Linking enhancers to target genes
We have developed leading computational models to predict which enhancers which genes in the human genome, and constructed initial maps across thousands of cell types.
Explore enhancer maps across 1000s of cell types here:
Apply model to build enhancer maps in new cell types:
ENCODE-rE2G model – For cell types in which you have bulk ATAC-seq or DNase-seq, plus optionally H3K27ac and Hi-C
scE2G model – For single-cell ATAC-seq or multiomic single-cell RNA+ATAC-seq
ABC model – The Activity-by-Contact (ABC) model is now incorporated into ENCODE-rE2G and scE2G
Evaluate new models using genomic perturbation data:
CRISPR benchmarking pipeline — How well do models predict results of CRISPR perturbations to distal elements?
eQTL benchmarking pipeline — How well do models link fine-mapped eQTL variants to target eGenes?
GWAS benchmarking pipeline — How well do model link fine-mapped GWAS variants to high-confidence target genes?
Activity-by-Contact (ABC) Model
The Activity-by-Contact (ABC) Model predicts which enhancers regulate which genes in the genome, based on estimating enhancer activity and enhancer-promoter contact frequency from epigenomic datasets.
Publications:
Fulco and Nasser et al. Nature Genetics (2019). Initial description and validation of the ABC model. See Supplementary Methods for details of the implementation.
Nasser et al. Nature (2021): Genome-wide enhancer maps connect risk variants to disease genes. Produces ABC predictions across 131 biosamples and describes their utility in interpreting fine-mapped GWAS variants.
Data:
ABC predictions in 131 cell types and tissues (all element-gene connections with ABC scores >= 0.015; see Nasser et al. Nature (2021)). https://mitra.stanford.edu/engreitz/oak/public/Nasser2021/AllPredictions.AvgHiC.ABC0.015.minus150.ForABCPaperV3.txt.gz FTP (requires FTP client): ftp://ftp.broadinstitute.org/outgoing/lincRNA/ABC/AllPredictions.AvgHiC.ABC0.015.minus150.ForABCPaperV3.txt.gz
ABC pipeline outputs for 131 cell types and tissues (see Nasser et al. Nature (2021)). FTP (requires FTP client): ftp://ftp.broadinstitute.org/outgoing/lincRNA/ABC/Nasser2021-Full-ABC-Output/
Web browser for ABC predictions in 131 biosamples (by Fritz Lekschas)
Curated CRISPR datasets for validating predictive models of enhancer-gene regulation (see Nasser et al. Nature (2021))
Protocols and Code:
CRISPRi tiling and FlowFISH
CRISPRi tiling is a method to characterize the functions of noncoding elements on the expression of a gene of interest. It combines CRISPR interference (CRISPRi) with various gene-centric screening tools, including RNA FlowFISH.
We have applied these tools to dissect enhancer-promoter regulation across many genes and cell types.
Publications and Data:
Fulco and Nasser et al. Nature Genetics (2019). Description of the CRISPRi-FlowFISH method, and application to comprehensively test all putative regulatory elements around 30 genes in K562 cells.
Fulco et al. Science (2016). Initial description of the CRISPRi tiling approach, and application to tile gRNAs across ~1.2 Mb of sequence around two genes of interest (GATA1 and MYC).
Protocols:
RNA Antisense Purification (RAP)
RNA Antisense Purification (RAP) is a biochemical purification method that allows unbiased identification of the DNA, RNA, or proteins associated with an RNA of interest. RAP involves crosslinking cells to fix RNA interactions, capturing the RNA of interest using long (60-120-nucleotide) antisense oligos, and identifying associated biomolecules using DNA sequencing, RNA sequencing, or proteomics.
We have applied these tools to discover how lncRNAs localize to genomic DNA using the 3D organization of the genome, and how RNA-RNA interactions can guide lncRNA localization to chromatin.
Publications and Data:
Engreitz et al. Cell (2014). Description of RAP-RNA, and updated RAP-DNA protocol. RAP-DNA and RAP-RNA for U1 snRNA, Malat1 lncRNA, and other small noncoding RNAs.
Engreitz et al. Science (2013). Initial description of the RAP-DNA protocol, and application to study how Xist spreads across the X chromosome.
Protocols and Code:
RAP-DNA and RAP-RNA were developed together with the Mitch Guttman Lab.