MAPS: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments
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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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Chromatin loops connecting active promoters to distal regulatory regions were found in microglia, neurons, and oligodendrocytes.
“Chromatin loops were identified between active promoters and distal regulatory regions in microglia, neurons and oligodendrocytes (26).”
From Brain cell type–specific enhancer–promoter interactome maps and disease <b>-</b> risk association (2019) · cites this paper as [26]
The Introduction (page unavailable) says distal enhancers frequently form chromatin loops with target-gene promoters, supporting the general type of interaction. The Results and Methods passages describe analyses in mouse embryonic stem cells and GM12878 cells, not microglia, neurons, or oligodendrocytes. Our copy therefore does not support the claim that these loops were identified in the three named cell types. (LLM, conf=0.86)
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Self-citationChIATAC is an efficient strategy for multi-omics mapping of 3D epigenomes from low-cell inputs
Chai, H.-X., Tjong, H., Li, P., Liao, W., Wang, P., Wong, C.-H., Ngan, C., Leonard, W., Wei, C.-L., & Ruan, Y. (2023) · Nature Communications · doi:10.1038/s41467-023-35879-5 · Cited by 23 · see citing works
Benaglio, P., Zhu, H., Okino, M.-L., Yan, J., Elgamal, R. M., Nariai, N., Beebe, E., Korgaonkar, K., Qiu, Y., Donovan, M. K. R., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100214 · Cited by 35 · see citing works
“80 We used hg19 as the reference genome and H3K27ac ChIP-seq peaks in EndoC-b H1 cells from a published study.”
MethodSelf-citationHiC-ACT: Improved Detection of Chromatin Interactions from Hi-C Data via Aggregated Cauchy Test
Lagler, T. M., Yang, Y.-C., Abnousi, A., Hu, M., & Li, Y. (2020) · bioRxiv · doi:10.1101/2020.10.28.359869 · Cited by 23
“We compared these peak calls to mESC ChIP-seq (H3K4me3, H3K4me1, H3K27ac, and CTCF) peaks and ATAC-seq peaks.(26) We defined an overlap as a HiCACT/FitHiC2-called peak with either 10 kb bin overlapping a ChIP-seq/ATAC-seq peak.”
Self-citationDiverse Molecular Mechanisms Contribute to Differential Expression of Human Duplicated Genes
Shew, C. J., Carmona-Mora, P., Soto, D. C., Mastoras, M., Roberts, E., Rosas, J., Jagannathan, D., Kaya, G., O'Geen, H., & Dennis, M. (2020) · bioRxiv · doi:10.1093/molbev/msab131 · Cited by 21 · see citing works
SnapHiC2: A computationally efficient loop caller for single cell Hi-C data
Li, X., Lee, L., Abnousi, A., Yu, M., Liu, W., Huang, L., Li, Y., & Hu, M. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.046 · Cited by 20 · see citing works
Self-citationFath, M. K., Karimfar, N., Fazlollahpour-Naghibi, A., Shafa, S., Shiran, M. G., Ataei, M., Dehghanzadeh, H., Afjadi, M. N., Ghadiri, T., Payandeh, Z., et al. (2022) · Cancer Cell International · doi:10.1186/s12935-022-02617-8 · Cited by 20 · see citing works
“Finally, the biotin-labeled DNA that corresponds to the linker is enriched, followed by library preparation and paired-end DNA sequencing [59, 60].”
Liu, C.-F., Abnousi, A., Bazeley, P., Ni, Y., Morley, M., Moravec, C. S., Hu, M., & Tang, W. H. W. (2020) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2020.06.001 · Cited by 19 · see citing works
“We applied the Model-based analysis of the PLAC-seq (MAPS) pipeline to analyze PLACseq data as previously described[18].”
