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OncoBinder facilitates interpretation of proteomic interaction data by capturing coactivation pairs in cancer

机译:OncoBinder通过捕获癌症中的共激活对来促进蛋白质组相互作用数据的解释

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摘要

High-throughput methods such as co-immunoprecipitationmass spectrometry (coIP-MS) and yeast 2 hybridization (Y2H) have suggested a broad range of unannotated protein-protein interactions (PPIs), and interpretation of these PPIs remains a challenging task. The advancements in cancer genomic researches allow for the inference of “coactivation pairs” in cancer, which may facilitate the identification of PPIs involved in cancer. Here we present OncoBinder as a tool for the assessment of proteomic interaction data based on the functional synergy of oncoproteins in cancer. This decision tree-based method combines gene mutation, copy number and mRNA expression information to infer the functional status of protein-coding genes. We applied OncoBinder to evaluate the potential binders of EGFR and ERK2 proteins based on the gastric cancer dataset of The Cancer Genome Atlas (TCGA). As a result, OncoBinder identified high confidence interactions (annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) or validated by low-throughput assays) more efficiently than co-expression based method. Taken together, our results suggest that evaluation of gene functional synergy in cancer may facilitate the interpretation of proteomic interaction data. The OncoBinder toolbox for Matlab is freely accessible online.
机译:诸如免疫共沉淀质谱法(coIP-MS)和酵母2杂交(Y2H)等高通量方法表明,广泛的未注释蛋白质-蛋白质相互作用(PPI),对这些PPI的解释仍然是一项艰巨的任务。癌症基因组研究的进展可以推断出癌症中的“共激活对”,这可能有助于鉴定参与癌症的PPI。在这里,我们介绍OncoBinder作为一种基于癌蛋白的功能协同作用来评估蛋白质组相互作用数据的工具。这种基于决策树的方法结合了基因突变,拷贝数和mRNA表达信息,以推断蛋白质编码基因的功能状态。我们基于癌症基因组图谱(TCGA)的胃癌数据集应用OncoBinder评估了EGFR和ERK2蛋白的潜在结合物。结果,与基于共表达的方法相比,OncoBinder可以更有效地识别高可信度的相互作用(由《京都议定书》的基因和基因组百科全书(KEGG)注释或通过低通量分析验证)。两者合计,我们的结果表明,评估癌症中的基因功能协同作用可能有助于蛋白质组相互作用数据的解释。可免费在线访问Matlab的OncoBinder工具箱。

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