DISPARE: DIScriminative PAttern REfinement for Position Weight Matrices.

BMC Bioinformatics
Isabelle da PiedadeOlivier Elemento

Abstract

The accurate determination of transcription factor binding affinities is an important problem in biology and key to understanding the gene regulation process. Position weight matrices are commonly used to represent the binding properties of transcription factor binding sites but suffer from low information content and a large number of false matches in the genome. We describe a novel algorithm for the refinement of position weight matrices representing transcription factor binding sites based on experimental data, including ChIP-chip analyses. We present an iterative weight matrix optimization method that is more accurate in distinguishing true transcription factor binding sites from a negative control set. The initial position weight matrix comes from JASPAR, TRANSFAC or other sources. The main new features are the discriminative nature of the method and matrix width and length optimization. The algorithm was applied to the increasing collection of known transcription factor binding sites obtained from ChIP-chip experiments. The results show that our algorithm significantly improves the sensitivity and specificity of matrix models for identifying transcription factor binding sites. When the transcription factor is known, it is...Continue Reading

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Citations

Dec 27, 2013·Bioinformatics·Ronak Y Patel, Gary D Stormo

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Methods Mentioned

BETA
footprinting
immunoprecipitation

Software Mentioned

MEME
DISPARE
ROCR
decodeanhmm
GLAM2
JASPAR
WEEDER
NestedMICA
PREGO
Scanseq

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