Integrated Assessment and Prediction of Transcription Factor Binding
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 co...
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| Vydáno v: | PLoS computational biology Ročník 2; číslo 6; s. e70 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
01.06.2006
Public Library of Science (PLoS) |
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| ISSN: | 1553-7358, 1553-734X, 1553-7358 |
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| 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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| AbstractList | 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.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. 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. Transcription factors (TFs) bind close to their target genes for regulating transcript levels depending on cellular conditions. Each gene may be regulated differently from others through the binding of specific groups of TFs (TF modules). Recently, a wide variety of large-scale measurements about transcriptional networks has become available. Here the authors present a framework for consistently integrating all of this evidence to systematically determine the precise set of genes directly regulated by each TF (i.e., TF–target interactions). The framework is applied to the yeast Saccharomyces cerevisiae using seven distinct sources of evidences to score all possible TF–target interactions in this organism. Subsequently, the authors employ another newly developed algorithm to reveal TF modules based on the top 5,000 TF–target interactions, yielding more than 300 TF modules. The new scoring scheme for TF–target interactions allows predicting the binding of TFs under so-far untested conditions, which is demonstrated by experimentally verifying interactions for two TFs (Pdr1p, Rpn4p). Importantly, the new methods (scoring of TF–target interactions and TF module identification) are scalable to much larger datasets, making them applicable to future studies in humans, which are thought to have substantially larger numbers of TF–target interactions. 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. 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. |
| Audience | Academic |
| Author | Möller, Ulrich Radke, Dörte Wilhelm, Thomas Hollunder, Jens Workman, Christopher Beyer, Andreas Ideker, Trey |
| AuthorAffiliation | 1 Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America 2 Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany 3 Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany EMBL Heidelberg, Germany |
| AuthorAffiliation_xml | – name: 2 Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany – name: 1 Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America – name: 3 Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany – name: EMBL Heidelberg, Germany |
| Author_xml | – sequence: 1 givenname: Andreas surname: Beyer fullname: Beyer, Andreas – sequence: 2 givenname: Christopher surname: Workman fullname: Workman, Christopher – sequence: 3 givenname: Jens surname: Hollunder fullname: Hollunder, Jens – sequence: 4 givenname: Dörte surname: Radke fullname: Radke, Dörte – sequence: 5 givenname: Ulrich surname: Möller fullname: Möller, Ulrich – sequence: 6 givenname: Thomas surname: Wilhelm fullname: Wilhelm, Thomas – sequence: 7 givenname: Trey surname: Ideker fullname: Ideker, Trey |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16789814$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2006 Public Library of Science 2006 Beyer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Beyer A, Workman C, Hollunder J, Radke D, Möller U, et al. (2006) Integrated Assessment and Prediction of Transcription Factor Binding. PLoS Comput Biol 2(6): e70. doi:10.1371/journal.pcbi.0020070 2006 Beyer et al. 2006 |
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| Snippet | Systematic chromatin immunoprecipitation (chIP-chip) experiments have become a central technique for mapping transcriptional interactions in model organisms... Systematic chromatin immunoprecipitation (chIP-chip) experiments have become a central technique for mapping transcriptional interactions in model organisms... |
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| SubjectTerms | Algorithms Base Sequence Binding Sites Bioinformatics - Computational Biology Cell Biology Chromatin Immunoprecipitation - methods Computer Simulation DNA microarrays Experiments Gene expression Genetic transcription Genetics Genomes Methods Models, Chemical Models, Genetic Molecular Sequence Data Observations Protein Binding Saccharomyces Sequence Alignment - methods Sequence Analysis, DNA - methods Systems Biology Systems Integration Transcription Factors - chemistry Transcription Factors - genetics Transcription, Genetic - genetics Yeasts |
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| Title | Integrated Assessment and Prediction of Transcription Factor Binding |
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