Bayesian Clustering of Transcription Factor Binding Motifs

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Bayesian Clustering of Transcription Factor Binding Motifs

Genes are often regulated in living cells by proteins called transcription factors (TFs) that bind directly to short segments of DNA in close proximity to specific genes. These binding sites have a conserved nucleotide appearance, which is called a motif. Several recent studies of transcriptional regulation require the reduction of a large collection of motifs into clusters based on the similar...

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A Bayesian Clustering Model for Transcription Factor Binding Motifs

Genes are often regulated in living cells by proteins called transcription factors that bind directly to short segments of DNA in close proximity to certain target genes. These short segments have a conserved appearance, which is called a motif. We propose a Bayesian hierarchical clustering model for the common structure between a set of discovered motifs. This clustering model utilizes a Diric...

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Transcription factor-DNA binding: beyond binding site motifs.

Sequence-specific transcription factors (TFs) regulate gene expression by binding to cis-regulatory elements in promoter and enhancer DNA. While studies of TF-DNA binding have focused on TFs' intrinsic preferences for primary nucleotide sequence motifs, recent studies have elucidated additional layers of complexity that modulate TF-DNA binding. In this review, we discuss technological developme...

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Varying levels of complexity in transcription factor binding motifs

Binding of transcription factors to DNA is one of the keystones of gene regulation. The existence of statistical dependencies between binding site positions is widely accepted, while their relevance for computational predictions has been debated. Building probabilistic models of binding sites that may capture dependencies is still challenging, since the most successful motif discovery approache...

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Theoretical and empirical quality assessment of transcription factor-binding motifs

Position-specific scoring matrices (PSSMs) are routinely used to predict transcription factor (TF)-binding sites in genome sequences. However, their reliability to predict novel binding sites can be far from optimum, due to the use of a small number of training sites or the inappropriate choice of parameters when building the matrix or when scanning sequences with it. Measures of matrix quality...

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ژورنال

عنوان ژورنال: Journal of the American Statistical Association

سال: 2008

ISSN: 0162-1459,1537-274X

DOI: 10.1198/016214507000000365