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Updated: Jan 31, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

SGCRNA: スペクトルクラスタリング誘導型共発現ネットワーク解析をスケールフリー制約なしでマルチオミクスデータに適用

Tatsunori Osone¹, Tomoka Takao¹, Shigeo Otake¹

¹Department of Regenerative Science, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan.

Briefings in bioinformatics

|January 30, 2026

Abstract:

Weighted gene co-expression network analysis (WGCNA) is among the most widely employed methods in bioinformatics. WGCNA enables the identification of gene clusters (modules) exhibiting correlated expression patterns, the association of these modules with traits, and the exploration of candidate biomarker genes by focusing on hub genes within the modules. WGCNA has been successfully applied in diverse biological contexts. However, conventional algorithms manifest three principal limitations: the assumption of scale-free topology, the requirement for parameter tuning, and the neglect of regression line slopes. These limitations are addressed by SGCRNA. SGCRNA provides Julia functions for the analysis of co-expression networks derived from various types of biological data, such as gene expression data. The Julia packages and their source code are freely available at https://github.com/C37H41N2O6/SGCRNAs.jl.

キーワード:

共発現ネットワーク解析マルチオミクススペクトルクラスタリング

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CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

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Published on: September 20, 2024

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