Classifying cREs in the Registry

Gene catalogues such as GENCODE define gene models irrespective of their varying expression levels and alternative transcripts across different cell types. By analogy, we provide a general, "cell type agnostic" classification of cREs based on the maximal Z- score of each feature across all cell types with ENCODE and Roadmap data, abbreviated henceforth as max-Z. The goal is to provide a useful overview of the entire cRE

landscape by integrating all input cell types for the four epigenetic features. We then classify cREs according to these four features at two levels of detail—the state classification and group classification—described below in turn.

As described above, all cREs must have a high DNase max-Z and furthermore must have a high max-Z for least one of three epigenetic signals—H3K4me3, H3K27ac, or CTCF. The state classification is simply a delineation of all possible combinations of high (max-Z ≥ 1.64) or low (max-Z < 1.64) H3K4me3, H3K27ac, and CTCF signals, with each combination called a state. This classification captures the fact that while some cREs are marked by just one high signal (41% of human and 59% of mouse cRE), many cREs have two or three high signals (Figure II-14 on page 100). Because the all-low state is not allowed, a cRE can adopt one of seven states. Furthermore, each cRE is classified as being proximal or distal (within or outside the ±2 kb window) to the nearest

states a cRE is in and is displayed in SCREEN with a color code alongside the

information on TSS proximity and whether the cRE is supported by concordant signals from the same cell type.

The group classification is an abbreviated abstraction that assigns each cRE to a group according to its biochemically dominant signature. As reported above for

transgenic mouse enhancer assays, the intensity of biochemical signals is positively but modestly predictive of functional enhancer activity. We define broad, mutually exclusive groups of elements in the expectation that they will be enriched in the respective

promoter-like, enhancer-like, or CTCF-mediated functions. We currently use three groups assigned in the following order (Figure II-13 on page 99):

1. cREs with promoter-like signatures (cRE-PLS) must have high H3K4me3 max- Zs. If they are TSS-distal, they must also have low H3K27ac max-Zs.

2. cREs with enhancer-like signatures (cRE-ELS) must have high H3K27ac max- Zs. If they are TSS-proximal, they must also have low H3K4me3 max-Zs.

3. CTCF-only cREs are the remaining cREs. They do not fall into either of the first two groups and thus by definition must have high CTCF max-Zs to qualify as cREs.

Classifications are assigned in the above order; thus, a cRE possessing high histone mark signals and a high CTCF signal will be classified as either PLS or ELS. This simplified classification scheme is designed to give users a first-cut idea of the most likely function for each cRE, although we are acutely aware that regulatory elements are known to play multiple roles. For example, the IFITM3 promoter is bound by CTCF and a SNP that interrupts the binding is associated with severe influenza risk in humans

(Allen et al. 2017). Massively parallel reporter assays indicate that some promoters also have enhancer activities while some enhancers also have promoter activities (Nguyen et al. 2016). A tiling-deletion-based CRISPR screen of the 2-Mb POU5F1 locus identified 45 cis-regulatory elements, among which 17 are promoters of functionally unrelated genes (Diao et al. 2017). CapStarr-seq data revealed that 2-3% of the coding-gene promoters display enhancer activity in a given mammalian cell line (Diao et al. 2017). Thus, the group classification is intended to ease analysis and simplify discussion, and we emphasize that many cREs belong to multiple groups.

As currently formulated, the Registry does not explicitly define negative elements, but we aim to include them in the next version of the Registry. We note that some of the cREs in the Registry may be repressive in the appropriate cellular contexts. Repression can be achieved through diverse mechanisms: binding a sequence-specific repressor, replacing the binding of a strongly activating transcription factor by a weakly activating one, competing for transcription factors with low abundance, attracting repressive epigenetic regulator such as Polycomb group proteins, or establishing DNA methylation. Indeed, depending on the cellular context, 25% of Drosophila

developmental enhancers can also function as Polycomb response elements, silencing transcription in a Polycomb-dependent manner (Erceg et al. 2017). Such findings underscore the notion that many cREs belong to multiple groups.

We analyzed the fraction of the genome covered by each group of cREs,

considering only regions of the genome which are mappable by 36-nt long sequences in DNase-seq experiments (~2.65 billion bases for human and 2.29 billion bases for mouse).

In total, 20.8% of the mappable genome is covered by cREs (4.2% by cREs-PLS, 15.9% by cREs-ELS, and 0.7% by CTCF-only cREs) and 8.8% of the mappable mouse genome is covered by cREs. The lower coverage for mouse is due to the smaller number of cell types with data available with which to define cREs.

The state classification scheme extends naturally to a specific cell type, by characterizing the biochemical activity of each cRE with the DNase, H3K4me3, H3K27ac, and CTCF data in that cell type. All cREs with low DNase Z-scores in a particular cell type are bundled into one “inactive” state for that cell type; the remaining “active” cREs are divided into eight states according to their H3K4me3, H3K27ac, and CTCF Z-scores.

The group classification scheme also extends naturally to a specific cell type, but two additional groups are needed: an inactive group, containing all cREs with low DNase Z-scores, and a DNase-only group, containing cREs with high DNase Z-scores but low H3K4me3, low H3K27ac, and low CTCF Z-scores within that cell type.

Using GM12878 lymphoblastoid cells as an example, Figure II-15 on page 101 summarises the cREs states, with each state further stratified by TSS proximity. The bar graph reveals that cREs with high H3K4me3 are mostly TSS-proximal, regardless of whether or not they have high H3K27ac or CTCF, while cREs with low H3K4me3 are mostly TSS-distal. We used additional ChIP-seq data for three factors in GM12878 to evaluate the group classification of cREs in this cell type (Figure II-14 on page 100): RNA polymerase II (POL2), which binds most active promoters; EP300, a histone acetyltransferase that binds many enhancers; and RAD21, another component of the

cohesin complex which includes CTCF. cREs-PLS have the highest median Pol II signal, cREs-ELS have the highest median EP300 signal, and CTCF-only cREs have the highest median RAD21 signal.