title: Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression.
abstract: The genomic neighborhood of a gene influences its activity, a behavior that is attributable in part to domain-scale regulation. Previous genomic studies have identified many types of regulatory domains. However, due to the difficulty of integrating genomics data sets, the relationships among these domain types are poorly understood. Semi-automated genome annotation (SAGA) algorithms facilitate human interpretation of heterogeneous collections of genomics data by simultaneously partitioning the human genome and assigning labels to the resulting genomic segments. However, existing SAGA methods cannot integrate inherently pairwise chromatin conformation data. We developed a new computational method, called graph-based regularization (GBR), for expressing a pairwise prior that encourages certain pairs of genomic loci to receive the same label in a genome annotation. We used GBR to exploit chromatin conformation information during genome annotation by encouraging positions that are close in 3D to occupy the same type of domain. Using this approach, we produced a model of chromatin domains in eight human cell types, thereby revealing the relationships among known domain types. Through this model, we identified clusters of tightly regulated genes expressed in only a small number of cell types, which we term "specific expression domains." We found that domain boundaries marked by promoters and CTCF motifs are consistent between cell types even when domain activity changes. Finally, we showed that GBR can be used to transfer information from well-studied cell types to less well-characterized cell types during genome annotation, making it possible to produce high-quality annotations of the hundreds of cell types with limited available data.
authors: Department of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA.
authors: Maxwell W
authors: MW
authors: Libbrecht
authors: Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
authors: Ferhat
authors: F
authors: Ay
authors: Princess Margaret Cancer Centre, University of Toronto, ON M5G 1L7, Canada Department of Medical Biophysics, University of Toronto, ON M5G 1L7, Canada.
authors: Michael M
authors: MM
authors: Hoffman
authors: Department of Biological Science, The Florida State University, Tallahassee, Florida 32304, USA.
authors: David M
authors: DM
authors: Gilbert
authors: Department of Electrical Engineering, University of Washington, Seattle, Washington 98195, USA.
authors: Jeffrey A
authors: JA
authors: Bilmes
authors: Department of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
authors: William Stafford
authors: WS
authors: Noble
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs / Q000235:genetics; D045744:Cell Line, Tumor; D002843:Chromatin / Q000235:genetics* / Q000378:metabolism; D022004:Chromosome Structures; D019295:Computational Biology / Q000379:methods*; D015894:Genome, Human / Q000235:genetics; D023281:Genomics / Q000379:methods*; D006367:HeLa Cells; D056945:Hep G2 Cells; D061307:Human Umbilical Vein Endothelial Cells; D006801:Humans; D008968:Molecular Conformation; D058977:Molecular Sequence Annotation / Q000379:methods*; D011401:Promoter Regions, Genetic / Q000235:genetics
chemical_list: D002843:Chromatin