xTea

xTea identifies transposable element (TE) insertions in WES and WGS sequencing data from short-read and long-read platforms (Illumina, PacBio, Nanopore and hybrid combinations) to enable characterization and annotation of TE-mediated genomic variation.


Key Features:

  • Comprehensive TE insertion identification: Detects TE insertions across genomic regions including highly repetitive and complex loci such as centromeres and telomeres.
  • Support for short-read and long-read data: Processes both short-read and long-read sequencing datasets and hybrid combinations for integrated analysis.
  • Enhanced performance on germline and somatic calls: Demonstrates improved discovery of germline and somatic TE insertions compared to short-read–only methods.
  • Cataloging and assembly of polymorphic insertions: With long-read data enables creation of comprehensive catalogs, including full assembly and annotation of insertional sequences such as pseudogenes and endogenous retroviruses.
  • Exploration of repetitive element reservoirs: Facilitates analysis revealing, for example, groups of full-length LINE-1 (L1) elements within centromeres as potential reservoirs of active TEs.

Scientific Applications:

  • Genome structure and variation analysis: Characterizes how TE insertions alter genome architecture and contribute to structural variation.
  • Gene regulation and disease studies: Investigates TE-mediated disruption of gene regulation and contributions to disease pathogenesis.
  • Germline and somatic mutation discovery: Detects and differentiates germline versus somatic TE insertions in sequencing datasets.
  • Polymorphic TE and retroelement cataloging: Generates annotated catalogs of polymorphic TEs and retroelements for population and functional studies.

Methodology:

Uses advanced algorithms to detect TE insertions by processing short-read and long-read sequencing inputs (WES/WGS from Illumina, PacBio, Nanopore or hybrid), integrates data across platforms, and performs assembly and annotation of insertional sequences.

Topics

Details

License:
Other
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python, Other
Added:
10/5/2021
Last Updated:
10/10/2021

Operations

Publications

Chu C, Borges-Monroy R, Viswanadham VV, Lee S, Li H, Lee EA, Park PJ. Comprehensive identification of transposable element insertions using multiple sequencing technologies. Nature Communications. 2021;12(1). doi:10.1038/s41467-021-24041-8. PMID:34158502. PMCID:PMC8219666.

PMID: 34158502
PMCID: PMC8219666
Funding: - U.S. Department of Health & Human Services | NIH | National Institute of Mental Health: U01MH106883 - U.S. Department of Health & Human Services | NIH | NCI | Division of Cancer Epidemiology and Genetics, National Cancer Institute: R03CA249364

Documentation

Downloads