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Cited by 121Genomics and Chromatin DynamicsChromatin Remodeling and CancerBioinformatics and Genomic Networks

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MAPS: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments

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Juric, Ivan, Yu, Miao, Abnousi, Armen, Raviram, Ramya, Fang, Rongxin, Zhao, Yuan, Zhang, Yanxiao, Qiu, Yunjiang, Yang, Yuchen, Li, Yun, Ren, Bing, Hu, Ming·2019·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1006982PMC PMC6483256

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Abstract

Hi-C and chromatin immunoprecipitation (ChIP) have been combined to identify long-range chromatin interactions genome-wide at reduced cost and enhanced resolution, but extracting information from the resulting datasets has been challenging. Here we describe a computational method, MAPS, Model-based Analysis of PLAC-seq and HiChIP, to process the data from such experiments and identify long-range chromatin interactions. MAPS adopts a zero-truncated Poisson regression framework to explicitly remove systematic biases in the PLAC-seq and HiChIP datasets, and then uses the normalized chromatin contact frequencies to identify significant chromatin interactions anchored at genomic regions bound by the protein of interest. MAPS shows superior performance over existing software tools in the analysis of chromatin interactions from multiple PLAC-seq and HiChIP datasets centered on different transcriptional factors and histone marks. MAPS is freely available at https://github.com/ijuric/MAPS.

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Fig 1 Framework of MAPS. MAPS workflow contains three major components: pre-processing, normalization and long-range interaction determination. Valid read pairs are obtained after pre-processing and then grouped to short-range reads or long-range reads depending on the genomic distance between two ends (1Kb is used). The long-range reads are further classified into the “AND”, “XOR” or “NOT” set. MAPS only considers the “AND” and “XOR” set of bin pairs for normalization and identification of the statistically significant long-range chromatin interactions. Short-range reads are used to estimate and correct for biases introduced by the ChIP procedure.
Fig 1 Framework of MAPS. MAPS workflow contains three major components: pre-processing, normalization and long-range interaction determination. Valid read pairs are obtained after pre-processing and then grouped to short-range reads or long-range reads depending on the genomic distance between two ends (1Kb is used). The long-range reads are further classified into the “AND”, “XOR” or “NOT” set. MAPS only considers the “AND” and “XOR” set of bin pairs for normalization and identification of the statistically significant long-range chromatin interactions. Short-range reads are used to estimate and correct for biases introduced by the ChIP procedure.CC BY · PubMed Central
Fig 2 Comparison of sensitivity of MAPS and hichipper. The Y-axis is the sensitivity, defined as the percentage of detectable HiCCUPS loops of deeply sequenced in situ Hi-C data (S6 Table) recovered by MAPS- or hichipper-identified interactions.
Fig 2 Comparison of sensitivity of MAPS and hichipper. The Y-axis is the sensitivity, defined as the percentage of detectable HiCCUPS loops of deeply sequenced in situ Hi-C data (S6 Table) recovered by MAPS- or hichipper-identified interactions.CC BY · PubMed Central
Fig 3 CTCF motif orientation of MAPS- and hichipper-identified interactions. The proportion of convergent, tandem and divergent CTCF motif pairs among testable MAPS- and hichipper-identified interactions. Only interactions with both ends containing either single CTCF motif or multiple CTCF motifs in the same direction are considered. The dotted vertical line indicates the expected convergent proportion from randomly chosen CTCF motif pairs (25%).
Fig 3 CTCF motif orientation of MAPS- and hichipper-identified interactions. The proportion of convergent, tandem and divergent CTCF motif pairs among testable MAPS- and hichipper-identified interactions. Only interactions with both ends containing either single CTCF motif or multiple CTCF motifs in the same direction are considered. The dotted vertical line indicates the expected convergent proportion from randomly chosen CTCF motif pairs (25%).CC BY · PubMed Central
Fig 4 Genome-browser shows MAPS-identified interactions anchored at Mtnr1a promoter from mESC H3K4me3 PLAC-seq data. Anchor regions around Mtnr1a promoter are highlighted by yellow box (chr8:45,065,000–45,075,000, two 5Kb bins). The MAPS-identified interactions overlapping this anchor region are marked by magenta arcs. The black arrow points to the interaction verified in the previous publication [20] and the other end of the interaction is marked by magenta box. Additional interacting regions identified by MAPS are marked by grey boxes. No interaction is identified by hichipper anchored at this region from mESC H3K4me3 PLAC-seq data.
Fig 4 Genome-browser shows MAPS-identified interactions anchored at Mtnr1a promoter from mESC H3K4me3 PLAC-seq data. Anchor regions around Mtnr1a promoter are highlighted by yellow box (chr8:45,065,000–45,075,000, two 5Kb bins). The MAPS-identified interactions overlapping this anchor region are marked by magenta arcs. The black arrow points to the interaction verified in the previous publication [20] and the other end of the interaction is marked by magenta box. Additional interacting regions identified by MAPS are marked by grey boxes. No interaction is identified by hichipper anchored at this region from mESC H3K4me3 PLAC-seq data.CC BY · PubMed Central
Fig 5 Cis-regulatory elements are enriched in the target bins of MAPS-identified “XOR” interactions. As only interactions from the “XOR” set are considered, CTCF enrichment analysis is not applicable for mESC CTCF PLAC-seq data, H3K4me3 enrichment analysis is not applicable for mESC H3K4me3 PLAC-seq data, and H3K27ac enrichment analysis is not applicable for GM12878 H3K27ac HiChIP data (denoted as N.A. in the heatmap). For each ChIP-seq/ATAC-seq data, we calculated the proportion of target bins and control bins containing ChIP-seq/ATAC-seq peaks, defined as %target and %control, respectively. We further defined the enrichment score as the ratio between %target and %control (numbers in S9 Table).
Fig 5 Cis-regulatory elements are enriched in the target bins of MAPS-identified “XOR” interactions. As only interactions from the “XOR” set are considered, CTCF enrichment analysis is not applicable for mESC CTCF PLAC-seq data, H3K4me3 enrichment analysis is not applicable for mESC H3K4me3 PLAC-seq data, and H3K27ac enrichment analysis is not applicable for GM12878 H3K27ac HiChIP data (denoted as N.A. in the heatmap). For each ChIP-seq/ATAC-seq data, we calculated the proportion of target bins and control bins containing ChIP-seq/ATAC-seq peaks, defined as %target and %control, respectively. We further defined the enrichment score as the ratio between %target and %control (numbers in S9 Table).CC BY · PubMed Central
Fig 6 Enrichment of CREs around the target bins of XOR set of MAPS-specific interactions. Enrichment of H3K27ac (ChIP-seq peaks), H3K4me1 (ChIP-seq peaks), ATAC-seq peaks, H3K4me3 (ChIP-seq peaks) and CTCF (ChIP-seq peaks) in a window of 500Kb around the target bins for all four datasets. Due to the definition of XOR set of interactions, H3K27ac, H3K4me3 and CTCF enrichment level is not analyzed for GM12878 H3K27ac HiChIP, mESC H3K4me3 and mESC CTCF PLAC-seq data, respectively.
Fig 6 Enrichment of CREs around the target bins of XOR set of MAPS-specific interactions. Enrichment of H3K27ac (ChIP-seq peaks), H3K4me1 (ChIP-seq peaks), ATAC-seq peaks, H3K4me3 (ChIP-seq peaks) and CTCF (ChIP-seq peaks) in a window of 500Kb around the target bins for all four datasets. Due to the definition of XOR set of interactions, H3K27ac, H3K4me3 and CTCF enrichment level is not analyzed for GM12878 H3K27ac HiChIP, mESC H3K4me3 and mESC CTCF PLAC-seq data, respectively.CC BY · PubMed Central
Fig 7 Frequency of MAPS-identified interactions and control bin pairs versus their rankings in the SPRITE contact matrix (see Methods for details). A bin pair with higher normalized SPRITE interaction frequency tends to rank top, among all bin pairs with the same genomic distance (only bin pairs in “AND” and “XOR” sets from the SPRITE contact matrix are considered).
Fig 7 Frequency of MAPS-identified interactions and control bin pairs versus their rankings in the SPRITE contact matrix (see Methods for details). A bin pair with higher normalized SPRITE interaction frequency tends to rank top, among all bin pairs with the same genomic distance (only bin pairs in “AND” and “XOR” sets from the SPRITE contact matrix are considered).CC BY · PubMed Central
Fig 8 MAPS-identified interactions from mESC H3K4me3 PLAC-seq data anchored at Med13l promoter. Anchor region around target promoter is highlighted by yellow box. The MAPS-identified interactions overlapping this anchor region are marked by magenta arcs. The deleted enhancer region in Moorthy et al study [24] is marked by magenta box.
Fig 8 MAPS-identified interactions from mESC H3K4me3 PLAC-seq data anchored at Med13l promoter. Anchor region around target promoter is highlighted by yellow box. The MAPS-identified interactions overlapping this anchor region are marked by magenta arcs. The deleted enhancer region in Moorthy et al study [24] is marked by magenta box.CC BY · PubMed Central

