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Representation and Quantification of Module Activity from Omics Data with Rroma

NPJ systems biology and applications(2024)SCI 2区SCI 1区

INSERM U900

Cited 0|Views28
Abstract
The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package’s capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at: www.github.com/sysbio-curie/rROMA . Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation.
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Genomic Data Integration,Systems Biology,Gene Set Enrichment Analysis
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要点】:本文介绍了rROMA软件包,一种用于快速准确计算基因集协调表达活动的工具,强调了从单个基因表达转向基因集活动的研究转变,并在囊性纤维化疾病研究中发现了新的相关生物机制。

方法】:rROMA通过改进计算算法,并添加了多项统计分析和可视化功能,提高了基因集活动计算的准确性和效率。

实验】:作者使用公开的转录组数据集,应用rROMA对囊性纤维化进行了研究,实验结果揭示了与疾病相关的活跃信号通路,并提出了新的研究假设和潜在的关键基因。