Detecting DNA regulatory motifs by incorporating positional trends in information content

Genome Biology
Katherina J KechrisMichael B Eisen

Abstract

On the basis of the observation that conserved positions in transcription factor binding sites are often clustered together, we propose a simple extension to the model-based motif discovery methods. We assign position-specific prior distributions to the frequency parameters of the model, penalizing deviations from a specified conservation profile. Examples with both simulated and real data show that this extension helps discover motifs as the data become noisier or when there is a competing false motif.

References

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Citations

Dec 2, 2008·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Man-Hung Eric TangOle Winther
Dec 15, 2010·Journal of Bioinformatics and Computational Biology·Xi WangXuegong Zhang
Apr 29, 2006·BMC Bioinformatics·Bertrand R Huber, Martha L Bulyk
Jul 31, 2007·BMC Evolutionary Biology·Farida N EnikeevaVsevolod J Makeev
Apr 8, 2006·Biology Direct·Geir Kjetil Sandve, Finn Drabløs
Sep 29, 2011·PloS One·Lala M Motlhabi, Gary D Stormo
Aug 21, 2012·Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·Z John DayeHongzhe Li
May 5, 2007·Journal of Bioinformatics and Computational Biology·Chengpeng Bi
Aug 2, 2007·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Michael A Lones, Andy M Tyrrell
Feb 24, 2005·Gene·Ekaterina A KotelnikovaMikhail S Gelfand

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Methods Mentioned

BETA
RSA

Software Mentioned

Crp
R
Gibbs
RSA
Gal4
ANSI
MEME
DPInteract
Gibbs Motif Sampler
BioProspector

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