Ultra-sensitive detection of transposon insertions across multiple families by transposable element display
Pol Vendrell-Mir1, Basile Leduque1, Leandro Quadrana2
1Institute of Plant Sciences Paris-Saclay (IPS2), Centre National de la Recherche Scientifique, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Université Evry, Université Paris-Saclay, Gif Sur Yvette, 91190, France.
Background:
Mobilization of transposable elements (TEs) can generate large effect mutations. However, due to the difficulty of detecting new TE insertions in genomes and the typically rare occurrence of transposition, the actual rate, distribution, and population dynamics of new insertions remain largely unexplored.
Results:
We present a TE display sequencing approach that leverages target amplification of TE extremities to detect non-reference TE insertions with high specificity and sensitivity, enabling the detection of insertions at frequencies as low as 1 in 250,000 within a DNA sample. Moreover, this method allows the simultaneous detection of insertions for distinct TE families, including both retrotransposons and DNA transposons, enhancing its versatility and cost-effectiveness for investigating complex "mobilomes." When combined with nanopore sequencing, this approach enables the identification of insertions using long-read information and achieves a turnaround time from DNA extraction to insertion identification of less than 24 h, significantly reducing the time-to-answer. By analyzing a population of Arabidopsis thaliana plants undergoing a transposition burst, we demonstrate the power of the multiplex TE display sequencing to analyze "evolve and resequence" experiments. Notably, we find that 3-4% of de novo TE insertions exhibit recurrent allele frequency changes indicative of either positive or negative selection.
Conclusions:
TE display sequencing is an ultra-sensitive, specific, simple, and cost-effective approach for investigating the rate and landscape of new TE insertions across multiple families in large-scale population experiments. We provide a step-by-step experimental protocol and ready-to-use bioinformatic pipelines to facilitate its straightforward implementation.
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