ArticleGenome research2013
Mapping functional transcription factor networks from gene expression data.
Article in Genome research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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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.
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Who cites it
31 citing papers in PubMed, 46 citations in OpenAlex.
- METANet: a supervised ensemble learning framework for reconstructing direct and functional tissue-specific transcription factor networks.Bioinformatics (Oxford, England) · 2026Article
- Combining Motifs, CRE Activity, And Gene Expression Data Using ML Greatly Improves the Accuracy of Tissue-Specific TF Network Maps.bioRxiv : the preprint server for biology · 2025Article
- Inferring Interaction Networks from Transcriptomic Data: Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2024Article
- GRaNIE and GRaNPA: inference and evaluation of enhancer-mediated gene regulatory networks.Molecular systems biology · 2023Article
- NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration.Bioinformatics (Oxford, England) · 2023Article
- Systems Genetics for Mechanistic Discovery in Heart Diseases.Circulation research · 2020Review
- Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses.Genome research · 2020Article
- NIN Acts as a Network Hub Controlling a Growth Module Required for Rhizobial Infection.Plant physiology · 2019Article
- Unraveling synthesis of the cryptococcal cell wall and capsule.Glycobiology · 2018Review
- ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference.BMC genomics · 2018Article
- Seven-Up Is a Novel Regulator of Insulin Signaling.Genetics · 2018Article
- NetProphet 2.0: mapping transcription factor networks by exploiting scalable data resources.Bioinformatics (Oxford, England) · 2018Article
- Genome-wide analyses of direct target genes of four rice NAC-domain transcription factors involved in drought tolerance.BMC genomics · 2018Article
- A Wor1-Like Transcription Factor Is Essential for Virulence ofFrontiers in cellular and infection microbiology · 2018Article
- Associating transcription factors and conserved RNA structures with gene regulation in the human brain.Scientific reports · 2017Article
- SeqEnrich: A tool to predict transcription factor networks from co-expressed Arabidopsis and Brassica napus gene sets.PloS one · 2017Article
- Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Past Roadblocks and New Opportunities in Transcription Factor Network Mapping.Trends in genetics : TIG · 2016Review
- Article
- Understanding transcriptional regulatory networks using computational models.Current opinion in genetics & development · 2016Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein-DNA interactions have been identified for most TFs by ChIP-chip, and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: The response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TF's binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: The best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1.
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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.