokhin Russian Cancer Research Center, Moscow 115478, Russia Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, Moscow 119121, Russia.
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia
Nucleic Acids Res. 2016 Apr 20;44(7):e62. doi: 10.1093/nar/gkv1478. Epub 2016 Jan 14.
The contribution of different mechanisms to the regulation of gene expression varies for different tissues and tumors. Complementation of predicted mRNA-miRNA and gene-transcription factor (TF) relationships with the results of expression correlation analyses derived for specific tumor types outlines the interactions with functional impact in the current biomaterial. We developed CrossHub software, which enables two-way identification of most possible TF-gene interactions: on the basis of ENCODE ChIP-Seq binding evidence or Jaspar prediction and co-expression according to the data of The Cancer Genome Atlas (TCGA) project, the largest cancer omics resource. Similarly, CrossHub identifies mRNA-miRNA pairs with predicted or validated binding sites (TargetScan, mirSVR, PicTar, DIANA microT, miRTarBase) and strong negative expression correlations. We observed partial consistency between ChIP-Seq or miRNA target predictions and gene-TF/miRNA co-expression, demonstrating a link between these indicators. Additionally, CrossHub expression-methylation correlation analysis can be used to identify hypermethylated CpG sites or regions with the greatest potential impact on gene expression. Thus, CrossHub is capable of outlining molecular portraits of a specific gene and determining the three most common sources of expression regulation: promoter/enhancer methylation, miRNA interference and TF-mediated activation or repression. CrossHub generates formatted Excel workbooks with the detailed results. CrossHub is freely available athttps://sourceforge.net/projects/crosshub/.
不同机制对基因表达调控的贡献因不同组织和肿瘤而异。将预测的mRNA- miRNA和基因-转录因子(TF)关系与特定肿瘤类型的表达相关性分析结果相结合,勾勒出当前生物材料中具有功能影响的相互作用。我们开发了CrossHub软件,它能够双向识别最可能的TF-基因相互作用:基于ENCODE ChIP-Seq结合证据或Jaspar预测以及根据癌症基因组图谱(TCGA)项目(最大的癌症组学资源)的数据进行的共表达分析。同样,CrossHub根据预测或已验证的结合位点(TargetScan、mirSVR、PicTar、DIANA microT、miRTarBase)以及强负表达相关性来识别mRNA- miRNA对。我们观察到ChIP-Seq或miRNA靶标预测与基因- TF/miRNA共表达之间存在部分一致性,证明了这些指标之间的联系。此外,CrossHub表达-甲基化相关性分析可用于识别对基因表达具有最大潜在影响的高甲基化CpG位点或区域。因此,CrossHub能够勾勒特定基因的分子图谱,并确定三种最常见的表达调控来源:启动子/增强子甲基化、miRNA干扰以及TF介导的激活或抑制。CrossHub生成带有详细结果的格式化Excel工作簿。可通过https://sourceforge.net/projects/crosshub/免费获取CrossHub。