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  1. CE Grant, 2011, FIMO: scanning for occurrences of a given motif, Bioinformatics (Oxford, England), 27, 1017, 10.1093/bioinformatics/btr064 · DOI · Paper page

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Metadata

Authors
Juric, I., Yu, M., Abnousi, A., Raviram, R., Fang, R., Zhao, Y., Zhang, Y., Qiu, Y., Yang, Y., Li, Y., Ren, B., & Hu, M.
Affiliations
  • Ludwig Cancer Research (US)
  • Cleveland Clinic (US)
  • University of California San Diego (US)
  • University of North Carolina at Chapel Hill (US)
Corresponding author
  • Ren, Bing
  • Hu, Ming
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2019-04-15
Volume
15
Issue
4
Pages
e1006982
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1006982
PMC
PMC6483256
OpenAlex
W2951448241
Cited by
121
References
35
Citations by year
2026: 11 · 2025: 10 · 2024: 22 · 2023: 12 · 2022: 23 · 2021: 22 · 2020: 10 · 2019: 10
FWCI
5.62
Type
journal article
Language
en
Update notices
new_version (2019-04-25)
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • UNC Libraries
Funders
Foundation for the National Institutes of Health (U54DK107977, R01HL129132)
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 › Bioinformatics and Genomic Networks
Keywords
long-range chromatin interactions, HiChIP, Hi-C, transcription factor binding sites, histone modifications, model-based analysis, maps
MeSH terms
Chromatin, Chromosome Mapping, Computer Simulation, Humans, Software, Genome, Sequence Analysis, DNA, Computational Biology, Genomics, Chromatin Assembly and Disassembly, Histone Code, Chromatin Immunoprecipitation, High-Throughput Nucleotide Sequencing

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