CoChat
Sign Up
Cited by 99Genomic variations and chromosomal abnormalitiesGenomics and Chromatin DynamicsGenome Rearrangement Algorithms

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

HiNT: a computational method for detecting copy number variations and translocations from Hi-C data

Open access
Wang, Su, Lee, Soohyun, Chu, Chong, Jain, Dhawal, Kerpedjiev, Peter, Nelson, Geoffrey M., Walsh, Jennifer M., Alver, Burak H., Park, Peter J.·2020·Genome Biology
DOI 10.1186/s13059-020-01986-5PMC PMC7087379

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

The three-dimensional conformation of a genome can be profiled using Hi-C, a technique that combines chromatin conformation capture with high-throughput sequencing. However, structural variations often yield features that can be mistaken for chromosomal interactions. Here, we describe a computational method HiNT (Hi-C for copy Number variation and Translocation detection), which detects copy number variations and interchromosomal translocations within Hi-C data with breakpoints at single base-pair resolution. We demonstrate that HiNT outperforms existing methods on both simulated and real data. We also show that Hi-C can supplement whole-genome sequencing in structure variant detection by locating breakpoints in repetitive regions.

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 · 10.1186/s13059-020-01986-5

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

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?

  • SVs can now be detected alongside SNVs using cost-effective methods such as single-enzyme Hi-C with short-read sequencing.

    “Importantly, SVs can now be detected alongside SNVs using cost-effective methods such as single-enzyme Hi-C with short-read sequencing, making their integration into conservation genomics protocols both feasible and scalable (196, 197).”

    From Topological Approaches in Animal Comparative Genomics (2026) · cites this paper as [196]

    The Conclusion (page not specified) says HiNT detects copy-number variations and interchromosomal translocations from Hi-C data, supporting the SV-detection portion of the claim. It does not establish detection alongside SNVs or describe single-enzyme Hi-C with short-read sequencing as a cost-effective method; the Methods passage examined describes Hi-C read processing, not those claims. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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

