Extraction of transcription regulatory signals from genome-wide DNA–protein interaction data
Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate, cis-acting motifs they bind, expression profiles they dictate and more complex relationships between alternative regulatory partnerships and alternativ...
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Published in | Nucleic acids research Vol. 33; no. 2; pp. 605 - 615 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
01.01.2005
Oxford Publishing Limited (England) |
Subjects | |
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Abstract | Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate, cis-acting motifs they bind, expression profiles they dictate and more complex relationships between alternative regulatory partnerships and alternative regulatory motifs that give rise to sub-modalities of expression profiles. The ‘location data’ in yeast is a comprehensive resource that provides transcription factor–DNA interaction information in vivo. Here, we provide two contributions: first, we developed means to assess the extent of noise in the location data, and consequently for extracting signals from it. Second, we couple signal extraction with better characterization of the genetic network architecture. We apply two methods for the detection of combinatorial associations between transcription factors (TFs), the integration of which provides a global map of combinatorial regulatory interactions. We discover the capacity of regulatory motifs and TF partnerships to dictate fine-tuned expression patterns of subsets of genes, which are clearly distinct from those displayed by most genes assigned to the same TF. Our findings provide carefully prioritized, high-quality assignments between regulators and regulated genes and as such should prove useful for experimental and computational biologists alike. |
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AbstractList | Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate,
cis
-acting motifs they bind, expression profiles they dictate and more complex relationships between alternative regulatory partnerships and alternative regulatory motifs that give rise to sub-modalities of expression profiles. The ‘location data’ in yeast is a comprehensive resource that provides transcription factor–DNA interaction information
in vivo
. Here, we provide two contributions: first, we developed means to assess the extent of noise in the location data, and consequently for extracting signals from it. Second, we couple signal extraction with better characterization of the genetic network architecture. We apply two methods for the detection of combinatorial associations between transcription factors (TFs), the integration of which provides a global map of combinatorial regulatory interactions. We discover the capacity of regulatory motifs and TF partnerships to dictate fine-tuned expression patterns of subsets of genes, which are clearly distinct from those displayed by most genes assigned to the same TF. Our findings provide carefully prioritized, high-quality assignments between regulators and regulated genes and as such should prove useful for experimental and computational biologists alike. Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate, cis-acting motifs they bind, expression profiles they dictate and more complex relationships between alternative regulatory partnerships and alternative regulatory motifs that give rise to sub-modalities of expression profiles. The 'location data' in yeast is a comprehensive resource that provides transcription factor-DNA interaction information in vivo. Here, we provide two contributions: first, we developed means to assess the extent of noise in the location data, and consequently for extracting signals from it. Second, we couple signal extraction with better characterization of the genetic network architecture. We apply two methods for the detection of combinatorial associations between transcription factors (TFs), the integration of which provides a global map of combinatorial regulatory interactions. We discover the capacity of regulatory motifs and TF partnerships to dictate fine-tuned expression patterns of subsets of genes, which are clearly distinct from those displayed by most genes assigned to the same TF. Our findings provide carefully prioritized, high-quality assignments between regulators and regulated genes and as such should prove useful for experimental and computational biologists alike. Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate, cis-acting motifs they bind, expression profiles they dictate and more complex relationships between alternative regulatory partnerships and alternative regulatory motifs that give rise to sub-modalities of expression profiles. The 'location data[rsquor] in yeast is a comprehensive resource that provides transcription factor-DNA interaction information in vivo. Here, we provide two contributions: first, we developed means to assess the extent of noise in the location data, and consequently for extracting signals from it. Second, we couple signal extraction with better characterization of the genetic network architecture. We apply two methods for the detection of combinatorial associations between transcription factors (TFs), the integration of which provides a global map of combinatorial regulatory interactions. We discover the capacity of regulatory motifs and TF partnerships to dictate fine-tuned expression patterns of subsets of genes, which are clearly distinct from those displayed by most genes assigned to the same TF. Our findings provide carefully prioritized, high-quality assignments between regulators and regulated genes and as such should prove useful for experimental and computational biologists alike. |
Author | Garten, Yael Kaplan, Shai Pilpel, Yitzhak |
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Copyright | Copyright Oxford University Press(England) 2005 The Author 2005. Published by Oxford University Press. All rights reserved. 2005 |
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Notes | To whom correspondence should be addressed. Tel: +972 8 9346058; Fax: +972 8 9344108; Email: pilpel@weizmann.ac.il istex:E413D262EFE2798BEC673BC213BC406C24738FFC local:gki166 ark:/67375/HXZ-0NWJW0QS-G ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Undefined-1 ObjectType-Feature-3 The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors |
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Snippet | Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate, cis-acting... Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate, cis... |
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SubjectTerms | Binding Sites Computational Biology - methods Data Interpretation, Statistical DNA-Binding Proteins - analysis DNA-Binding Proteins - metabolism Fungal Proteins - metabolism Gene Expression Profiling Gene Expression Regulation Genome Genomics - methods Promoter Regions, Genetic Regulatory Sequences, Nucleic Acid Transcription Factors - analysis Transcription Factors - metabolism Transcription, Genetic |
Title | Extraction of transcription regulatory signals from genome-wide DNA–protein interaction data |
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