ArticleFrontiers in molecular biosciences2019
Identification of Modules With Similar Gene Regulation and Metabolic Functions Based on Co-expression Data.
Article in Frontiers in molecular biosciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Integrative multi-transcriptomic analysis uncovers core genes and potential defense mechanisms in rice-Magnoporthe oryzae interaction.Plant cell reports · 2025Article
- Mining key circRNA-associated-ceRNA networks for milk fat metabolism in cows with varying milk fat percentages.BMC genomics · 2024Article
- Unlocking the genetic secrets of Dorper sheep: insights into wool shedding and hair follicle development.Frontiers in veterinary science · 2024Article
- Article
- Structure of co-expression networks ofFrontiers in molecular biosciences · 2023Article
- A globalBMC bioinformatics · 2022Article
- Identifying key genes in milk fat metabolism by weighted gene co-expression network analysis.Scientific reports · 2022Article
- Gene co-expression network analysis of the human gut commensal bacterium Faecalibacterium prausnitzii in R-Shiny.PloS one · 2022Article
- Co-expression of fibrotic genes in inflammatory bowel disease; A localized event?Frontiers in immunology · 2022Article
- Article
- Single-Cell RNA Sequencing Analysis of the Immunometabolic Rewiring and Immunopathogenesis of Coronavirus Disease 2019.Frontiers in immunology · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological systems respond to environmental perturbations and to a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. In particular, a wide variety of gene co-expression networks have been constructed in recent years thanks to the dramatic increase of experimental information obtained with techniques, such as microarrays and RNA sequencing. These networks allow the identification of groups of co-expressed genes that can function in the same process and, in turn, these networks may be related to biological functions of industrial, medical and academic interest. In this study, gene co-expression networks for 17 bacterial organisms from the COLOMBOS database were analyzed via weighted gene co-expression network analysis and clustered into modules of genes with similar expression patterns for each species. These networks were analyzed to determine relevant modules through a hypergeometric approach based on a set of transcription factors and enzymes for each genome. The richest modules were characterized using PFAM families and KEGG metabolic maps. Additionally, we conducted a Gene Ontology analysis for enrichment of biological functions. Finally, we identified modules that shared similarity through all the studied organisms by using comparative genomics.
Indexed as
Identifiers
What OpenQuestion holds
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.