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

View ORCID ProfileSu Wang, View ORCID ProfileSoohyun Lee, Chong Chu, Dhawal Jain, Geoff Nelson, Jennifer M. Walsh, Burak H. Alver, Peter J. Park
doi: https://doi.org/10.1101/657080
Su Wang
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Soohyun Lee
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Chong Chu
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Dhawal Jain
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Geoff Nelson
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Jennifer M. Walsh
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Burak H. Alver
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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Peter J. Park
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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  • For correspondence: peter_park@hms.harvard.edu
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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 (SV) 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 inter-chromosomal 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 SV detection by locating breakpoints in repetitive regions.

  • List of abbreviations

    HiNT
    Hi-C for copy Number variation and Translocation detection;
    CNV
    copy number variation;
    SV
    structural variation;
    GAM
    generalized additive model;
    WGS
    whole genome sequencing;
    1D
    1-dimensional;
    ROC
    receiver operating characteristic;
    TADs
    topologically associated domains;
    TP
    true positive;
    TN
    true negative;
    FP
    false positive;
    FN
    false negative;
    RP
    rank product.
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted June 03, 2019.
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    HiNT: a computational method for detecting copy number variations and translocations from Hi-C data
    Su Wang, Soohyun Lee, Chong Chu, Dhawal Jain, Geoff Nelson, Jennifer M. Walsh, Burak H. Alver, Peter J. Park
    bioRxiv 657080; doi: https://doi.org/10.1101/657080
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    HiNT: a computational method for detecting copy number variations and translocations from Hi-C data
    Su Wang, Soohyun Lee, Chong Chu, Dhawal Jain, Geoff Nelson, Jennifer M. Walsh, Burak H. Alver, Peter J. Park
    bioRxiv 657080; doi: https://doi.org/10.1101/657080

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