ArticlePLoS computational biology2009
A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 1 of them a synthesis that pooled it.
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Who cites it
115 citing papers in PubMed, 1 synthesis or guideline pooled it, 231 citations in OpenAlex.
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- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Sometimes it's good to be lucky: blood flow, glutathione, oxidative stress, and mitochondria.Frontiers in physiology · 2025Article
- DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.Bioinformatics (Oxford, England) · 2024Article
- A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering.BMC plant biology · 2024Article
- Dissecting the Immune System through Gene Regulation.Advances in experimental medicine and biology · 2024Article
- Exploring drought-responsive crucial genes iniScience · 2022Article
- Transformation to ischaemia tolerance of frog brain function corresponds to dynamic changes in mRNA co-expression across metabolic pathways.Proceedings. Biological sciences · 2022Article
- Pan-Cancer Analysis Reveals the Prognostic Potential of the THAP9/THAP9-AS1 Sense-Antisense Gene Pair in Human Cancers.Non-coding RNA · 2022Article
- A glomerular transcriptomic landscape of apolipoprotein L1 in Black patients with focal segmental glomerulosclerosis.Kidney international · 2022Article
- Network Analysis of Microarray Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Co-Expression Networks Unveiled Long Non-Coding RNAs as Molecular Targets of Drugs Used to Treat Bipolar Disorder.Frontiers in pharmacology · 2022Article
- Conditional transcriptional relationships may serve as cancer prognostic markers.BMC medical genomics · 2021Article
- Article
- Identification of defense related gene families and their response against powdery and downy mildew infections in Vitis vinifera.BMC genomics · 2021Article
- GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package.BMC bioinformatics · 2021Article
- Integrated Analysis of lncRNAs, mRNAs, and TFs to Identify Regulatory Networks Underlying MAP Infection in Cattle.Frontiers in genetics · 2021Article
- Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.International journal of molecular sciences · 2020Review
55 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcription factor (TF) regulation is often post-translational. TF modifications such as reversible phosphorylation and missense mutations, which can act independent of TF expression level, are overlooked by differential expression analysis. Using bovine Piedmontese myostatin mutants as proof-of-concept, we propose a new algorithm that correctly identifies the gene containing the causal mutation from microarray data alone. The myostatin mutation releases the brakes on Piedmontese muscle growth by translating a dysfunctional protein. Compared to a less muscular non-mutant breed we find that myostatin is not differentially expressed at any of ten developmental time points. Despite this challenge, the algorithm identifies the myostatin 'smoking gun' through a coordinated, simultaneous, weighted integration of three sources of microarray information: transcript abundance, differential expression, and differential wiring. By asking the novel question "which regulator is cumulatively most differentially wired to the abundant most differentially expressed genes?" it yields the correct answer, "myostatin". Our new approach identifies causal regulatory changes by globally contrasting co-expression network dynamics. The entirely data-driven 'weighting' procedure emphasises regulatory movement relative to the phenotypically relevant part of the network. In contrast to other published methods that compare co-expression networks, significance testing is not used to eliminate connections.
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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.