审视从rna结合蛋白到癌症靶标的功能相互作用网络

Sajal Kumar, Hua Zhong, Ruby Sharma, Yiyi Li, Mingzhou Song
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引用次数: 3

摘要

RNA结合蛋白(rbp)参与RNA生命周期的各个阶段,从转录、剪接到翻译。在ENCODE项目下,大量rbp在人类癌细胞系中被敲除,为推断rbp的靶点提供了极好的机会。以RBP结合位点和RBP敲低样本的RNA-seq图谱为输入,我们提出了一个管道来识别RBP RNA的因果相互作用。该管道采用了最新的功能卡方检验(FunChisq)来解读方向关联,并利用一种新的功能指数来衡量功能依赖的效应大小。我们在白血病(K562)和肝癌(HepG2)细胞系中检测了价值4500万美元的RBP RNA对,以寻找作为因果相互作用候选者的功能模式。在这里,我们报告了两种细胞系中总共936,707对RBP RNA,这些RBP RNA在统计上显示出显著的线性或非线性功能模式。这些配对中约有31%有来自其他来源的支持性生物学证据,表明该管道的有效性。这种相互作用构成了RBP特异性调控网络,可能代表了这两种癌症的核心机制。该管道通过R接口实现,其中包含预先计算的结果和数据库,供用户查询特定网络并可视化RBP RNA相互作用。这些网络为研究癌症中的RNA失调提供了有用的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scrutinizing functional interaction networks from RNA-binding proteins to their targets in cancer
RNA-binding proteins (RBPs) participate in all stages of RNA life cycle from transcription, splicing, to translation. Under the ENCODE project, a large number of RBPs were knocked down in human cancer cell lines, offering an excellent opportunity to infer targets of RBPs. Taking both RBP binding sites and RNA-seq profiles of RBP knockdown samples as input, we present a pipeline to identify causal RBP RNA interactions. The pipeline employs a recent functional chi-square test (FunChisq) that deciphers directional association, and utilizes a novel functional index that measures the effect size of functional dependency. We examined $\sim 45$ million RBP RNA pairs in leukemia (K562) and liver cancer (HepG2) cell lines for functional patterns as causal interaction candidates. Here, we report a total of 936,707 RBP RNA pairs in the two cell lines that show statistically significant linear or nonlinear functional patterns. About 31% of these pairs have supportive biological evidence from other sources, suggesting the effectiveness of the pipeline. The interactions constitute RBP specific regulatory networks that may potentially represent core mechanisms in the two cancers. The pipeline is implemented through an R interface with pre-computed results and data libraries for users to query specific networks and visualize RBP RNA interactions. Such networks serve as a useful resource for studying RNA dysregulation in cancer.
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