The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An integrative approach for fine-mapping chromatin interactions
Open accessPublished version of a preprint
Abstract
Motivation: Chromatin interactions play an important role in genome architecture and gene regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g. 5-25 kb), which is substantially coarser than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions. Results: To predict the sources of Hi-C-identified interactions at a high resolution (e.g. 100 bp), we developed a computational method that integrates data from DNase-seq and ChIP-seq of TFs and histone marks. Our method, χ-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. χ-CNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show χ-CNN predictions enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological significance. χ-CNN provides an approach for analyzing important aspects of genome architecture and gene regulation at a higher resolution than previously possible. Availability and implementation: χ-CNN software is available on GitHub (https://github.com/ernstlab/X-CNN). Supplementary information: Supplementary data are available at Bioinformatics online.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
Early AE fusion can be used to initialize the first layer of another neural network, as demonstrated by Jaroszewicz et al. on fine-mapping of chromatin peaks.
“Early AE fusion can also be used to initialize the first layer of another neural network as demonstrated by Jaroszewicz et al. [32] on fine-mapping of chromatin peaks.”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [32]
Initialization with a useful joint latent representation of the data can enhance the training procedure significantly.
“Initialization with a useful joint latent representation of the data can enhance the training procedure significantly.”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [32]
Citations
Cites
Chromosome conformation elucidates regulatory relationships in developing human brain
Won, H., de la Torre-Ubieta, L., Stein, J. L., Parikshak, N. N., Huang, J., Opland, C. K., Gandal, M. J., Sutton, G. J., Hormozdiari, F., Lu, D., et al. (2016) · Nature · doi:10.1038/nature19847 · Cited by 584
Self-citation
Flagged first, then by citations
Research works printed but not linked: 31
- Ay, 2014, Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts, Genome Res, 24, 999, 10.1101/gr.160374.113 · DOI
- Bergstra, 2012, Random search for hyper-parameter optimization, J. Mach. Learn. Res, 13, 281
- Bromley, 1994, Signature Verification Using a “Siamese” Time Delay Neural Network, 737
- Cameron, 2018, Estimating DNA–DNA interaction frequency from Hi-C data at restriction-fragment resolution, bioRxiv, 5, 1
- Cao, 2017, Reconstruction of enhancer–target networks in 935 samples of human primary cells, tissues and cell lines, Nat. Genet, 49, 1428, 10.1038/ng.3950 · DOI
- Carron, 2019, Boost-HiC: computational enhancement of long-range contacts in chromosomal contact maps, Bioinformatics, 35, 2724, 10.1093/bioinformatics/bty1059 · DOI
- Davydov, 2010, Identifying a high fraction of the human genome to be under selective constraint using GERP ++, PLoS Comput. Biol, 6, e1001025, 10.1371/journal.pcbi.1001025 · DOI · Paper page
- 2012, An integrated encyclopedia of DNA elements in the human genome, Nature, 489, 57, 10.1038/nature11247 · DOI · Paper page
- Ernst, 2012, ChromHMM: automating chromatin-state discovery and characterization, Nat. Methods, 9, 215, 10.1038/nmeth.1906 · DOI
- Ernst, 2013, Interplay between chromatin state, regulator binding, and regulatory motifs in six human cell types, Genome Res, 23, 1142, 10.1101/gr.144840.112 · DOI
- Ernst, 2015, Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues, Nat. Biotechnol, 33, 364, 10.1038/nbt.3157 · DOI
- Farré, 2018, Dense neural networks for predicting chromatin conformation, BMC Bioinformatics, 19, 1, 10.1186/s12859-018-2286-z · DOI
- 2017, Genetic effects on gene expression across human tissues, Nature, 550, 204, 10.1038/nature24277 · DOI · Paper page
- Hoffman, 2013, Integrative annotation of chromatin elements from ENCODE data, Nucleic Acids Res, 41, 827, 10.1093/nar/gks1284 · DOI
- Huang, 2015, Predicting chromatin organization using histone marks, Genome Biol, 16, 1, 10.1186/s13059-015-0740-z · DOI
- Kheradpour, 2014, Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments, Nucleic Acids Res, 42, 2976, 10.1093/nar/gkt1249 · DOI
- Krizhevsky, 2012, pp. 1097–1105
- Lieberman-Aiden, 2009, Comprehensive mapping of long-range interactions reveals folding principles of the human genome, Science, 326, 289, 10.1126/science.1181369 · DOI · Paper page
- Lupiáñez, 2015, Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions, Cell, 161, 1012, 10.1016/j.cell.2015.04.004 · DOI · Paper page
- Mifsud, 2015, Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C, Nature Genet, 47, 598, 10.1038/ng.3286 · DOI · Paper page
- Mumbach, 2016, HiChIP: efficient and sensitive analysis of protein-directed genome architecture, Nat. Methods, 13, 919, 10.1038/nmeth.3999 · DOI · Paper page
- Naumova, 2012, Analysis of long-range chromatin interactions using Chromosome Conformation Capture, Methods, 58, 192, 10.1016/j.ymeth.2012.07.022 · DOI
- Park, 2009, ChIP-seq: advantages and challenges of a maturing technology, Nat. Rev, 10, 669, 10.1038/nrg2641 · DOI · Paper page
- Rao, 2014, A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping, Cell, 159, 1665, 10.1016/j.cell.2014.11.021 · DOI · Paper page
- Roy, 2015, A predictive modeling approach for cell line-specific long-range regulatory interactions, Nucleic Acids Res, 43, 8694, 10.1093/nar/gkv865 · DOI
- Sanborn, 2015, Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes, Proc. Natl. Acad. Sci. USA, 112, 6456, 10.1073/pnas.1518552112 · DOI · Paper page
- Song, 2010, DNase-Seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells, Cold Spring Harb. Protocol, 2010, 1
- Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res, 15, 1929
- Whalen, 2016, Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin, Nat. Genet, 488, 10.1038/ng.3539 · DOI · Paper page
- Xi, 2018, Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy, PLoS Comput. Biol, 14, e1006625, 10.1371/journal.pcbi.1006625 · DOI · Paper page
- Zhang, 2018, Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus, Nat. Commun, 9, 1
Cited by
Sorted by citations
Multimodal deep learning for biomedical data fusion: a review
Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab569 · Cited by 766
Metadata
- Authors
- Jaroszewicz, A., & Ernst, J.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Ernst, Jason
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-11-19
- Volume
- 36
- Issue
- 6
- Pages
- 1704-1711
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC7425030
- OpenAlex
- W2990636390
- Cited by
- 7
- References
- 1
- Citations by year
- 2026: 1 · 2024: 1 · 2022: 1 · 2021: 3 · 2020: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Health (T32HG002536, R01ES024995, U01HG007912, DP1DA044371)
- National Science Foundation
- NIH
- NSF (1254200)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Chromosomal and Genetic Variations
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- chromatin interactions, Hi-C, fine mapping, convolutional neural networks, DNase-seq, ChIP-seq, transcription factor binding sites, histone modifications, feature attribution, genome organization, gene expression regulation, expression quantitative trait loci
- MeSH terms
- Chromatin, Software, Neural Networks, Computer, Genome, Histone Code
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
