CoChat
Sign Up
Cited by 171Genomics and Chromatin DynamicsChromatin Remodeling and CancerEpigenetics and DNA Methylation

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

High-resolution genome-wide functional dissection of transcriptional regulatory regions and nucleotides in human

Open access
Wang, Xinchen, He, Liang, Goggin, Sarah M., Saadat, Alham, Wang, Li, Sinnott-Armstrong, Nasa, Claussnitzer, Melina, Kellis, Manolis·2018·Nature Communications
DOI 10.1038/s41467-018-07746-1PMC PMC6300699

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Genome-wide epigenomic maps have revealed millions of putative enhancers and promoters, but experimental validation of their function and high-resolution dissection of their driver nucleotides remain limited. Here, we present HiDRA (High-resolution Dissection of Regulatory Activity), a combined experimental and computational method for high-resolution genome-wide testing and dissection of putative regulatory regions. We test ~7 million accessible DNA fragments in a single experiment, by coupling accessible chromatin extraction with self-transcribing episomal reporters (ATAC-STARR-seq). By design, fragments are highly overlapping in densely-sampled accessible regions, enabling us to pinpoint driver regulatory nucleotides by exploiting differences in activity between partially-overlapping fragments using a machine learning model (SHARPR-RE). In GM12878 lymphoblastoid cells, we find ~65,000 regions showing enhancer function, and pinpoint ~13,000 high-resolution driver elements. These are enriched for regulatory motifs, evolutionarily-conserved nucleotides, and disease-associated genetic variants from genome-wide association studies. Overall, HiDRA provides a high-throughput, high-resolution approach for dissecting regulatory regions and driver nucleotides.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-018-07746-1

