MotifPrototyper: a Bayesian profile model for motif families.

Abstract:

:In this article, we address the problem of modeling generic features of structurally but not textually related DNA motifs, that is, motifs whose consensus sequences are entirely different but nevertheless share "metasequence features" reflecting similarities in the DNA-binding domains of their associated protein recognizers. We present MotifPrototyper, a profile Bayesian model that can capture structural properties typical of particular families of motifs. Each family corresponds to transcription regulatory proteins with similar types of structural signatures in their DNA-binding domains. We show how to train MotifPrototypers from biologically identified motifs categorized according to the TRANSFAC categorization of transcription factors and present empirical results of motif classification, motif parameter estimation, and de novo motif detection by using the learned profile models.

authors

Xing EP,Karp RM

doi

10.1073/pnas.0403564101

keywords:

subject

Has Abstract

pub_date

2004-07-20 00:00:00

pages

10523-8

issue

29

eissn

0027-8424

issn

1091-6490

pii

0403564101

journal_volume

101

pub_type

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