ArticlePLoS computational biology2006
Integrated assessment and prediction of transcription factor binding.
Article in PLoS computational biology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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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.
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Who cites it
65 citing papers in PubMed, 91 citations in OpenAlex.
- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- Systematic inference of indirect transcriptional regulation by protein kinases and phosphatases.PLoS computational biology · 2022Article
- Reg1 and Snf1 regulate stress-induced relocalization of protein phosphatase-1 to cytoplasmic granules.The FEBS journal · 2021Article
- Molecular networks in Network Medicine: Development and applications.Wiley interdisciplinary reviews. Systems biology and medicine · 2020Review
- The Rad53Nature communications · 2020Article
- Transcription factor regulatory modules provide the molecular mechanisms for functional redundancy observed among transcription factors in yeast.BMC bioinformatics · 2019Article
- Integrated systems-genetic analyses reveal a network target for delaying glioma progression.Annals of clinical and translational neurology · 2019Article
- Transcriptional responses to DNA damage.DNA repair · 2019Review
- Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant.Nucleic acids research · 2018Article
- Bipartite graphs in systems biology and medicine: a survey of methods and applications.GigaScience · 2018Review
- Construction of an integrated gene regulatory network link to stress-related immune system in cattle.Genetica · 2017Article
- TRRUST: a reference database of human transcriptional regulatory interactions.Scientific reports · 2015Article
- cDREM: inferring dynamic combinatorial gene regulation.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Epigenomic elements enriched in the promoters of autoimmunity susceptibility genes.Epigenetics · 2014Article
- Mapping condition-dependent regulation of lipid metabolism in Saccharomyces cerevisiae.G3 (Bethesda, Md.) · 2013Article
- Mapping functional transcription factor networks from gene expression data.Genome research · 2013Article
- Checkpoint kinases regulate a global network of transcription factors in response to DNA damage.Cell reports · 2013Article
- Allele-specific behavior of molecular networks: understanding small-molecule drug response in yeast.PloS one · 2013Article
- Inferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout data.BMC systems biology · 2013Article
- Assessing computational methods for transcription factor target gene identification based on ChIP-seq data.PLoS computational biology · 2013Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Systematic chromatin immunoprecipitation (chIP-chip) experiments have become a central technique for mapping transcriptional interactions in model organisms and humans. However, measurement of chromatin binding does not necessarily imply regulation, and binding may be difficult to detect if it is condition or cofactor dependent. To address these challenges, we present an approach for reliably assigning transcription factors (TFs) to target genes that integrates many lines of direct and indirect evidence into a single probabilistic model. Using this approach, we analyze publicly available chIP-chip binding profiles measured for yeast TFs in standard conditions, showing that our model interprets these data with significantly higher accuracy than previous methods. Pooling the high-confidence interactions reveals a large network containing 363 significant sets of factors (TF modules) that cooperate to regulate common target genes. In addition, the method predicts 980 novel binding interactions with high confidence that are likely to occur in so-far untested conditions. Indeed, using new chIP-chip experiments we show that predicted interactions for the factors Rpn4p and Pdr1p are observed only after treatment of cells with methyl-methanesulfonate, a DNA-damaging agent. We outline the first approach for consistently integrating all available evidences for TF-target interactions and we comprehensively identify the resulting TF module hierarchy. Prioritizing experimental conditions for each factor will be especially important as increasing numbers of chIP-chip assays are performed in complex organisms such as humans, for which "standard conditions" are ill defined.
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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.