Transposable elements (Transposons; TEs) constitute the majority of DNA in genomes and are a major source of genetic polymorphisms. TEs act as potential regulators of gene expression and lead to phenotypic plasticity in plants and animals. In crops, several TEs were identified to influence alleles associated with important agronomic traits, such as apical dominance in maize and seed number in rice. Crops may harbor more TE-mediated genetic regulations than expected in view of multifunctional TEs in genomes. However, tools that accurately annotate TEs and clarify their associations with agronomic traits are still lacking, which largely limits applications of TEs in crop breeding. Here we 1) evaluate performances of popular tools and strategies to identify TEs in genomes, 2) develop a tool 'DeepTE' to annotate TEs based on deep learning models, and 3) develop a tool 'TE-marker' to identify potential TE-regulated alleles associated with agronomic traits. As a result, we propose a series of recommendations and a guideline to develop a comprehensive library to precisely identify TEs in genomes. Secondly, 'DeepTE' classifies TEs into 15-24 super families according to sequences from plants, metazoans, and fungi. For unknown sequences, this tool can distinguish non-TEs and TEs in plant species. Finally, the 'TE-marker' tool builds a TE-based marker system that is able to cluster rice populations similar to a classical SNP marker approach. This system can also detect association peaks that are equivalent to the ones produced by SNP markers. 'TE-marker' is a novel complementary approach to the classical SNP markers that it assists in revealing population structures and in identifying alleles associated with agronomic traits. / Doctor of Philosophy / Transposable elements (Transposons; TEs) are DNA fragments that can jump and integrate into new positions in the genome. TEs potentially act as regulators of gene expression and alter traits of plants and animals. In crops, several TEs were identified to influence functions of genes that control important agronomic traits, such as branching in maize and seed number in rice. However, tools that identify these associations in the crops are still lacking, which largely limits applications of TEs in crop breeding. Here we evaluated performance of popular tools and strategies that identify TEs, and provide a series of recommendations to efficiently apply these tools to the TE identification. In view of structural and sequence differences, TEs are classified into multiple families. We developed a 'DeepTE' tool to precisely cluster TEs into different families using a deep learning method. Finally, a 'TE-marker' tool was developed to build TE-based genetic markers to identify nearby alleles associated with agronomic traits. Overall, this work could promote the use of TEs as markers in improving quality and yielding crops.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/106637 |
| Date | 21 May 2020 |
| Creators | Yan, Haidong |
| Contributors | Horticulture, Zhao, Bingyu, Li, Song, Bombarely Gomez, Aureliano, Vinatzer, Boris A., Haak, David C. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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