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

Dhawal Jain1, Chong Chu1, Burak Han Alver1

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.

Bioinformatics (Oxford, England)
|November 2, 2020
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Summary

Hi-C data can now detect mobile transposable element (TE) insertions genome-wide using the new HiTea pipeline. This method complements whole-genome sequencing for a comprehensive understanding of the TE-insertion landscape.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Hi-C is a widely used technique for studying 3D chromatin conformation.
  • Recent advancements utilize Hi-C data for genome assembly and structural variation detection.
  • Mobile transposable elements (TEs) are significant contributors to genome evolution and variation.

Purpose of the Study:

  • To develop and validate a novel computational pipeline for detecting mobile transposable element (TE) insertions using Hi-C data.
  • To assess the efficacy of Hi-C based TE detection compared to existing whole-genome sequencing methods.

Main Methods:

  • Development of the Hi-C-based TE analyzer (HiTea) pipeline.
  • Utilization of clipped Hi-C reads and discordant read pairs for TE insertion detection.
  • Application of HiTea to human cell-line Hi-C samples.

Main Results:

  • HiTea successfully detects insertions of three major families of active human TEs.
  • The pipeline demonstrates competitive performance against whole-genome sequencing callers, despite uneven Hi-C data coverage.
  • HiTea effectively supplements whole-genome sequencing for characterizing the TE-insertion landscape.

Conclusions:

  • Hi-C data, analyzed with the HiTea pipeline, provides a valuable method for genome-wide transposable element insertion detection.
  • HiTea offers a complementary approach to whole-genome sequencing, enhancing the characterization of TE insertions.
  • The HiTea pipeline expands the applications of Hi-C technology in genomic variation analysis.