Summary of Encode Phase 3 Data Production

The ENCODE Consortium has produced data on three main aspects of genome activity— transcriptomes, DNA-based regulatory elements for transcription and replication, and RNA-based elements for post-transcriptional regulation. Phase III greatly expanded the number of experiments in each category and released 4,903 experiments (3,797 on human and 1,106 on mouse; see Figure II-1 on page 87). Table II-1 on page 78 summarizes these experiments by category. We define an experiment as the application of a genomic assay (such as ChIP-seq, RNA-seq, DNase-seq, or ATAC-seq) to a particular biosample type (such as a tissue, a cell line, primary cells, or stem cells). In this section, we summarize the new assays and highlight the results of Phase III data production.

New polyA and short RNA transcriptome data production has focused on primary cells from different body locations and various embryological origins. Single-cell long- RNA-seq was further developed for laser-capture microdissection of human and mouse brain tissues. To better define full-length transcripts, we analyzed captured RNAs using long-read sequencing. This effort, in collaboration with the GENCODE project,

improved the annotations of gene and transcript structures for 14,667 human and 8,708 mouse long noncoding RNAs (Lagarde et al., in review).

A new 5´-complete cDNA sequencing assay called RAMPAGE quantifies gene expression, identifies promoter locations, and assigns 5´ capped termini to their

corresponding RNA isoforms (Batut et al. 2013). RAMPAGE yields data at single- nucleotide resolution and is more accurate than RNA-seq for quantifying expression (Batut et al. 2013)—advances which enable it to improve transcription start site (TSS) annotation and transcript quantification. For example, the gene ARHGAP23, which encodes Rho GTPase-activating protein 23, has 12 GENCODE-annotated transcripts and 11 different TSSs. RAMPAGE data revealed a novel TSS in the testis (Figure II-2a on page 88), located 9.2 kb upstream of the nearest annotated TSS, and another novel TSS in exon 7 specific to the spleen (Figure II-2b on page 88). As another example, two different TSSs, 824 bp apart, are annotated by GENCODE V26 and UCSC for EP300, which encodes a widely studied histone acetyltransferase important for enhancer activity. RAMPAGE data across six cell and tissue types showed that although both TSSs are active, one TSS is used far more frequently than the other (Figure II-3 on page 89).

The coverage of noncoding, biochemically marked DNA elements, many of which have potential regulatory functions, has been greatly expanded during ENCODE Phase III. We completed 163 new DNase accessibility maps, including deep sequencing DNase-seq datasets on hundreds of cell and tissue samples, thus facilitating the prediction of regulatory protein occupancy by footprinting (Hesselberth et al. 2009). The ATAC-seq assay (Buenrostro et al. 2013), which assesses chromatin accessibility via insertion by the Tn5 transposome, was conducted on tens of human and mouse tissues and primary cells. We expanded the application of ChIP-seq to map the locations of modified histones, histone variants, and 33 chromatin regulators and modifiers in a carefully selected collection of five human cell lines—K562, H1, GM12878, HepG2, and A549. Over 600

ChIP-seq experiments were completed in Phase III for 493 different transcription factors (TFs) in at least one cell type (1,622 experiments on 549 different TFs in Phases II and III combined). For these ChIP-seq experiments, we used either TF-specific antibodies or epitope-tagged TFs created by BAC transfections or CRISPR/Cas9 genome editing. ChIA-PET of Rad21 and CTCF, which are involved in the nuclear organization, along with Hi-C experiments, provide 3D linkage data that include many regulatory regions and cognate target genes. Through the ENCODE Portal (encodeproject.org/antibodies/), we provide quality metrics for all datasets as well as detailed information about the

antibodies used in our experiments to help users evaluate and use the data most effectively.

DNA replication timing provides insights into gene regulation and spatiotemporal genome compartmentalization (Gilbert 2002). We measured replication timing during fate commitment of human embryonic stem cells, thus yielding 84 datasets for 26 cell types representing the embryonic layers endoderm, mesoderm, ectoderm, and neural crest (Rivera-Mulia et al. 2015) (see Figure II-4 on page 90). Because replication timing differs across cell types, we expected that clustering of these datasets would recapitulate their developmental lineages, and that was indeed observed (see Figure II-5 on page 91).

The mouse component of ENCODE Phase III focused on embryo development at daily intervals between embryonic day 10.5 (e10.5) and postnatal day 0 (p0), with 6-12 tissues sampled per day. RNA-seq of polyA RNAs and miRNAs, ChIP-seq for eight histone modifications, ATAC-seq, and whole-genome bisulfite sequencing were

performed on all the samples of the mouse embryonic developmental series, augmented by DNase-seq and ChIP-seq of three TFs in selected samples.

A new project in ENCODE Phase III was to identify and characterize functional RNA elements bound by RNA-binding proteins (RBPs) (van Nostrand et al., in

preparation). Four types of related data were generated: RIP-seq and enhanced UV crosslinking and immunoprecipitation of RBPs followed by sequencing (eCLIP-seq) (Van Nostrand et al. 2016) to identify bound RNAs in vivo and pinpoint the portions of these RNAs involved in binding interactions; RNA-seq on cells depleted of specific RBPs by shRNA or CRISPR; RNA Bind-N-Seq (RBNS)(Lambert et al. 2014) to determine the relative binding affinity of RBPs in vitro for all possible RNA sequences; and subcellular localization of RBPs by immunostaining.

The breadth of our RBP data enables integrative analyses to relate genetic variation to RBP regulation. For the 18 RBPs with eCLIP, RBNS and, RBP-knockdown RNA-seq data, we identified 26 variants from the Exome Aggregation Consortium (ExAC)(Lek et al. 2016) that overlapped an eCLIP peak, disrupted an RBNS motif, and produced a splicing change upon knockdown of the corresponding RBP (van Nostrand et al., in preparation). For example, intron 66 of UTRN (dystrophin-related protein 1) harbors an RBFOX2 eCLIP peak downstream of an alternatively spliced exon (Figure II-6c on page 92), which overlaps an ExAC variant (Lek et al. 2016). This G→C variant disrupts the RBFOX2 binding motif (GCAUG) at the first position. RBNS data reveal that this variant substantially changes the RBFOX2 binding site—the top 5-mer has an enrichment value of 13.58 for the major G allele but 0.89 for the C variant (Figure II-6d

on page 92), thus suggesting that the mutation disrupts RBFOX2 binding in vivo. To determine whether the disruption of RBFOX2 binding would alter splicing, we performed RNA-seq on HepG2 cells after knocking down RBFOX2. In wild-type cells, the upstream exon was included in 87% of messages, whereas the inclusion was decreased to 28% in the RBFOX2 knockdown cells (Figure II-6c on page 92). Taken together, these data argue that this G→C variant disrupts RBFOX2 binding, leading to decreased inclusion of the upstream exon in over half of UTRN messages, and resulting in an altered

composition of protein isoforms. Overall, the actual number of variants that influence RNA metabolism is larger than the 26 ExAC variants identified in this way, because they may affect aspects of RNA biology other than splicing.