MethodSelf-citationMSL2 ensures biallelic gene expression in mammals
Sun, Y., Wiese, M., Hmadi, R., Karayol, R., Seyfferth, J., Greene, J. A. M., Erdogdu, N. U., Deboutte, W., Arrigoni, L., Holz, H., et al. (2023) · Nature · doi:10.1038/s41586-023-06781-3 · Cited by 21 · see citing works
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Self-citationThe impact of the embryonic DNA methylation program on CTCF-mediated genome regulation
Monteagudo-Sánchez, A., Albert, J. R., Scarpa, M., Noordermeer, D., & Greenberg, M. V. C. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae724 · Cited by 14 · see citing works
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Self-citationFerris, E., Murcia, J. D. G., Rodriguez, A. C., Steinwand, S., Hörndli, C. S., Traenkner, D., Maldonado-Catala, P. J., & Gregg, C. (2024) · bioRxiv · doi:10.1101/2024.06.26.600891 · Cited by 13
The dynamic role of cohesin in maintaining human genome architecture
Agarwal, A., Korsak, S., Choudhury, A., & Plewczynski, D. M. (2023) · Bioessays · doi:10.1002/bies.202200240 · Cited by 13 · see citing works
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Self-citationA screen for regeneration-associated silencer regulatory elements in zebrafish
Ando, K., Ou, J., Thompson, J. D., Welsby, J., Bangru, S., Shen, J.-W., Wei, X., Diao, Y.-R., & Poss, K. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.01.004 · Cited by 12 · see citing works
A comprehensive review of bioinformatics tools for chromatin loop calling
Liu, L., Han, K.-Y., Sun, H., Han, L., Gao, D., Xi, Q., Zhang, L.-R., & Lin, H. (2023) · Briefings Bioinform. · doi:10.1093/bib/bbad072 · Cited by 12 · see citing works
THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data
Rowland, B., Huh, R., Hou, Z., Crowley, C., Wen, J., Shen, Y., Hu, M., Giusti‐Rodríguez, P., Sullivan, P. F., & Li, Y. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010102 · Cited by 11 · see citing works
“[36,37] Used in concert with methods to identify longrange chromatin interactions from HP data[38], our method is anticipated to efficiently leverage interchromosomal contacts jointly with high quality intrachromosomal contacts to estimate underlying cell type proportions.”
MethodSelf-citationProximity Ligation-Assisted ChIP-Seq (PLAC-Seq).
Yu, M., Juric, I., Abnousi, A., Hu, M., & Ren, B. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1597-3_10 · Cited by 12 · see citing works
Self-citationNott, A., Holtman, I., Coufal, N., Schlachetzki, J., Yu, M., Hu, R., Han, C. Z., Pena, M., Xiao, J., Wu, Y., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/778183 · Cited by 11 · see citing works
“MAPS was used to call chromatin loops between H3K4me3 peaks and distal regulatory regions from PLAC-seq datasets in microglia, neurons and oligodendrocytes (58).”
MethodSelf-citationGlucocorticoids unmask silent non-coding genetic risk variants for common diseases
Nguyen, T., Gao, H.-Y., Liu, D., Philips, T., Ye, Z.-Q., Lee, J.-H., Shi, G.-X., Copenhaver, K., Zhang, L.-X., Wei, L., et al. (2021) · bioRxiv · doi:10.1093/nar/gkac1045 · Cited by 10 · see citing works
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Self-citationLee, L., Yu, M., Li, X., Zhu, C., Zhang, Y.-X., Yu, H.-Y., Chen, Z.-Y., Mishra, S., Ren, B., Li, Y., et al. (2022) · bioRxiv · doi:10.1101/2022.08.05.502991 · Cited by 9 · see citing works
Self-citationMachine Learning and Deep Learning in Genetics and Genomics
Wu, D., Karhade, D. S., Pillai, M., Jiang, M.-Z., Huang, L., Li, G., Cho, H., Roach, J., Li, Y., & Divaris, K. (2021) · Machine Learning in Dentistry · doi:10.1007/978-3-030-71881-7_13 · Cited by 9 · see citing works
Self-citationPioneer factor GATA6 promotes colorectal cancer through 3D genome regulation
Lyu, H., Chen, X., Cheng, Y., Zhang, T., Wang, P., Wong, J., Wang, J., Stasiak, L., Sun, L., Yang, G., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads4985 · Cited by 8 · see citing works
Extensive folding variability between homologous chromosomes in mammalian cells
Irastorza-Azcarate, I., Kukalev, A., Kempfer, R., Thieme, C. J., Mastrobuoni, G., Markowski, J., Loof, G., Sparks, T. M., Brookes, E., Natarajan, K., et al. (2024) · bioRxiv · doi:10.1101/2024.05.08.591087 · Cited by 8
Self-citationTang, L., Hill, M., Ellinor, P., & Li, M. (2022) · Genome Biology · doi:10.1186/s13059-021-02597-4 · Cited by 8 · see citing works
“MAPS [29] adopted a zero-truncated Poisson regression framework to explicitly remove the biases of HiChIP/PLAC-seq data, and then identifies the chromatin interactions by the normalized contact frequencies.”
