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HiTea: a computational pipeline to identify non-reference transposable element insertions in Hi-C data

Dhawal Jain, Chong Chu, Burak Han Alver, Soohyun Lee, Eunjung Alice Lee, Peter J. Park
doi: https://doi.org/10.1101/2020.04.27.060145
Dhawal Jain
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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Chong Chu
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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Burak Han Alver
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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Soohyun Lee
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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Eunjung Alice Lee
2Division of Genetics and Genomics, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
3Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Peter J. Park
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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  • For correspondence: peter_park@hms.harvard.edu
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Abstract

Hi-C is a common technique for assessing three-dimensional chromatin conformation. Recent studies have shown that long-range interaction information in Hi-C data can be used to generate chromosome-length genome assemblies and identify large-scale structural variations. Here, we demonstrate the use of Hi-C data in detecting mobile transposable element (TE) insertions genome-wide. Our pipeline HiTea (Hi-C based Transposable element analyzer) capitalizes on clipped Hi-C reads and is aided by a high proportion of discordant read pairs in Hi-C data to detect insertions of three major families of active human TEs. Despite the uneven genome coverage in Hi-C data, HiTea is competitive with the existing callers based on whole genome sequencing (WGS) data and can supplement the WGS-based characterization of the TE insertion landscape. We employ the pipeline to identify TE insertions from human cell-line Hi-C samples. HiTea is available at https://github.com/parklab/HiTea and as a Docker image.

Competing Interest Statement

The authors have declared no competing interest.

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 April 28, 2020.
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HiTea: a computational pipeline to identify non-reference transposable element insertions in Hi-C data
Dhawal Jain, Chong Chu, Burak Han Alver, Soohyun Lee, Eunjung Alice Lee, Peter J. Park
bioRxiv 2020.04.27.060145; doi: https://doi.org/10.1101/2020.04.27.060145
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HiTea: a computational pipeline to identify non-reference transposable element insertions in Hi-C data
Dhawal Jain, Chong Chu, Burak Han Alver, Soohyun Lee, Eunjung Alice Lee, Peter J. Park
bioRxiv 2020.04.27.060145; doi: https://doi.org/10.1101/2020.04.27.060145

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