Hide and seek: de novo identification in sugar beet reveals impact of non-autonomous LTR retrotransposons

Abstract Plant genomes are filled with retrotransposons and their derivatives, constantly undergoing sequence diversification and structural rearrangement. Among them, short, non-autonomous retrotransposons lack full coding capacity and often form subfamilies. As a result, non-autonomous retrotransposons are incompletely identified in most to all genome assemblies. Here, we capitalize on our comprehensive understanding of the transposable element (TE) landscape in sugar beet ( Beta vulgaris ) to assess the extent of the blind spot for non-autonomous long terminal repeat (LTR) retrotransposons. This use case serves to answer if all of these sequences are derivatives of easier-to-identify full-length elements or if there is more variability that is currently overlooked. For this we applied a semi-automated structural discovery workflow followed by in-depth manual verification to characterize non-autonomous LTR retrotransposons in sugar beet. We retrieve more than 100 non-autonomous LTR retrotransposon families that lack complete autonomous coding capacity, including canonical terminal-repeat retrotransposons in miniature (TRIMs), elongated non-coding derivatives and families retaining fragmented coding remnants. The identified families span a broad range, including elements exceeding 15,000 bp in length and display evidence for reshuffling and modular evolution. Only a subset of families could be confidently linked to autonomous retrotransposons, showing sequence diversification within the non-autonomous LTR retrotransposon fraction beyond the autonomous genomic templates. We highlight that a large fraction of non-autonomous LTR retrotransposons is incompletely recovered with the current TE identification workflows, even if the output is well-curated and condensed into TE libraries and suggest procedures to remedy this gap. This study gives a genome-wide view into the non-autonomous LTR retrotransposon landscape of a single plant genome and highlights the importance of structure-based approaches for their identification and classification.