MethodLam, D. D., Nikolić, A., Zhao, C., Mirza-Schreiber, N., Krężel, W., Oexle, K., & Winkelmann, J. (2021) · bioRxiv · doi:10.1101/2021.09.03.458823 · Cited by 8
HPRep: Quantifying Reproducibility in HiChIP and PLAC-Seq Datasets
Rosen, J. D., Yang, Y.-C., Abnousi, A., Chen, J.-W., Song, M., Jones, I. R., Shen, Y., Hu, M., & Li, Y. (2020) · bioRxiv · doi:10.3390/cimb43020082 · Cited by 8 · see citing works
Self-citationWang, L., Taylor, T., Rathnakumar, K., Khyzha, N., Liang, M.-G., Alizada, A., Campitelli, L., Pour, S. E., Patel, Z. M., Antounians, L., et al. (2024) · bioRxiv · doi:10.1101/gr.280357.124 · Cited by 7
“processed using the MAPS pipeline (Juric et al. 2019) with default parameters.”
Sun, T., Xu, Y.-Y., Angel, N., Chen, L.-N., Zhang, K., Hoffman, B., Ou, J., Meng, Z., Varghese, S., & Diao, Y.-R. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01770-2 · Cited by 6 · see citing works
“Using HiCAR data from hES cells on stiff matrices, we identified 7,638 chromatin interactions anchored to the 4,981 YAP/CTCF co-occupied TE regions (10 kb loop calling using MAPS 68 , false discovery rate (FDR) <0.01; Supplementary Table 10).”
InfluentialHuang, Q., Sim, S., Geib, S., Childers, A. K., Liu, J.-F., Wei, X., Han, W.-S., Posada-Flórez, F., Xue, A. Z., Li, Z., et al. (2022) · GigaScience · doi:10.1093/gigascience/giad056 · Cited by 6 · see citing works
SnapHiC: a computational pipeline to map chromatin contacts from single cell Hi-C dataPreprint
Yu, M., Abnousi, A., Zhang, Y.-X., Li, G., Lee, L., Chen, Z.-Y., Fang, R.-X., Wen, J., Sun, Q., Li, Y., et al. (2020) · bioRxiv · doi:10.1101/2020.12.13.422543 · Cited by 6 · see citing works
Self-citationSong, M., Pebworth, M., Yang, X.-Y., Abnousi, A., Fan, C.-X., Wen, J., Rosen, J. D., Choudhary, M. N. K., Cui, X.-K., Jones, I. R., et al. (2020) · bioRxiv · doi:10.1101/2020.02.24.963652 · Cited by 6
Self-citationTsiakanikas, P., Athanasopoulou, K., Darioti, I. A., Agiassoti, V. T., Theocharis, S., Scorilas, A., & Adamopoulos, P. (2024) · Life · doi:10.3390/life14080922 · Cited by 5 · see citing works
Liu, W., Zhong, W., Giusti‐Rodríguez, P., Jiang, Z., Wang, G. W., Sun, H., Hu, M., & Li, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae426 · Cited by 3 · see citing works
Self-citationWen, J., Lagler, T. M., Sun, Q., Yang, Y.-C., Chen, J.-W., Harigaya, Y., Sankaran, V., Hu, M., Reiner, A., Raffield, L., et al. (2021) · bioRxiv · doi:10.1371/journal.pgen.1009984 · Cited by 5 · see citing works
“We then used the -log10 (MAPS q-value) from the MAPS [8] calls as the interaction score for each promoter-PIR interaction, and took the cumulative sum of all interaction scores per promoter bait.”