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Overview of HiDRA. a Cells with the desired genotype and open chormatin patterns are selected for library construction. Tn5 transposase is used to preferentially fragment genomic DNA at regions of open chromatin. Fragments are then size-selected on an agarose gel and mtDNA contamination is removed by selective CRISPR-Cas9 degradation. The fragment library is amplified by PCR and cloned into an enhancer reporter vector. Gel image adapted from Buenrostro et al.21. Fragments are cloned into the STARR-seq vector backbone, introduced into target cells (which can differ from cells used to construct the library), and RNA is collected and sequenced. After data processing, the activity of partially-overlapping fragments is compared to identify driver nucleotides using the SHARPR-RE algorithm. b Size distribution of HiDRA library fragments (blue) and tiled regions (green). Bimodal shape for library fragment sizes is due to Tn5 preference to cut adjacent to nucleosomes. Fragment bin size = 20 nt, region bin size = 50 nt. c Number of ChromHMM-predicted active enhancer, active TSS and ATAC-seq peaks covered by multiple unique HiDRA fragments. d HiDRA plasmid library recapitulates the genomic coverage of a conventional ATAC-seq experiment
Fig. 1 Overview of HiDRA. a Cells with the desired genotype and open chormatin patterns are selected for library construction. Tn5 transposase is used to preferentially fragment genomic DNA at regions of open chromatin. Fragments are then size-selected on an agarose gel and mtDNA contamination is removed by selective CRISPR-Cas9 degradation. The fragment library is amplified by PCR and cloned into an enhancer reporter vector. Gel image adapted from Buenrostro et al.21. Fragments are cloned into the STARR-seq vector backbone, introduced into target cells (which can differ from cells used to construct the library), and RNA is collected and sequenced. After data processing, the activity of partially-overlapping fragments is compared to identify driver nucleotides using the SHARPR-RE algorithm. b Size distribution of HiDRA library fragments (blue) and tiled regions (green). Bimodal shape for library fragment sizes is due to Tn5 preference to cut adjacent to nucleosomes. Fragment bin size = 20 nt, region bin size = 50 nt. c Number of ChromHMM-predicted active enhancer, active TSS and ATAC-seq peaks covered by multiple unique HiDRA fragments. d HiDRA plasmid library recapitulates the genomic coverage of a conventional ATAC-seq experimentCC BY · PubMed Central
Fig. 2 HiDRA identifies transcriptional regulatory elements. a Scatterplot of abundances for HiDRA fragments in input (plasmid DNA) and output (RNA) samples. Abundances calculated after merging all five replicates. Active HiDRA fragments called by DESeq2 highlighted with red dots (FDR < 0.05), blue color intensity corresponds to greater density of points. b The majority of HiDRA active regions are distal to annotated TSSs ( > 2 kb). c HiDRA identifies enhancer activity within an intron in the immunoglobulin heavy chain locus. Red bar, DNA segment active in luciferase assay performed by Huang et al.23. Orange bar and highlight, region identified by HiDRA as having transcriptional regulatory activity. d Quantitative comparison of luciferase assay activity levels to HiDRA for 21 predicted enhancer elements. HiDRA signal corresponds to maximum activity within the region tested by luciferase, and luciferase value corresponds to median normalized activity over biological replicates. Pearson correlation calculated after log2 transformation. e Comparison of HiDRA-called active regions with luciferase assay results for 13 enhancers at the NEK6 locus. Luciferase experiments are colored in red or gray depending on whether DNA fragments drive luciferase activity in GM12878 cells as determined by Huang et al.23
Fig. 2 HiDRA identifies transcriptional regulatory elements. a Scatterplot of abundances for HiDRA fragments in input (plasmid DNA) and output (RNA) samples. Abundances calculated after merging all five replicates. Active HiDRA fragments called by DESeq2 highlighted with red dots (FDR < 0.05), blue color intensity corresponds to greater density of points. b The majority of HiDRA active regions are distal to annotated TSSs ( > 2 kb). c HiDRA identifies enhancer activity within an intron in the immunoglobulin heavy chain locus. Red bar, DNA segment active in luciferase assay performed by Huang et al.23. Orange bar and highlight, region identified by HiDRA as having transcriptional regulatory activity. d Quantitative comparison of luciferase assay activity levels to HiDRA for 21 predicted enhancer elements. HiDRA signal corresponds to maximum activity within the region tested by luciferase, and luciferase value corresponds to median normalized activity over biological replicates. Pearson correlation calculated after log2 transformation. e Comparison of HiDRA-called active regions with luciferase assay results for 13 enhancers at the NEK6 locus. Luciferase experiments are colored in red or gray depending on whether DNA fragments drive luciferase activity in GM12878 cells as determined by Huang et al.23CC BY · PubMed Central
Fig. 3 Active HiDRA fragments are enriched in endogenously active regulatory regions. a Overlap of active HiDRA fragments with different endogenous chromatin states. Heights correspond to proportion of nucleotides within active HiDRA fragments in each chromatin state. Inset: histone modification enrichments in each of 18 ChromHMM chromatin states b HiDRA fragment regulatory activity (fold-change increase in RNA levels) across different chromatin states. Numbers correspond to chromatin state numbers in 18-state ChromHMM model. c, d Endogenously inactive genomic regions have low levels of TF binding (c) but comparable TF motif composition (d) to predicted active regions. Colored bars, regions from each chromatin state overlapping active HiDRA regions. Gray bars, regions from each chromatin state overlapping all fragments tested
Fig. 3 Active HiDRA fragments are enriched in endogenously active regulatory regions. a Overlap of active HiDRA fragments with different endogenous chromatin states. Heights correspond to proportion of nucleotides within active HiDRA fragments in each chromatin state. Inset: histone modification enrichments in each of 18 ChromHMM chromatin states b HiDRA fragment regulatory activity (fold-change increase in RNA levels) across different chromatin states. Numbers correspond to chromatin state numbers in 18-state ChromHMM model. c, d Endogenously inactive genomic regions have low levels of TF binding (c) but comparable TF motif composition (d) to predicted active regions. Colored bars, regions from each chromatin state overlapping active HiDRA regions. Gray bars, regions from each chromatin state overlapping all fragments testedCC BY · PubMed Central
Fig. 4 High-resolution mapping of transcriptional regulatory elements with SHARPR-RE. a Example region used in high-resolution mapping. Fragment activity shown on log2 scale with two fragments with highest and lowest activity removed for color scale to avoid outliers. The transparent red bar indicates the driver element identified at the regional FWER < 0.05. b Size distribution of driver elements. c Enrichment of immune-related TF motifs in driver elements compared with shuffled driver elements within tiled regions. d TF motifs enriched in driver elements cluster into groups of co-occurring motifs, suggesting diversity of TF motifs involved in transcriptional regulatory activity. e Significantly more driver elements are evolutionary conserved compared to shuffled driver elements within tiled regions. Evolutionary conservation cut-off chosen as conservation score for top 5% of shuffled regions (p = 2.23 x 10–73 vs. Gaussian distribution estimated by random shuffling of driver element positions in tiled regions). f SNPs within driver elements have significantly greater allelic skew by MPRA (Tewhey et al.27) compared with those within tiled regions or across the genome. P value calculated by Mann-Whitney U test
Fig. 4 High-resolution mapping of transcriptional regulatory elements with SHARPR-RE. a Example region used in high-resolution mapping. Fragment activity shown on log2 scale with two fragments with highest and lowest activity removed for color scale to avoid outliers. The transparent red bar indicates the driver element identified at the regional FWER < 0.05. b Size distribution of driver elements. c Enrichment of immune-related TF motifs in driver elements compared with shuffled driver elements within tiled regions. d TF motifs enriched in driver elements cluster into groups of co-occurring motifs, suggesting diversity of TF motifs involved in transcriptional regulatory activity. e Significantly more driver elements are evolutionary conserved compared to shuffled driver elements within tiled regions. Evolutionary conservation cut-off chosen as conservation score for top 5% of shuffled regions (p = 2.23 x 10–73 vs. Gaussian distribution estimated by random shuffling of driver element positions in tiled regions). f SNPs within driver elements have significantly greater allelic skew by MPRA (Tewhey et al.27) compared with those within tiled regions or across the genome. P value calculated by Mann-Whitney U testCC BY · PubMed Central
Fig. 5 High-resolution driver elements are enriched for fine-mapped GWAS SNPs. a Driver elements overlap more GWAS fine-mapped SNPs associated with 21 human immune-related complex traits than randomly shuffled regions. p-value calculated empirically by random shuffling of driver element positions within tiled regions. b Example locus at rs12946510 that overlaps a high-resolution driver element. Highlighted segment indicates the driver element identified at the regional FWER < 0.05. Red bar at top corresponds to region with luciferase activity as demonstrated by Hitomi et al.32
Fig. 5 High-resolution driver elements are enriched for fine-mapped GWAS SNPs. a Driver elements overlap more GWAS fine-mapped SNPs associated with 21 human immune-related complex traits than randomly shuffled regions. p-value calculated empirically by random shuffling of driver element positions within tiled regions. b Example locus at rs12946510 that overlaps a high-resolution driver element. Highlighted segment indicates the driver element identified at the regional FWER < 0.05. Red bar at top corresponds to region with luciferase activity as demonstrated by Hitomi et al.32CC BY · PubMed Central
Fig. 6 Identification of human genetic variants that alter HiDRA activity. a Overview of genotyping approach for HiDRA fragments. HiDRA fragments were originally quantified at high-depth using 37 nt paired-end reads. At this read length the allele composition of fragments is mostly unobserved. As every HiDRA fragment has a unique identifier (genomic alignment position and random 4 nt barcode), long-read re-sequencing of the HiDRA library can assign SNP genotypes to fragments that were previously quantified for activity using short reads. b q-q plot for allelic imbalance at SNPs covered by HiDRA fragments. CENTIPEDE “effect” SNPs were identified by Moyerbrailean et al.34. c “Effect” SNPs and SNPs within HiDRA active regions are more likely to be nominally significant for allelic imbalance. p-values from Fisher’s exact test. d The A allele of rs2382817, a SNP associated with inflammatory bowel disease, is more active in the HiDRA assay than the C allele. e Alelle-specific HiDRA activity signal tracks for rs2382817
Fig. 6 Identification of human genetic variants that alter HiDRA activity. a Overview of genotyping approach for HiDRA fragments. HiDRA fragments were originally quantified at high-depth using 37 nt paired-end reads. At this read length the allele composition of fragments is mostly unobserved. As every HiDRA fragment has a unique identifier (genomic alignment position and random 4 nt barcode), long-read re-sequencing of the HiDRA library can assign SNP genotypes to fragments that were previously quantified for activity using short reads. b q-q plot for allelic imbalance at SNPs covered by HiDRA fragments. CENTIPEDE “effect” SNPs were identified by Moyerbrailean et al.34. c “Effect” SNPs and SNPs within HiDRA active regions are more likely to be nominally significant for allelic imbalance. p-values from Fisher’s exact test. d The A allele of rs2382817, a SNP associated with inflammatory bowel disease, is more active in the HiDRA assay than the C allele. e Alelle-specific HiDRA activity signal tracks for rs2382817CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 45 references

