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Uncovering transcriptional interactions via an adaptive fuzzy logic approach

BACKGROUND: To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical intera...

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Autores principales: Chuang, Cheng-Long, Hung, Kenneth, Chen, Chung-Ming, Shieh, Grace S
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2797023/
https://www.ncbi.nlm.nih.gov/pubmed/19961622
http://dx.doi.org/10.1186/1471-2105-10-400
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author Chuang, Cheng-Long
Hung, Kenneth
Chen, Chung-Ming
Shieh, Grace S
author_facet Chuang, Cheng-Long
Hung, Kenneth
Chen, Chung-Ming
Shieh, Grace S
author_sort Chuang, Cheng-Long
collection PubMed
description BACKGROUND: To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy. RESULTS: AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms. CONCLUSION: AdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast.
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spelling pubmed-27970232009-12-23 Uncovering transcriptional interactions via an adaptive fuzzy logic approach Chuang, Cheng-Long Hung, Kenneth Chen, Chung-Ming Shieh, Grace S BMC Bioinformatics Research article BACKGROUND: To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy. RESULTS: AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms. CONCLUSION: AdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast. BioMed Central 2009-12-06 /pmc/articles/PMC2797023/ /pubmed/19961622 http://dx.doi.org/10.1186/1471-2105-10-400 Text en Copyright ©2009 Chuang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research article
Chuang, Cheng-Long
Hung, Kenneth
Chen, Chung-Ming
Shieh, Grace S
Uncovering transcriptional interactions via an adaptive fuzzy logic approach
title Uncovering transcriptional interactions via an adaptive fuzzy logic approach
title_full Uncovering transcriptional interactions via an adaptive fuzzy logic approach
title_fullStr Uncovering transcriptional interactions via an adaptive fuzzy logic approach
title_full_unstemmed Uncovering transcriptional interactions via an adaptive fuzzy logic approach
title_short Uncovering transcriptional interactions via an adaptive fuzzy logic approach
title_sort uncovering transcriptional interactions via an adaptive fuzzy logic approach
topic Research article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2797023/
https://www.ncbi.nlm.nih.gov/pubmed/19961622
http://dx.doi.org/10.1186/1471-2105-10-400
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