MethodSelf-citationExtensive folding variability between homologous chromosomes in mammalian cells
Irastorza-Azcarate, I., Kukalev, A., Kempfer, R., Thieme, C. J., Mastrobuoni, G., Markowski, J., Loof, G., Sparks, T. M., Brookes, E., Natarajan, K., et al. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00107-3 · Cited by 4 · see citing works
Self-citationFan, S., Dang, D., Gao, L., & Zhang, S.-H. (2024) · Advancement of science · doi:10.1002/advs.202406364 · Cited by 4 · see citing works
Buka, K., Parteka-Tojek, Z., Agarwal, A., Denkiewicz, M., Korsak, S., Chiliński, M., Banecki, K. H., & Plewczynski, D. (2024) · bioRxiv · doi:10.1038/s42003-025-07847-w · Cited by 4 · see citing works
“Using HiChIP-speci fi c tools such as MAPS 28 , we identi fi ed interactions that are undetectable when using KR-https normalised interaction maps for loop calling.”
InfluentialHalder, A. K., Agarwal, A., Jodkowska, K., & Plewczyński, D. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae009 · Cited by 5 · see citing works
Chromatin activity identifies differential gene regulation across human ancestries
Pettie, K. P., Mumbach, M. R., Lea, A., Ayroles, J., Chang, H. Y., Kasowski, M. M., & Fraser, H. (2022) · bioRxiv · doi:10.1186/s13059-024-03165-2 · Cited by 4 · see citing works
Integrative analysis of the 3D genome and epigenome in mouse embryonic tissues
Yu, M., Zemke, N. R., Chen, Z., Jurić, I., Hu, R., Raviram, R., Abnousi, A., Fang, R., Zhang, Y., Gorkin, D. U., et al. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-024-01431-2 · Cited by 9 · see citing works
Self-citationHiC-DC+: systematic 3D interaction calls and differential analysis for Hi-C and HiChIP
Sahin, M., Wong, W., Zhan, Y.-Q. A., Deynze, K. V., Koche, R., & Leslie, C. (2020) · bioRxiv · doi:10.1101/2020.10.11.335273 · Cited by 4
“Similarly, we ranked candidate interactions using three other published methods: Fit-HiChIP 11 , an adaptation of Fit-Hi-C for HiChIP; MAPS 13 , another approach based on fitting a generalized linear model to estimate a background distribution; and hichipper 10 , a method that models the background…”
MethodInfluentialJenkinson, H. (2017) · JAMA dermatology · doi:10.1001/jamadermatol.2017.2923 · Cited by 4
Ding, Y., He, B., Bogush, D., Schramm, J., Singh, C., Dovat, K., Randazzo, J., Tukaramrao, D., Hengst, J., Annageldiyev, C., et al. (2024) · bioRxiv · doi:10.1038/s41375-025-02651-1 · Cited by 3 · see citing works
“There is a dynamic shift in the function of the genes regulated by H3K27me3 LOCKs: in day #1 following IKAROS re-expression, H3K27me3 LOCKs regulate mostly pathways in cancer and cellular proliferation, while in day #2, H3K27me3 LOCKs mostly regulate genes involved in immune response (Fig.”
