Integrative annotation of chromatin elements from ENCODE data

Abstract

The ENCODE Project has generated a wealth of experimental information mapping diverse chromatin properties in several human cell lines. Although each such data track is independently informative toward the annotation of regulatory elements, their interrelations contain much richer information for the systematic annotation of regulatory elements. To uncover these interrelations and to generate an interpretable summary of the massive datasets of the ENCODE Project, we apply unsupervised learning methodologies, converting dozens of chromatin datasets into discrete annotation maps of regulatory regions and other chromatin elements across the human genome. These methods rediscover and summarize diverse aspects of chromatin architecture, elucidate the interplay between chromatin activity and RNA transcription, and reveal that a large proportion of the genome lies in a quiescent state, even across multiple cell types. The resulting annotation of non-coding regulatory elements correlate strongly with mammalian evolutionary constraint, and provide an unbiased approach for evaluating metrics of evolutionary constraint in human. Lastly, we use the regulatory annotations to revisit previously uncharacterized disease-associated loci, resulting in focused, testable hypotheses through the lens of the chromatin landscape.

Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants

“…Our analysis of these cell type-specific enhancer clusters shows GWAS SNP enrichment (SI Appendix, Fig. S16), supporting the concept that disease susceptibility is at least partially mediated by variation in cell type-specific enhancers (2,6,7,(24)(25)(26).…”

“…Regulatory elements such as promoters, enhancers, insulators, transcribed, and repressed regions are marked by distinct patterns of histone modifications (1), including histone H3 lysine 27 acetylation (H3K27ac), H3K27 trimethylation (H3K27me3), H3K36me3, H3K4 monomethylation (H3K4me1), H3K4me3, and the CCCTC-binding factor (CTCF). Systematic chromatin state identification has recently emerged as a powerful technique to interpret and compare regulatory landscapes within and between cell types (2)(3)(4)(5)(6)(7). Such methods use an unsupervised approach to identify recurrent combinations of histone modifications across the genome, thereby producing a map of representative chromatin states that are likely to be biologically relevant.…”

Genome-wide map of regulatory interactions in the human genome

“…Specific information for each data set and each factor can be found in Supplemental Tables S2 and S3. To better understand the nature of each interaction, we compared our ChIA-PET results to the extensive list of elements defined by the ENCODE Project Consortium (2012). We first defined a set of ''regulatory elements'' in K562 cells by intersecting chromatin state calls as determined by Hoffman et al (2013) and DNase I hypersensitive sites (DHSs) as defined by Thurman et al (2012). Each DHS was assigned to a single chromatin state based in the chromatin state it overlapped, with the most resulting in 169,871 annotated regulatory elements and 32,395 undefined elements that did not overlap any chromatin state calls (Table 1; see Supplemental Methods for details).…”

“…interactions (Kagey et al 2010;Hoffman et al 2013;Merkenschlager and Odom 2013;Phillips-Cremins et al 2013). CTCF, a canonical insulator protein, which is found at the majority (96.7%) of RAD21 sites in K562 cells, has also recently been implicated in long-range interactions (Handoko et al 2011).…”

“…DHSs for K562 were intersected with ''combined'' chromatin state calls via integration of both ChromHMM and Segway outputs as determined by Hoffman et al (2013). DHSs were annotated as the chromatin state that they overlapped the most.…”