Cited by

Sorted by citations

See all 171

Related

Metadata

Authors
Wang, X., He, L., Goggin, S. M., Saadat, A., Wang, L., Sinnott-Armstrong, N., Claussnitzer, M., & Kellis, M.
Affiliations
Broad Institute (US)
Corresponding author
  • Claussnitzer, Melina
  • Kellis, Manolis
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2018-12-19
Volume
9
Issue
1
Pages
5380-5380
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-018-07746-1
PMC
PMC6300699
OpenAlex
W2903823332
Cited by
159
References
45
Citations by year
2026: 5 · 2025: 16 · 2024: 27 · 2023: 18 · 2022: 18 · 2021: 23 · 2020: 30 · 2019: 12 · 2018: 7
FWCI
6.46
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • Nature
Funders
  • National Institutes of Health (GM12878, NIH-R01)
  • McDonnell Center for Systems Neuroscience
  • National Human Genome Research Institute (U01HG007610, R01HG008155, U01HG009088)
  • National Institute of General Medical Sciences (R01GM113708)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
Keywords
HIDRA, enhancer function, transcriptional regulatory sequences, chromatin accessibility, machine learning, regulatory motifs, disease-associated variants
MeSH terms
Gene Expression Regulation, Genetic Techniques, Humans, Regulatory Sequences, Nucleic Acid, Genome, Human, Polymorphism, Single Nucleotide, Genome-Wide Association Study

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review