Download Prior Knowledge-based Mammalian Gene Regulatory Network

Prior Knowledge-based Mammalian Gene Regulatory Network
Inference: From Chromatin Dynamics To Multilineage Differentiation
Xi
1
Chen ,
Christoph
1
Hafemeister ,
Richard
1,2
Bonneau
1.  Center for Genomics and Systems Biology, Department of Biology, New York University
2.  Department of Computer Science, Courant Institute of Mathematical Sciences, New York University
Summary
During metazoan animal development, changes in
chromatin states give rise to diverse patterns of gene
expression that direct the differentiation of progenitor
cells into various tissues and organs. We use
information from TF binding motifs and chromatin
accessibility data to infer cell-type specific TF
occupancy, and incorporate inferred TF-target
interactions as prior knowledge to learn mammalian
gene regulatory networks alongside compendia of gene
expression data.
We use a simple ordinary differential equation"
model where transcription factors affect "
transcription rate and mRNA degradation rate is "
proportional to mRNA level, and develop a Bayesian"
framework to find a parsimonious solution to the"
regression problem by incorporating network priors."
Robustness to false prior information!
Mixture of Negative Binomial Distributions!
Performance with increasing noise in priors: To test the robustness of our methods to incorrect prior "
information, we consider half of the gold standard as true prior interactions, and added a varying "
number of random false prior interactions. In general high weight parameters make the methods more"
susceptible to noise, but performance throughout all noise levels is still better than any method without"
prior known interactions. Reference: Greenfield, A., Hafemeister, C., & Bonneau, R. (2013). Robust data-driven !
incorporation of prior knowledge into the inference of dynamic regulatory networks. Bioinformatics (Oxford, England), !
29(8), 1060–7. !
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Classify motif sites based on chromatin!
accessibility using the mixture model !
putative CTCF binding sites!
Clustering accessibility landscape recovers patterns of multilineage !
differentiation!