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.
- Windows / Linux / MacOS
- R package
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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.