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Large-scale correlation network construction for unraveling the coordination of complex biological systems.

ABSTRACT: Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

SUBMITTER: Becker M

PROVIDER: S-EPMC10727505 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Becker Martin M Nassar Huda H Espinosa Camilo C Stelzer Ina A IA Feyaerts Dorien D Berson Eloise E Bidoki Neda H NH Chang Alan L AL Saarunya Geetha G Culos Anthony A De Francesco Davide D Fallahzadeh Ramin R Liu Qun Q Kim Yeasul Y Marić Ivana I Mataraso Samson J SJ Payrovnaziri Seyedeh Neelufar SN Phongpreecha Thanaphong T Ravindra Neal G NG Stanley Natalie N Shome Sayane S Tan Yuqi Y Thuraiappah Melan M Xenochristou Maria M Xue Lei L Shaw Gary G Stevenson David D Angst Martin S MS Gaudilliere Brice B Aghaeepour Nima N

Nature computational science 20230413 4

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dim ...[more]

PMID: 38116462

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Project description:Network medicine relies on different types of networks: from the molecular level of protein?protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein?protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs-including long non-coding RNAs (lncRNAs) -competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes-called switch genes-critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.

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Dataset Information

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Publications

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

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