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A computational pipeline to identify new potential regulatory motifs in melanoma progression

机译:确定黑素瘤进展中新的潜在调控基元的计算管道

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摘要

Molecular biology experiments allow to obtain reliable data about the expression of different classes of molecules involved in several cellular processes. This information is mostly static and does not give much clue about the causal relationships (i.e., regulation) among the different molecules. A typical scenario is the presence of a set of modulated mRNAs (up or down regulated) along with an over expression of one or more small non-coding RNAs molecules like miRNAs. To computationally identify the presence of transcriptional or post-transcriptional regulatory modules between one or more miRNAs and a set of target modulated genes, we propose a computational pipeline designed to integrate data from multiple online data repositories. The pipeline produces a set of three types of putative regulatory motifs involving coding genes, intronic miRNAs, and transcription factors. We used this pipeline to analyze the results of a set of expression experiments on a melanoma cell line that showed an over expression of miR-214 along with the modulation of a set of 73 other genes. The results suggest the presence of 27 putative regulatory modules involving miR-214, NFKB1, SREBPF2, miR-33a and 9 out of the 73 miR-214 modulated genes (ALCAM, POSTN, TFAP2A, ADAM9, NCAM1, SEMA3A, PVRL2, JAG1, EGFR1). As a preliminary experimental validation we focused on 9 out of the 27 identified regulatory modules that involve miR-33a and SREBF2. The results confirm the importance of the predictions obtained with the presented computational approach
机译:分子生物学实验可以获取有关参与几种细胞过程的不同类别分子表达的可靠数据。该信息大部分是静态的,并且没有提供关于不同分子之间的因果关系(即调节)的太多线索。典型的场景是存在一组调制的mRNA(上调或下调),以及一个或多个小的非编码RNA分子(如miRNA)的过表达。为了以计算方式识别一个或多个miRNA与一组目标调控基因之间转录或转录后调控模块的存在,我们提出了一种计算管道,旨在整合来自多个在线数据存储库的数据。管道产生了一套三类推定的调控基序,涉及编码基因,内含子miRNA和转录因子。我们使用该管道分析了在黑色素瘤细胞系上进行的一系列表达实验的结果,该实验显示了miR-214的过度表达以及一组73个其他基因的调节。结果表明,在73个miR-214调节基因(ALCAM,POSTN,TFAP2A,ADAM9,NCAM1,SEMA3A,PVRL2,JAG1, EGFR1)。作为初步的实验验证,我们集中于27个已鉴定的涉及miR-33a和SREBF2的调控模块中的9个。结果证实了所提出的计算方法获得的预测的重要性

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