Multi-omics data integration for topology-based pathway activation assessment and personalized drug ranking.

IF 2.4 4区生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY

Molecular omics Pub Date : 2025-10-01 DOI:10.1039/d5mo00151j

Nicolas Borisov, Yaroslav Ilnytsky, Boseon Byeon, Olga Kovalchuk, Igor Kovalchuk

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引用次数: 0

Abstract

Although multi-omics analysis is popular for revealing diverse physiological effects and biomarkers in many branches of state-of-the-art molecular and cell biology and bioinformatics, there is still no consensus on a gold standard protocol for the integration of various multi-omics profiles into a uniformly shaped system bioinformatics platform. In the current study, we performed the integration of data on DNA methylation, and the expression of coding RNA (mRNA), micro-RNA (miRNA), and long non-coding RNA into a joint platform for calculation of signaling pathway impact analysis (SPIA) and drug efficiency index (DEI). We found that the mirrored and balanced DEI values fitted the DNA methylome data better than the original DEI. Additionally, the protein-coding mRNA-based values correlated more strongly with antisense lncRNA-based values than with miRNA-based values. The whole correlation between the mRNA-based and antisense lncRNA-based values was generally positive. This platform allowed integrative analysis of several levels of gene expression regulation of protein-coding genes and their regulators, including methylation and noncoding RNAs.

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基于拓扑的通路激活评估和个体化药物排序的多组学数据集成。

尽管多组学分析在最新的分子和细胞生物学和生物信息学的许多分支中揭示了不同的生理效应和生物标志物，但对于将各种多组学剖面整合到统一形状的系统生物信息学平台中的金标准方案仍然没有达成共识。在本研究中，我们将DNA甲基化数据以及编码RNA （mRNA）、微RNA （miRNA）和长链非编码RNA的表达整合到一个联合平台中，用于计算信号通路影响分析（SPIA）和药物效率指数（DEI）。我们发现镜像和平衡的DEI值比原始DEI更适合DNA甲基组数据。此外，基于蛋白质编码mrna的值与基于反义lncrna的值的相关性比基于mirna的值更强。基于mrna的值与反义lncrna的值总体呈正相关。该平台允许对蛋白质编码基因及其调控因子的几个水平的基因表达调控进行综合分析，包括甲基化和非编码rna。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Molecular omics Biochemistry, Genetics and Molecular Biology-Biochemistry

CiteScore

5.40

自引率

3.40%

发文量

期刊介绍： Molecular Omics publishes high-quality research from across the -omics sciences. Topics include, but are not limited to: -omics studies to gain mechanistic insight into biological processes – for example, determining the mode of action of a drug or the basis of a particular phenotype, such as drought tolerance -omics studies for clinical applications with validation, such as finding biomarkers for diagnostics or potential new drug targets -omics studies looking at the sub-cellular make-up of cells – for example, the subcellular localisation of certain proteins or post-translational modifications or new imaging techniques -studies presenting new methods and tools to support omics studies, including new spectroscopic/chromatographic techniques, chip-based/array technologies and new classification/data analysis techniques. New methods should be proven and demonstrate an advance in the field. Molecular Omics only accepts articles of high importance and interest that provide significant new insight into important chemical or biological problems. This could be fundamental research that significantly increases understanding or research that demonstrates clear functional benefits. Papers reporting new results that could be routinely predicted, do not show a significant improvement over known research, or are of interest only to the specialist in the area are not suitable for publication in Molecular Omics.