Flagged first, then by citations

Research works printed but not linked: 40

  1. Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO, Sandstrom R, Bernstein B, Bender MA, Groudine M, Gnirke A, Stamatoyannopoulos J, Mirny LA, Lander ES, Dekker J. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009;326:289–93. · DOI · Paper page
  2. Engreitz JM, Agarwala V, Mirny LA. Three-dimensional genome architecture influences partner selection for chromosomal translocations in human disease. PLoS One. 2012;7:e44196. · DOI
  3. Giorgetti L, Lajoie BR, Carter AC, Attia M, Zhan Y, Xu J, Chen C-J, Kaplan N, Chang HY, Heard E, Dekker J. Structural organization of the inactive X chromosome in the mouse. Nature. 2016;535:575–9. · DOI
  4. Corces MR, Corces VG. The three-dimensional cancer genome. Curr Opin Genet Dev. 2016;36:1–7. · DOI
  5. Burton JN, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat Biotechnol. 2013;31:1119–25. · DOI · Paper page
  6. Fudenberg G, Getz G, Meyerson M, Mirny LA. High order chromatin architecture shapes the landscape of chromosomal alterations in cancer. Nat Biotechnol. 2011;29:1109–13. · DOI
  7. Wu P, Li T, Li R, Jia L, Zhu P, Liu Y, Chen Q, Tang D, Yu Y, Li C. 3D genome of multiple myeloma reveals spatial genome disorganization associated with copy number variations. Nat Commun. 2017:1–11. · DOI
  8. Wu H-J, Michor F. A computational strategy to adjust for copy number in tumor Hi-C data. Bioinformatics. 2016;32(24):3695–701. · DOI
  9. Guan P, Sung W-K. Structural variation detection using next-generation sequencing data: a comparative technical review. Methods. 2016;102(C):36–49. · DOI
  10. Spielmann M, Lupiáñez DG, Mundlos S. Structural variation in the 3D genome. Nat Rev Genet. 2018;19(7):453–67. · DOI
  11. Guan P, Sung W-K. Structural variation detection using next-generation sequencing data. Methods. 2016;102:36–49. · DOI
  12. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv. 2013; preprint arXiv:1303.3997. · arXiv
  13. Rao SSP, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES, Aiden EL. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell. 2014;159:1665–80. · DOI · Paper page
  14. Hu M, Deng K, Selvaraj S, Qin Z, Ren B, Liu JS. HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics. 2012;28:3131–3. · DOI
  15. Yaffe E, Tanay A. Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat Genet. 2011;43:1059–65. · DOI
  16. Xi R, Hadjipanayis AG, Luquette LJ, Kim T-M, Lee E, Zhang J, Johnson MD, Muzny DM, Wheeler DA, Gibbs RA, Kucherlapati R, Park PJ. Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion. Proc Natl Acad Sci. 2011;108:E1128–36. · DOI
  17. Xi R, Lee S, Xia Y, Kim T-M, Park PJ. Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants. Nucleic Acids Res. 2016;44:6274–86. · DOI
  18. Zhou B, Ho SS, Greer SU, Zhu X, Bell JM, Arthur JG, Spies N, Zhang X, Byeon S, Pattni R, Ben-Efraim N, Haney MS, Haraksingh RR, Song G, Ji HP, Perrin D, Wong WH, Abyzov A, Urban AE. Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562. Genome Res. 2019;29:472–84. · DOI
  19. Yardımcı GG, Ozadam H, Sauria MEG, Ursu O, Yan K-K, Yang T, Chakraborty A, Kaul A, Lajoie BR, Song F, Zhan Y, Ay F, Gerstein M, Kundaje A, Li Q, Taylor J, Yue F, Dekker J, Noble WS. Measuring the reproducibility and quality of Hi-C data; 2019. p. 1–19.
  20. Chakraborty A, Ay F. Identification of copy number variations and translocations in cancer cells from Hi-C data. Bioinformatics. 2017;34(2):338–45. · DOI · Paper page
  21. Vidal E, le Dily F, Quilez J, Stadhouders R, Cuartero Y, Graf T, Marti-Renom MA, Beato M, Filion GJ. OneD: increasing reproducibility of Hi-C samples with abnormal karyotypes. Nucleic Acids Res. 2018;46:e49. · DOI
  22. Durand NC, Shamim MS, Machol I, Rao SSP, Huntley MH, Lander ES, Aiden EL. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Systems. 2016;3:95–8. · DOI
  23. Abdennur N, Mirny LA. Cooler: scalable storage for Hi-C data and other genomically labeled arrays. Bioinformatics. 2019:36(1):311–6. · DOI
  24. Harewood L, Kishore K, Eldridge MD, Wingett S, Pearson D, Schoenfelder S, Collins VP, Fraser P. Hi-C as a tool for precise detection and characterisation of chromosomal rearrangements and copy number variation in human tumours. Genome Biol. 2017;18(1):125. · DOI · Paper page
  25. Dixon JR, Xu J, Dileep V, Zhan Y, Song F, Le VT, Yardımcı GG, Chakraborty A, Bann DV, Wang Y, Clark R, Zhang L, Yang H, Liu T, Iyyanki S, An L, Pool C, Sasaki T, Rivera-Mulia J-C, Ozadam H, Lajoie BR, Kaul R, Buckley M, Lee K, Diegel M, Pezic D, Ernst C, Hadjur S, Odom DT, Stamatoyannopoulos JA, et al. Integrative detection and analysis of structural variation in cancer genomes. Nat Genet. 2018;50(10):1388–98. · DOI · Paper page
