Abstract
Mounting evidence supports the idea that transcriptional patterns serve as more specific identifiers of active enhancers than histone marks; however, the optimal strategy to identify active enhancers both experimentally and computationally has not been determined. Here, we compared 13 genome-wide RNA sequencing (RNA-seq) assays in K562 cells and show that nuclear run-on followed by cap-selection assay (GRO/PRO-cap) has advantages in enhancer RNA detection and active enhancer identification. We also introduce a tool, peak identifier for nascent transcript starts (PINTS), to identify active promoters and enhancers genome wide and pinpoint the precise location of 5′ transcription start sites. Finally, we compiled a comprehensive enhancer candidate compendium based on the detected enhancer RNA (eRNA) transcription start sites (TSSs) available in 120 cell and tissue types, which can be accessed at https://pints.yulab.org. With knowledge of the best available assays and pipelines, this large-scale annotation of candidate enhancers will pave the way for selection and characterization of their functions in a time- and labor-efficient manner.
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Data availability
Processed TRE calls are publicly accessible via our web portal (https://pints.yulab.org). Data that support the findings of this study are available within the paper and its Supplementary information files. All sequencing data analyzed in this study were retrieved from public databases (NCBI GEO and ENCODE portal); lists of accessions are available in Supplementary Tables 1 and 4. Source data are provided with this paper.
Code availability
The source code of PINTS is publicly available at https://github.com/hyulab/PINTS; scripts and pipelines used to generate results reported in this study can be retrieved from https://github.com/hyulab/PINTS_analysis.
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Acknowledgements
Computation was performed on a cluster administered by the Biotechnology Resource Center at Cornell University. We thank members of the Yu and Lis laboratories and the ENCODE Consortium (specifically A. Mortazavi, M. Ljungman and J. E. Moore) for helpful discussions and guidance; and H. Zhu for her suggestions on concept visualization. This work was supported by grants from the National Institutes of Health (no. UM1HG009393 to J.T.L. and H.Y. and nos. R01DK115398, R01DK127778 and R01HD082568 to H.Y.). L.Y. was supported by the Cornell Presidential Life Sciences Fellowship.
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Extended data
Extended Data Fig. 1 An extended evaluation of eRNA detection sensitivity of different assays.
a and c are the extended versions for Fig. 2a,b, respectively. a and b show the capability of different assays to capture previously identified enhancers. The color of stacked bars indicates the detection of eRNAs originated from either one or both strands of the enhancer loci. The transparency level shows the number of reads for an enhancer locus to be considered as covered. The top track in a is derived from the CRISPR or CRISPRi based reference set (n=803), the bottom track is derived from consensus loci validated by STARR-seq and MPRA (n=550). b, Sensitivity evaluated in the other cell line, GM12878, with orientation-independent enhancers identified from previous studies (n=3,544)6,46. c, Differences in read coverage among stable (n=13,861) and unstable (n=6,380) transcripts. The error bars in the top track show the extrema of effect sizes (n=5,000). The center dots, box limits, and whiskers in the bottom track of c denote the median, upper and lower quartiles, and 1.5× interquartile range, respectively.
Extended Data Fig. 2 Effect of technical artifacts on eRNA capture.
a, A new strategy for evaluating strand specificity without the interference from promoter-upstream transcripts (PROMPTs)81. Red and blue colors indicate reads’ mapping direction; the highlighted (yellow) region indicates a previously validated82 PROMPT. Only the first exon in green was used for evaluation. b, Strand specificities of three stranded and unstranded RNA-seq libraries with our strategy. The p-value was estimated by a two-sided t test; c, Strand specificity for all libraries evaluated with our strategy. Values and error bars represent the mean and SD. n=2 (GRO-cap, CoPRO, csRNA-seq, PRO-seq, GRO-seq, mNET-seq), n=3 (STRIPE-seq), n=4 (CAGE and RAMPAGE), n=8 (BruUV-seq, total RNA-seq), n=9 (Bru-seq). d, Distribution of 3-mers at flush end sites83 for RIP-seq and TGIRT-seq. The dashed red lines stand for the frequency of RT3-mers (sequence identical to the last three nts for the RT primer [for RIP-seq] or the 3′ adapter [for TGIRT-seq]) in the genome. e, Log odds ratios (LORs) of observed RT3-mer at flushing end sites versus in the genome (top) and internal priming rates (bottom) of assays when the internal priming could be detected from the sequencing data. f, The overlap between enhancers in the RppH library (Capped+Uncapped as ‘C + U’) that are also covered in the Capped library (C). The x-axis shows the minimum number of reads required for an enhancer locus to be considered as covered. g, Difference of log-transformed read counts between the capped (C) and RppH (C + U) libraries. The effect size was measured by Cohen’s d. In the box plot, the center dots, box limits, and whiskers denote the median, upper and lower quartiles, and 1.5× interquartile range, respectively. h, Pearson’s r of log-transformed reads from promoters of expressed transcripts (TPM > 5) was quantified using PRO-seq and POLR2A ChIP-exo. n=4,747 (low), n=9,058 (medium), and n=2,470 (high).
Extended Data Fig. 3 Analyses of factors affecting assays’ sensitivity in detecting eRNAs.
a is the extended version for Fig. 3a. b, An example shows that divergent transcripts detected by NT-assays can originate from two overlapping genes (MMP23B and SLC35E2B) instead of from a regulatory element. Sequencing reads were RPM-normalized. c, Proportion of mappable reads from different assays originated from various abundant RNA families. d, Effects of rRNA depletion in eRNA enrichment. For each category, three downsampled libraries were included. BruUV-seq libraries from a previously published study84 were used for this analysis. The p-value for rRNA percentage was calculated by two proportions z test (two-sided, p-value: 0); the p-value for true enhancer coverage was calculated by McNemar’s test (two-sided, p-value: 2.1 × 10−25). Values and error bars represent the mean and SD. e, The distribution of sequencing reads (in RPM) around GENCODE-annotated splicing junction sites. The shaded area indicates the 95% confidence interval of mean values estimated via bootstrap.
