ArticleMethods in molecular biology (Clifton, N.J.)2023
Coexpression Network Construction and Visualization from Transcriptomes Underlying ER Stress Responses.
Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- A tissue-resolved, network-based transcriptomic framework for abiotic stress responses in sorghum.The Plant journal : for cell and molecular biology · 2026Article
- A network-enabled pipeline for gene discovery and validation in non-model plant species.Cell reports methods · 2025Article
- Post-translational modifications: emerging directors of cell-fate decisions during endoplasmic reticulum stress in Arabidopsis thaliana.Biochemical Society transactions · 2024Review
- Multi-omics Resources for Understanding Gene Regulation in Response to ER Stress in Plants.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Mechanisms of Endoplasmic Reticulum Protein Homeostasis in Plants.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Dynamic gene expression changes are primary cellular reactions in response to most stresses and developmental cues in all organisms, including plants. With the ever-decreasing cost and increasing access, high-throughput transcriptome analyses have become a significant research tool to understand a wide spectrum of complex gene regulatory mechanisms. However, it is still challenging to understand the complete picture of gene responses because of the interactive and dynamic nature of gene expression in biological networks. Coexpression network analyses followed by network mapping are being increasingly applied to overcome this challenge. In this chapter, we will introduce detailed instructions for performing a weighted coexpression network analysis (WGCNA) and network visualization using a transcriptome dataset obtained during recovery from endoplasmic reticulum (ER) stress in Arabidopsis thaliana. The streamlined workflow described here allows biologists to identify and visualize coexpression interactions among genes, accessing a comprehensive landscape of dynamic gene expression changes for further downstream analyses using their datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.