Sep 302013
 

CENTIPEDE beta

:: DESCRIPTION

CENTIPEDE is a method developed by Roger Pique-Regi and Jacob Degner that uses PWM information plus experimental data such as DNase1, histone marks or FAIRE to infer transcription factor binding sites with high specificity. CENTIPEDE applies a hierarchical Bayesian mixture model to infer regions of the genome that are bound by particular transcription factors. It starts by identifying a set of candidate binding sites (e.g., sites that match a certain position weight matrix (PWM)), and then aims to classify the sites according to whether each site is bound or not bound by a TF. CENTIPEDE is an unsupervised learning algorithm that discriminates between two different types of motif instances using as much relevant information as possible.

::DEVELOPER

Pritchard Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 CENTIPEDE

:: MORE INFORMATION

Citation

Pique-Regi RP, Degner JF, Pai AA, Gaffney DG, Gilad Y, Pritchard JK.
Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data“,
Accepted to Genome Research.

 Posted by at 9:00 am
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