Evidence map›Paper›PMID 41518506›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

A Practical Guide to Gene Regulatory Networks in Plants: From RNA Sequencing to Identification of Transcription Factor Binding Sites.

Joaquín Grau, José M Franco-Zorrilla

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Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Joaquín GrauDepartment of Plant Molecular Genetics, Centro Nacional de Biotecnología-CSIC, Madrid, Spain.
José M Franco-ZorrillaDepartment of Plant Molecular Genetics, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Madrid, Spain. jmfranco@cnb.csic.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plant adaptive responses to their environment are orchestrated by transcriptional reprogramming, regulated by sequence-specific transcription factors (TFs) recognizing specific TF-binding sites (TFBSs), and understanding their regulatory grammar is crucial for unravelling plant adaptation mechanisms. Empirical data have contributed to the elucidation of TFBS sequence motifs involved in biological processes, but comprehensive experimental approaches may not be feasible, especially in non-model species. Different computational algorithms have facilitated the elucidation of TFBS sequence motifs and the construction of predictive models, shedding light on plant gene regulatory networks. In this context, the development of straightforward computational pipelines and easy-to-use bioinformatics tools is of particular relevance to make gene expression analysis accessible to the research community. This chapter presents a methodology to infer TF regulators applicable to 60 plant species from RNA sequencing (RNA-seq) data as the starting point. It includes RNA-seq analysis and quantification, gene clustering using WGCNA to identify co-regulated gene modules, and searching for enriched TFBSs associated with these modules. The methodology is illustrated using Arabidopsis RNA-seq data related to abiotic stress. By providing a user-friendly pipeline, researchers are empowered to unravel the molecular basis of gene expression dynamics in plants.

Indexed as

Computational BiologyGene Regulatory NetworksPlantsSequence Analysis, RNATranscription FactorsArabidopsisBinding SitesGene Expression Regulation, PlantSoftwareTranscription FactorsDNA-binding sitesRNA-seqTFBS enrichmentTranscription factor (TF)WGCNA clustering

Identifiers

PMID41518506

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Registered trials

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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.