Ng, H.-L., Robinson, M. E., May, P. C., Innes, A. J., Hiemeyer, C., & Feldhahn, N. (2024) · British Journal of Haematology · doi:10.1111/bjh.19322 · Cited by 3 · see citing works
MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops
Tang, L., Liao, J., Hill, M., Hu, J., Zhao, Y.-C., Ellinor, P., & Li, M. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae029 · Cited by 3 · see citing works
“Up to now, there are 10 peak-based methods called ChIA-PET Tool ( 11 ), MICC ( 12 ), ChIA-PET2 ( 13 ), ChIA-PET Tool V3 ( 14 ), ChIAPoP ( 15 ), ChiaSig ( 16 ), Mango ( 17 ), Hichipper ( 18 ), MAPS ( 19 ), FitHiChIP ( 20 ) and HiCCUPS ( 21 ) (see Supplementary Table S1 ).”
Self-citationCatta-Preta, R., Lindtner, S., Ypsilanti, A., Price, J. D., Abnousi, A., Su-Feher, L., Wang, Y.-R., Juric, I., Jones, I. R., Akiyama, J., et al. (2023) · bioRxiv · doi:10.1101/2023.06.28.546894 · Cited by 3
Self-citationComputational methods for identifying enhancer‐promoter interactions
Gong, H., Chen, Z., Tang, Y.-X., Li, M.-H., Zhang, S., Zhang, X.-T., & Chen, Y. (2023) · Quant. Biol. · doi:10.15302/j-qb-022-0322 · Cited by 3 · see citing works
Mohammed, J., Arora, N., Matthews, H., Hansen, K. L., Bader, M., Walsh, S., Shaffer, J., Weinberg, S., Swigut, T., Claes, P., et al. (2022) · bioRxiv · doi:10.1101/2022.09.05.506587 · Cited by 3
“To call significant 3D interactions, MAPS requires precomputed 1-dimensional (1D) ChIP peaks, ideally called from independent ChIP-seq experiments.”
MethodInfluentialDiscovery and Functional Characterization of Pro-growth Enhancers in Human Cancer CellsPreprint
Chen, P.-S. B., Fiaux, P. C., Li, B., Zhang, K., Kubo, N., Jiang, S., Hu, R., Wu, S.-H., Wang, M.-C., Wang, W., et al. (2021) · bioRxiv · doi:10.1101/2021.02.04.429675 · Cited by 3 · see citing works
“Supplementary Materials: Materials and Methods Figures S1-S9 Tables S1-S11 References (1-81)”
BackgroundMethodInfluentialSelf-citationEpigenetic regulation in osteoarthritis: recent updates and emerging mechanisms
Zhao, Y., & Liu, X.-W. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1862466 · Cited by 2 · see citing works
“…(HiChIP), and proximity ligation-assisted chromatin immunoprecipitation sequencing (PLAC-seq) to systematically characterize chromatin interaction landscapes in OA chondrocytes, subchondral bone cells, and synovial cells (Juric et al., 2019; Bittner et al., 2024; Roberts and Rice, 2024).”
Self-citationPickering, S. H., Galigniana, N., Abdelhalim, M., Sørensen, A., Madsen-Østerbye, J., Zucknick, M., Collas, P., & Briand, N. (2026) · Communications Biology · doi:10.1038/s42003-025-09469-8 · Cited by 2 · see citing works
“When calling chromatin interactions (loops) using the MAPs pipeline 14 , we found a similar number of loops across conditions (counts ≥ 6; Supplementary Fig.”
Accurate and robust 3D genome feature discovery from multiplexed DNA FISHPreprint
Yu, H., Zhou, L.-B., Xie, L., Strambio-De-Castillia, C., Hu, M., & Li, Y. (2025) · bioRxiv · doi:10.1101/2025.11.26.690837 · Cited by 2 · see citing works
“As a working ground truth, we collected six reference loop lists identified from mESCs: MAPS-identified loops from CTCF PLAC-seq data 48 , MAPS-identified loops from H3K4me3 PLAC-seq data 48 , CTCF ChIA-PET loops 49 , POLR2A ChIA-PET loops 49 , HiCExplorer 50 and FitHiC2 51 loops from bulk Hi-C 14 .”
