Bayesian modeling of ChIP-chip data using latent variables

A boosting approach for motif modeling using ChIP-chip data

MOTIVATION Building an accurate binding model for a transcription factor (TF) is essential to differentiate its true binding targets from those spurious ones. This is an important step toward understanding gene regulation. RESULTS This paper describes a boosting approach to modeling TF-DNA binding. Different from the widely used weight matrix model, which predicts TF-DNA binding based on a li...

Bayesian modeling of embryonic growth using latent variables.

In a growth model, individuals move progressively through a series of states in which each state is indicative of developmental status. Interest lies in estimating the rate of progression through each state while incorporating covariates that might affect the transition rates. We develop a Bayesian discrete-time multistate growth model for inference from cross-sectional data with unknown initia...

Analyzing ChIP-chip Data Using Bioconductor

The interpretation of ChIP-chip data poses two computational challenges: first, what can be termed primary statistical analysis, which includes quality assessment, data normalization and transformation, and the calling of regions of interest; second, integrative bioinformatic analysis, which interprets the data in the context of existing genome annotation and of related experimental results obt...

Supplement: Analyzing ChIP-chip data using Bioconductor

Genome-Wide Modeling of Transcription Preinitiation Complex Disassembly Mechanisms using ChIP-chip Data

Apparent occupancy levels of proteins bound to DNA in vivo can now be routinely measured on a genomic scale. A challenge in relating these occupancy levels to assembly mechanisms that are defined with biochemically isolated components lies in the veracity of assumptions made regarding the in vivo system. Assumptions regarding behavior of molecules in vivo can neither be proven true nor false, a...