CENTIPEDE fits a bayesian hierarchical mixture model to learn TF-specific distribution of experimental data on a particular cell-type for a set of candidate binding sites described by a motif.
This is a practical tutorial for running CENTIPEDE with DNase-Seq data. It explains how to prepare the data and how to run the analysis. The goal is to predict if a putative transcription factor binding site is actually bound or not. For details about the statistical models underlying the methods, please see (Pique-Regi, et al. 2011).
Read the tutorial online or download the PDF:
This repository has functions to ease the use of CENTIPEDE:
centipede_data()converts data to the format required for CENTIPEDE.
parse_region()parses a string like "chr1:123-456".
read_bedGraph()reads a bedGraph file with 4 columns: chrom, start, end, score.
read_fimo()reads a text file output by FIMO and selects sites that meet a significance threshold.
I also provide example data that you can use to follow the tutorial:
cenis a list with two items:
cen$matis a matrix of read-start counts for 3,337 genomic regions.
cen$regionsis a dataframe describing those regions.
site_consis a vector with mean conservation scores for the 3,337 regions, computed across 100 vertebrates.
Install CENTIPEDE by running this in your shell (not within an R session):
wget http://download.r-forge.r-project.org/src/contrib/CENTIPEDE_1.2.tar.gz R CMD INSTALL CENTIPEDE_1.2.tar.gz
Next, install the tutorial package:
# This command didn't work for me. # install.packages("CENTIPEDE", repos="http://R-Forge.R-project.org") install.packages("devtools") devtools::install_github("slowkow/CENTIPEDE.tutorial")
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