Data From - TE Density: a tool to investigate the biology of transposable elements
Teresi, Scott; Edger, Patrick (2022), Data From - TE Density: a tool to investigate the biology of transposable elements, Dryad, Dataset, https://doi.org/10.5061/dryad.sj3tx965m
Background: Transposable elements (TEs) are powerful creators of genotypic and phenotypic diversity due to theirinherent mutagenic capabilities and in this way they serve as a deep reservoir of sequences for genomic variation. As agents of genetic disruption, a TE’s potential to impact phenotype is partially a factor of its location in the genome. Previous research has shown TEs’ ability to impact the expression of neighboring genes, however our understanding of this trend is hampered by the exceptional amount of diversity in the TE world, and a lack of publicly availablecomputational methods that quantify the presence of TEs relative to genes.
Results: Here, we have developed a tool to more easily quantify TE presence relative to genes through the useof only a gene and TE annotation, yielding a new metric we call TE density. Briefly defined as the proportion of TE-occupied base-pairs relative to a window-size of the genome. This new pipeline reports TE density for each gene in the genome, for each type descriptor of TE (order and superfamily), and for multiple positions and distances relative to the gene (upstream, intragenic, and downstream) over sliding, user-defined windows. In this way, we overcome previous limitations to the study of TE-gene relationships by focusing on all TE types present in the genome, utilizing flexible genomic distances for measurement, and reporting a TE presence metric for every gene in the genome.
Conclusions: Together, this new tool opens up new avenues for studying TE-gene relationships, genome architecture,comparative genomics, along with the tremendous diversity present in the TE world.
Data Availability: TE Density is open-source and freely available at: https://github.com/sjteresi/TE_Density.
Please refer to the README available with this Dryad submission, the Implementation section of the publication, and the README of the project GitHub repository https://github.com/sjteresi/TE_Density
Michigan State University