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DNA familial binding profiles made easy: Comparison of various motif alignment and clustering strategies

Mahony, S and Auron, PE and Benos, PV (2007) DNA familial binding profiles made easy: Comparison of various motif alignment and clustering strategies. PLoS Computational Biology, 3 (3). 0578 - 0591. ISSN 1553-734X

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Transcription factor (TF) proteins recognize a small number of DNA sequences with high specificity and control the expression of neighbouring genes. The evolution of TF binding preference has been the subject of a number of recent studies, in which generalized binding profiles have been introduced and used to improve the prediction of new target sites. Generalized profiles are generated by aligning and merging the individual profiles of related TFs. However, the distance metrics and alignment algorithms used to compare the binding profiles have not yet been fully explored or optimized. As a result, binding profiles depend on TF structural information and sometimes may ignore important distinctions between subfamilies. Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP-chip data where frequently multiple putative targets of usually unknown TFs are predicted. Various comparison metrics and alignment algorithms are evaluated (a total of 105 combinations). We find that local alignments are generally better than global alignments at detecting eukaryotic DNA motif similarities, especially when combined with the sum of squared distances or Pearson's correlation coefficient comparison metrics. In addition, multiple-alignment strategies for binding profiles and tree-building methods are tested for their efficiency in constructing generalized binding models. A new method for automatic determination of the optimal number of clusters is developed and applied in the construction of a new set of familial binding profiles which improves upon TF classification accuracy. A software tool, STAMP, is developed to host all tested methods and make them publicly available. This work provides a high quality reference set of familial binding profiles and the first comprehensive platform for analysis of DNA profiles. Detecting similarities between DNA motifs is a key step in the comparative study of transcriptional regulation, and the work presented here will form the basis for tool and method development for future transcriptional modeling studies. © 2007 Mahony et al.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Mahony, S
Auron, PEauron@pitt.eduAURON
Benos, PVbenos@pitt.eduGSBCPLRC
ContributionContributors NameEmailPitt UsernameORCID
Centers: Other Centers, Institutes, Offices, or Units > Pittsburgh Cancer Institute
Date: 1 March 2007
Date Type: Publication
Journal or Publication Title: PLoS Computational Biology
Volume: 3
Number: 3
Page Range: 0578 - 0591
DOI or Unique Handle: 10.1371/journal.pcbi.0030061
Schools and Programs: School of Public Health > Human Genetics
Dietrich School of Arts and Sciences > Computer Science
School of Medicine > Computational Biology
Refereed: Yes
ISSN: 1553-734X
PubMed ID: 17397256
Date Deposited: 11 Jul 2012 17:25
Last Modified: 29 Oct 2022 11:55


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