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TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches.

Mourdas MohamedFrancois SabotMarion VaroquiBruno MugatKatell AudouinAlain PélissonAnna-Sophie Fiston-LavierSéverine Chambeyron
Published in: Genome biology (2023)
Transposable Element MOnitoring with LOng-reads (TrEMOLO) is a new software that combines assembly- and mapping-based approaches to robustly detect genetic elements called transposable elements (TEs). Using high- or low-quality genome assemblies, TrEMOLO can detect most TE insertions and deletions and estimate their allele frequency in populations. Benchmarking with simulated data revealed that TrEMOLO outperforms other state-of-the-art computational tools. TE detection and frequency estimation by TrEMOLO were validated using simulated and experimental datasets. Therefore, TrEMOLO is a comprehensive and suitable tool to accurately study TE dynamics. TrEMOLO is available under GNU GPL3.0 at https://github.com/DrosophilaGenomeEvolution/TrEMOLO .
Keyphrases
  • high resolution
  • single cell
  • electronic health record
  • genome wide
  • data analysis
  • rna seq
  • high density
  • mass spectrometry
  • copy number
  • deep learning
  • artificial intelligence
  • gene expression
  • quantum dots