Wijetunga N Ari, Johnston Andrew D, Maekawa Ryo, Delahaye Fabien, Ulahannan Netha, Kim Kami, Greally John M
Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.
Division of Obstetrics and Gynecology, Yamaguchi University, 677-1 Yoshida, Yamaguchi Prefecture, 753-8511, Japan.
BMC Bioinformatics. 2017 Jan 18;18(1):41. doi: 10.1186/s12859-017-1477-3.
The 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.
SMITE combines p-values by accounting for the correlation between non-independent values within data sets, allowing genes and gene modules in an interaction network to be assigned significance values. The contribution of each type of genomic data can be weighted, permitting integration of individually under-powered data sets, increasing the overall ability to detect effects within modules of genes. We apply SMITE to a complex genomic data set including the epigenomic and transcriptomic effects of Toxoplasma gondii infection on human host cells and demonstrate that SMITE is able to identify novel subnetworks of dysregulated genes. Additionally, we show that SMITE outperforms Functional Epigenetic Modules (FEM), the current paradigm of using the spin-glass algorithm to integrate gene expression and epigenetic data.
SMITE represents a flexible, scalable tool that allows integration of transcriptional and epigenetic regulatory data from genome-wide assays to boost confidence in finding gene modules reflecting altered cellular states.
检测基因表达、转录和表观遗传调控的分子检测方法日益多样且数量众多。每种检测方法单独产生的信息能让人深入了解所检测细胞的状态。有可能的是，将来自不同的互补检测方法的信息相加，以获得对细胞状态更具可信度的见解。目前，对多维、大规模全基因组数据的分析需要一个初始的筛选步骤，以创建可管理的观察子集，然后用于整合，这会减小相交数据集的大小以及获得生物学见解的潜力。我们开发了基于显著性的转录组和表观基因组整合模块（SMITE）方法，用于整合转录和表观遗传调控数据而不损失分辨率。
SMITE通过考虑数据集中非独立值之间的相关性来合并p值，从而使相互作用网络中的基因和基因模块能够被赋予显著性值。可以对每种类型的基因组数据的贡献进行加权，允许整合单独效力不足的数据集，提高在基因模块内检测效应的整体能力。我们将SMITE应用于一个复杂的基因组数据集，该数据集包括弓形虫感染对人类宿主细胞的表观基因组和转录组影响，并证明SMITE能够识别失调基因的新子网。此外，我们表明SMITE优于功能表观遗传模块（FEM），后者是使用自旋玻璃算法整合基因表达和表观遗传数据的当前范式。
SMITE是一种灵活、可扩展的工具，它允许整合来自全基因组检测的转录和表观遗传调控数据，以增强在寻找反映细胞状态改变的基因模块方面的信心。