  26. Wittebolle L, Marzorati M, Clement L, Balloi A, Daffonchio D, Heylen K, De Vos P, Verstraete W, Boon N. Initial community evenness favours functionality under selective stress. Nature. 2009;458:623–6. · DOI
  27. Rausch T, Zichner T, Schlattl A, Stütz AM, Benes V, Korbel JO. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics. 2012;28:i333–9. · DOI
  28. Yang L, Luquette LJ, Gehlenborg N, Xi R, Haseley PS, Hsieh C-H, Zhang C, Ren X, Protopopov A, Chin L, Kucherlapati R, Lee C, Park PJ. Diverse mechanisms of somatic structural variations in human cancer genomes. Cell. 2013;153:919–29. · DOI
  29. Becker T, Lee W-P, Leone J, Zhu Q, Zhang C, Liu S, Sargent J, Shanker K, Mil-homens A, Cerveira E, Ryan M, Cha J, Navarro FCP, Galeev T, Gerstein M, Mills RE, Shin D-G, Lee C, Malhotra A. FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods; 2018. p. 1–14.
  30. Bao W, Kojima KK, Kohany O. Repbase update, a database of repetitive elements in eukaryotic genomes. Mob DNA. 2015;6:11. · DOI
  31. Lin D, Hong P, Zhang S, Xu W, Jamal M, Yan K, Lei Y, Li L, Ruan Y, Fu ZF, Li G, Cao G. Digestion-ligation-only Hi-C is an efficient and cost-effective method for chromosome conformation capture. Nat Genet. 2018;50:754–63. · DOI
  32. Lai WR, Johnson MD, Kucherlapati R, Park PJ. Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics. 2005;21:3763–70. · DOI
  33. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74. · DOI · Paper page
  34. Miga KH, Koren S, Rhie A, Vollger MR, Gershman A, Bzikadze A, Brooks S, Howe E, Porubsky D, Logsdon GA, Schneider VA, Potapova T, Wood J, Chow W, Armstrong J, Fredrickson J, Pak E, Tigyi K, Kremitzki M, Markovic C, Maduro V, Dutra A, Bouffard GG, Chang AM, Hansen NF, Thibaud-Nissen F, Schmitt AD, Belton J-M, Selvaraj S, Dennis MY, et al. Telomere-to-telomere assembly of a complete human X chromosome. bioRxiv. 2019;8:758.
  35. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCrthy S, McVean GA, Abecasis GR. A global reference for human genetic variation. Nature. 2015;526:68–74. · DOI · Paper page
  36. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, Berger MF, Monahan JE, Morais P, Meltzer J, Korejwa A, Jané-Valbuena J, Mapa FA, Thibault J, Bric-Furlong E, Raman P, Shipway A, Engels IH, Cheng J, Yu GK, Yu J, Aspesi P, de Silva M, et al. The Cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–7. · DOI · Paper page
  37. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics. 2005;21:3940–1. · DOI
  38. Knight PA, Ruiz D. A fast algorithm for matrix balancing. IMA J Numer Anal. 2013;33:1029–47. · DOI
  39. Wang S, Lee S, Chu C, Jain D, Kerpedjiev P, Nelson G, Walsh JM, Alver BH, Park PJ. HiNT: a computational method for detecting copy number variations and translocations from Hi-C data. Github. 2020. https://github.com/parklab/HiNT. · DOI
  40. Wang S, Lee S, Chu C, Jain D, Kerpedjiev P, Nelson G, Walsh JM, Alver BH, Park PJ. HiNT: a computational method for detecting copy number variations and translocations from Hi-C data. Source code. Zenodo. 2020. https://zenodo.org/record/3669319. · DOI

Cited by

Sorted by citations

Metadata

Authors
Wang, S., Lee, S., Chu, C., Jain, D., Kerpedjiev, P., Nelson, G. M., Walsh, J. M., Alver, B. H., & Park, P. J.
Affiliations
Harvard University (US)
Corresponding author
Park, Peter J.
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2020-03-23
Volume
21
Issue
1
Pages
73-73
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-020-01986-5
PMC
PMC7087379
OpenAlex
W3014004911
Cited by
99
References
6
Citations by year
2026: 14 · 2025: 12 · 2024: 17 · 2023: 20 · 2022: 8 · 2021: 21 · 2020: 6
FWCI
9.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Foundation for the National Institutes of Health (U01CA200059)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genome Rearrangement Algorithms
Keywords
copy number variation, interchromosomal translocations, Hi-C, chromatin conformation, structural variant calling, change point detection, single base pair resolution, repetitive regions, hints
MeSH terms
Whole Genome Sequencing, Repetitive Sequences, Nucleic Acid, Translocation, Genetic, Sequence Analysis, DNA, Genomics, Chromosome Breakpoints, DNA Copy Number Variations, 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 ›

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