Self-citationLoop Catalog: a comprehensive HiChIP database of human and mouse samples
Reyna, J., Fetter, K., Ignacio, R., Marandi, C. C. A., Ma, A., Rao, N., Jiang, Z., Figueroa, D., Bhattacharyya, S., & Ay, F. (2025) · Genome Biology · doi:10.1186/s13059-025-03615-5 · Cited by 2 · see citing works
“For the identification of loops from HiChIP data, there again are a number of computational methods available, including those originally designed for Hi-C (e.g., HiCCUPS) and others specifically for HiChIP (e.g., FitHiChIP, hichipper, MAPS) [59, 88, 89].”
Rosen, J. D., Lee, L., Abnousi, A., Chen, J.-W., Wen, J., Hu, M., & Li, Y. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.01.003 · Cited by 2 · see citing works
“HPTAD starts from the processed HP data, following our previous work [42].”
InfluentialSelf-citationGlotov, O., Chernov, A., Fedyakov, M., Larionova, V., Zaretsky, A., Donnikov, M., & Glotov, A. (2022) · Biological Communications · doi:10.21638/spbu03.2022.403 · Cited by 2 · see citing works
Kubo, N., Hu, R., Ye, Z., & Ren, B. (2021) · bioRxiv · doi:10.1101/2021.03.17.435905 · Cited by 2 · see citing works
“, Takahashi, Y.H., Mickey, K., Voets, O., 536 Verrijzer, C.P., and Shilatifard, A. (2012). Enhancer-associated H3K4 monomethylation by 537 Trithorax-related, the Drosophila homolog of mammalian Mll3/Mll4. Genes Dev 26, 2604-2620. 538 Hu, D., Gao, X., Cao, K., Morgan, M.A., Mas, G., Smith, E.R., Volk, A.G., Bartom, E.T…”
InfluentialSelf-citationGunasekara, C. W. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.152499 · Cited by 1 · see citing works
Ou, J.-H., & Poss, K. D. (2025) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf299 · Cited by 1 · see citing works
Choppavarapu, L., Nosirov, B., Kahn, M., & Jin, V. X. (2026) · Cancers · doi:10.3390/cancers18213416
Zhou, L.-B., Chen, C., Zhao, J. Z., Hu, M., & Li, Y. (2026) · Methods and Protocols · doi:10.3390/mps9050135
“For mESC, we used ChIP-seq peaks for CTCF, H3K4me1, H3K4me3, H3K27ac [31–33], ATAC-seq peaks [33], super-enhancer annotations from dbSUPER [34], and active enhancer annotations from FANTOM5 [35].”
Self-citation3D epigenome of glial cell types in developing human cortex
Jones, I. R., Wang, L., Kosicki, M., Battle, S. L., Narayan, V. J., Bi, Q., Gemenes, K., Liu, Y., Zhou, L., Song, M., et al. (2026) · Nature · doi:10.1038/s41586-026-10987-6
“87.”
Self-citationA Microglial Regulatory Program Linked to Neuropsychiatric DisordersPreprint
Traenkner, D., Johnson, A. P., Williams, M. E., & Rothenfluh, A. (2026) · bioRxiv · doi:10.64898/2026.08.27.747643
“Reproducible interactions involving Hoxb8-bound loci were identified using MAPS at 5-kb resolution and a maximum interaction distance of 5 Mb (Juric et al., 2019).”
Agarwal, A., Bkhetan, Z. A., & Plewczynski, D. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag636
“Tools such as HiC-Pro (Servant et al. 2015), MAPS (Juric et al. 2019), and FitHiChIP (Bhattacharyya et al. 2019) have introduced workflows for detecting chromatin loops.”
InfluentialBaruri, S., Batra, L., Adhikari, S., & El-Baz, A. S. (2026) · Epigenomes · doi:10.3390/epigenomes10030053 · Cited by 1 · see citing works
- 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
- 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
- gold
- doi.org
- 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
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 ›
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