Modeling regulatory sites with higher order position-dependent weight matrices

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Identification of regulatory signals in DNA depends on the nature and quality of the patterns of representative sequences. These patterns are constructed from training sets of sequences by means of probabilistic models that either assume independence between positions or that suffer from considerable computational complexity. We have developed and tested higher order mod-els that account for significant dependent position pairs or triads, thereby capturing position-dependent information hidden in DNA binding sites. We have evaluated our algorithm on several data sets, including eukaryotic and bacterial transcription factor binding sites and shown that the scores from the higher order representation of binding sites have significant positive correlation to the binding affinity scores.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages629-632
Number of pages4
DOIs
StatePublished - Sep 16 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • DNA binding sites
  • Position weight matrix
  • Regulatory signal
  • Transcription factor

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