The ENCODE Project Consortium, Moore JE*, Purcaro MJ,* Pratt HE*, Epstein CB*, Shoresh N*, Adrian J*, Kawli T*, Davis CA*, Dobin A*, Kaul R*, Halow J*, Nostrand EL*, Freese P*, Gorkin DU*, Shen Y*, He Y*, Mackiewicz M*, Pauli-Behn F*, Williams BA, Keller CA, Zhang X, Huey J, Dickel DE, Snetkova V, Wei X, Wang X, Rivera-Mulia JC, Rozowsky J, Zhang J, Chhetri SB, Zhang J, Victorsen A, White KP, Visel A, Yeo GW, Burge CB, Lécuyer E, Gilbert DM, Dekker J, Rinn J, Mendenhall EM, Ecker JR, Kellis M, Klein RJ, Noble WS, Kundaje A, Guigó R, Farnham PJ, Cherry JM^†, Myers RM^†, Ren B^†, Graveley BR^†, Gerstein MB^†, Pennacchio LA^†, Snyder MP^†, Bernstein BE^†, Wold B^†, Hardison RC^†, Gingeras TR^†, Stamatoyannopoulos JA^†, Weng Z^†,

Nature. 2020 July 30, *authors contributed equally to this work

The ENCyclopedia of DNA Elements (ENCODE) Project launched in 2003 with the long-term goal of developing a comprehensive map of functional elements in the human genome. These included genes, biochemical regions associated with gene regulation (for example, transcription factor binding sites, open chromatin, and histone marks) and transcript isoforms. The marks serve as sites for cis candidate regulatory elements (cCREs) that may serve functional roles in regulating gene expression . The project has been extended to model organisms, particularly the mouse. In the third phase of ENCODE, nearly a million and more than 300,000 cCRE annotations have been generated for human and mouse, respectively, and these have provided a valuable resource for the scientific community.

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Moore JE, Pratt HE, Purcaro MJ, Weng Z.

Genome Biol. 2020 Jan 22

Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes. To facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactions. We use BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the TargetFinder and PEP supervised learning methods. We find that while TargetFinder is the best-performing method, it is only modestly better than a baseline distance method for most benchmark datasets when trained and tested with the same cell type and that TargetFinder often does not outperform the distance method when applied across cell types. Our results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.

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Chen W*, Moore J*, Ozadam H, Shulha HP, Rhind N, Weng Z, Moore MJ.

Cell. 2018 May 03, *authors contributed equally to this work

Full understanding of eukaryotic transcriptomes and how they respond to different conditions requires deep knowledge of all sites of intron excision. Although RNA sequencing (RNA-seq) provides much of this information, the low abundance of many spliced transcripts (often due to their rapid cytoplasmic decay) limits the ability of RNA-seq alone to reveal the full repertoire of spliced species. Here, we present “spliceosome profiling,” a strategy based on deep sequencing of RNAs co-purifying with late-stage spliceosomes. Spliceosome profiling allows for unambiguous mapping of intron ends to single-nucleotide resolution and branchpoint identification at unprecedented depths. Our data reveal hundreds of new introns in S. pombe and numerous others that were previously misannotated. By providing a means to directly interrogate sites of spliceosome assembly and catalysis genome-wide, spliceosome profiling promises to transform our understanding of RNA processing in the nucleus, much as ribosome profiling has transformed our understanding mRNA translation in the cytoplasm.

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