title: Uncovering transcription factor modules using one- and three-dimensional analyses.
abstract: Transcriptional regulation is a critical mediator of many normal cellular processes, as well as disease progression. Transcription factors (TFs) often co-localize at cis-regulatory elements on the DNA, form protein complexes, and collaboratively regulate gene expression. Machine learning and Bayesian approaches have been used to identify TF modules in a one-dimensional context. However, recent studies using high throughput technologies have shown that TF interactions should also be considered in three-dimensional nuclear space. Here, we describe methods for identifying TF modules and discuss how moving from a one-dimensional to a three-dimensional paradigm, along with integrated experimental and computational approaches, can lead to a better understanding of TF association networks.
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: Xun
authors.initials: X
authors.lastname: Lan
authors.forename: Peggy J
authors.initials: PJ
authors.lastname: Farnham
authors.forename: Victor X
authors.initials: VX
authors.lastname: Jin
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D046912:Multiprotein Complexes / Q000502:physiology*; D014157:Transcription Factors / Q000502:physiology*
chemical_list: D046912:Multiprotein Complexes; D014157:Transcription Factors