Extended Data Fig. 4 Extended evaluations of assays’ specificity.
a, Epigenomic and transcription factor binding profiles for the enhancer and non-enhancer sets. For H3K27ac and CTCF, the profiles are presented as fold-changes over control; for DHS, the profile is shown as normalized sequencing depth. Solid lines represent mean densities, and shades depict the 95% confidence interval of mean values estimated via bootstrap. KE: known enhancers; NE: non-enhancers. b Signal-to-noise ratios evaluated in K562. n=803 for known enhancers, n=6,777 for non-enhancers. c, Signal-to-noise ratios evaluated in GM12878. n=3,544 (Known enhancers), and n=153,809 (Non-enhancers). For b and c, 10,000 bootstrapped samples were used for calculating the fold enrichment (FE). The center dots, box limits, and whiskers in b and c denote the median, upper and lower quartiles, and 1.5× interquartile range, respectively. d, False discovery rates estimated by the overlap between the top 5,000, 10,000, 20,000, and 100,000 genomic bins and the true and non-enhancer sets. Downsampled libraries were used (n=3); values and error bars represent the mean and SD.
Extended Data Fig. 5 Assessments of transcript unit prediction and schematic illustration of PINTS.
a, The consistencies vary greatly between transcription units annotated in GENCODE (Annot.) and those predicted by different tools58,59,85 (Pred.). Lines in the violin plot indicate the 25th, 50th, and 75th quartiles, respectively. b, Schematic plot of PINTS. i, Improvement of TSS identification resolution by focusing only on read ends and using zero-inflated Poisson (ZIP) models to fit local background to address the substantially increased sparsity of signals. The thin grey lines indicate sequencing reads with the 5′ ends highlighted in red. ii, The existence of other potential true peaks (pink) elevates the estimation of read density in the local background. iii, A schematic plot shows how IQR-ZIP works. The blue box shows the read density distribution of the local background; the purple dot shows the density of the peak to be tested; the pink dot shows the density of a potential true peak close to the peak to be tested, whose read density is a clear outlier and thus excluded from local background estimation.
Extended Data Fig. 6 Profiles of peak calls generated by different peak callers for various assays.
a, Aggregated profiles of epigenomic marks, transcription binding sites, and chromatin accessibility in true enhancer regions and distal TREs identified by different peak callers for TSS- and NT-assays. The shaded area indicates the 95% confidence interval of mean values estimated via bootstrap; b, An example demonstrating why MACS2 is not suitable for identifying TREs. c, Distribution of element sizes identified from 12 assays by all applicable peak callers. In the box plot, the center lines, box limits, and whiskers denote the median, upper and lower quartiles, and 1.5× interquartile range, respectively; points show observations that are not in the range of quartiles ±1.5 × (Q3 − Q1). A table of sample sizes is available in Supplementary Table 5.
Extended Data Fig. 7 Extended analyses on the robustness of element predictions.
a, A previous study showed that the sequences between hg19 and hg38 are very similar as hg38 has 0.09% more ungapped non-centromeric sequences than hg19, only 0.17% of ungapped hg19 sequences are not in hg3861. Here we show the distribution of sequencing reads in the genome. The read counts of each assay were summarized against their frequency in a log scale with hg19 as blue lines and hg38 as orange lines. The p-values were calculated by two-sided Student’s t tests. b, Robustness (Jaccard index) of different peak callers when applying them to experimental data with technical and biological replicates. Correlations between alignments (Sample cor.) were calculated as Pearson’s r of log-transformed read counts among genomic bins (500 bp).
Extended Data Fig. 8 Performance evaluation of peak callers under different sequencing depths.
a, Epigenomic patterns of the true positive (enhancers, promoters) and true negative (non-enhancers) sets used for ROC calculation for peak calling from GRO-cap. b~d, Sensitivity and specificity of different peak callers when analyzing TSS-libraries (n=7) downsampled to 18.9 (b), 15 (c), and 10 (d) million mappable reads. The corresponding shaded areas show the 95% confidence interval of the means (via bootstrap). For tools where ROCs cannot be calculated, solid dots represent their performance with default parameters. Values and error bars show mean and SD.
Extended Data Fig. 9 Profiles of unique distal elements identified by different tools.
a, Comparison of the epigenomic signals (fold change over control) in elements uniquely identified by PINTS and other tools. b, Enrichment (measured as log odds ratios) of TF-binding motifs in PINTS unique TREs compared to other tools. The circles indicate the corresponding p-values (−log2p, two-sided z tests), and the error bars indicate the 90% confidence interval.
Extended Data Fig. 10 A summary of the computational tools compared in this study.
The features of different algorithms are summarized and grouped by their roles in the peak calling procedure (colored blocks). Features utilized by each tool to call peaks from nascent transcript sequencing data are indicated.
Supplementary information
Supplementary Tables 1–5.
Supplementary Table 1: Summaries of sequencing libraries analyzed in this study. Supplementary Table 2: Known enhancer sets. Supplementary Table 3: Non-enhancer set. Supplementary Table 4: Datasets integrated in the PINTS web server. Supplementary Table 5: Sample size for TREs and each tool predicted in different assays.
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Yao, L., Liang, J., Ozer, A. et al. A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers.
Nat Biotechnol (2022). https://doi.org/10.1038/s41587-022-01211-7
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DOI: https://doi.org/10.1038/s41587-022-01211-7
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