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SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information

机译:SMITE:R / Bioconductor软件包通过整合基因组和表观基因组信息来识别网络模块

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摘要

BackgroundThe molecular assays that test gene expression, transcriptional, and epigenetic regulation are increasingly diverse and numerous. The information generated by each type of assay individually gives an insight into the state of the cells tested. What should be possible is to add the information derived from separate, complementary assays to gain higher-confidence insights into cellular states. At present, the analysis of multi-dimensional, massive genome-wide data requires an initial pruning step to create manageable subsets of observations that are then used for integration, which decreases the sizes of the intersecting data sets and the potential for biological insights. Our Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) approach was developed to integrate transcriptional and epigenetic regulatory data without a loss of resolution.
机译:背景技术用于测试基因表达,转录和表观遗传调控的分子测定法越来越多样化且数量众多。每种化验类型产生的信息都可以让您深入了解被测细胞的状态。应该有可能添加从单独的补充测定中获得的信息,以获得对细胞状态的更高置信度的洞察力。目前,多维,海量全基因组数据的分析需要一个初始的修剪步骤,以创建可管理的观测子集,然后将其用于整合,这将减少相交数据集的大小和生物学见解的潜力。我们整合了转录组和表观基因组(SMITE)方法的基于显着性的模块可以在不降低分辨率的情况下整合转录和表观遗传调控数据。

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