Abstract:
:Recognition of genomic binding sites by transcription factors can occur through base-specific recognition, or by recognition of variations within the structure of the DNA macromolecule. In this article, we investigate what information can be retrieved from local DNA structural properties that is relevant to transcription factor binding and that cannot be captured by the nucleotide sequence alone. More specifically, we explore the benefit of employing the structural characteristics of DNA to create binding-site models that encompass indirect recognition for the Escherichia coli model organism. We developed a novel methodology [Conditional Random fields of Smoothed Structural Data (CRoSSeD)], based on structural scales and conditional random fields to model and predict regulator binding sites. The value of relying on local structural-DNA properties is demonstrated by improved classifier performance on a large number of biological datasets, and by the detection of novel binding sites which could be validated by independent data sources, and which could not be identified using sequence data alone. We further show that the CRoSSeD-binding-site models can be related to the actual molecular mechanisms of the transcription factor DNA binding, and thus cannot only be used for prediction of novel sites, but might also give valuable insights into unknown binding mechanisms of transcription factors.
journal_name
Nucleic Acids Resjournal_title
Nucleic acids researchauthors
Meysman P,Dang TH,Laukens K,De Smet R,Wu Y,Marchal K,Engelen Kdoi
10.1093/nar/gkq1071subject
Has Abstractpub_date
2011-01-01 00:00:00pages
e6issue
2eissn
0305-1048issn
1362-4962pii
gkq1071journal_volume
39pub_type
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