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Bioinformatics Advance Access published online on October 27, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti083
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received April 14, 2004
Revised October 4, 2004
Accepted October 6, 2004

Article

Vocabulon: a dictionary model approach for reconstruction and localization of transcription factor binding sites

Chiara Sabatti 1*, Lars Rohlin 2, Kenneth Lange 3, and James C. Liao 2

1 Department of Human Genetics, UCLA, Los Angeles CA 90095-7088; Department of Statistics, UCLA, Los Angeles CA 90095-7088
2 Department of Chemical Engineering, UCLA, Los Angeles CA 90095
3 Department of Human Genetics, UCLA, Los Angeles CA 90095-7088; Department of Statistics, UCLA, Los Angeles CA 90095-7088; Department of Biomathematics, UCLA, Los Angeles, CA 90095

* To whom correspondence should be addressed.
Chiara Sabatti, E-mail: csabatti{at}mednet.ucla.edu


  Abstract

We describe the first implementation of dictionary-style models to the study of transcription factors binding sites in an entire genome. Vocabulon's unique feature is that it can both reconstruct binding sites characterized by unknown motifs and impute locations of known binding sites in long sequences by simultaneous search. On one hand, the dictionary model specifies a probability for the entire sequence taking simultaneously into account all the possible binding sites. This greatly reduces the number of false positives. On the other hand, the possibility of refining motif description, as an increasing number of binding sites are identified, augments the sensitivity of the method. We illustrate these properties with examples in Escherichia Coli. The results of gene expression arrays are used both to guide the search and corroborate it.


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C. Sabatti and G. M. James
Bayesian sparse hidden components analysis for transcription regulation networks
Bioinformatics, March 15, 2006; 22(6): 739 - 746.
[Abstract] [Full Text] [PDF]


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