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Uncovering structural genomic contents of wheat

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Çağırıcı, Halise Büşra (2019) Uncovering structural genomic contents of wheat. [Thesis]

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Official URL: http://risc01.sabanciuniv.edu/record=b2315133_ (Table of contents)

Abstract

Production rate of wheat, an important food source worldwide, is significantly limited by both biotic and abiotic stress factors. Development of stress resistant cultivars are highly dependent on the understanding of the molecular mechanisms and structural elements in wheat and/or wheat interacting species. The huge and complex genome of bread wheat (BBAADD genome) has stood as a vital obstruction for understanding the molecular mechanisms until the recent availability of wheat reference genome. In this study, we provided improved and/or novel methodologies to reveal structural elements in plants. These methodologies include miRNA identification, manual curation of lncRNAs, identification of lncRNAs using wheat specific prediction models and a comparative analysis of WES data analysis tools. Using these techniques, we here focused on the uncovering of structural genomic contents of wheat. With an improved identification methodologies and manual annotation of lncRNAs, we revealed several miRNAs and lncRNAs in Triticum turgidum species and Wheat stem sawfly (WSS), a major pest of wheat. We provided a comprehensive transcriptome analysis of tetraploid wheat varieties and revealed drought responsive transcripts. Additionally, we presented the first clues of miRNA mobility between WSS larva and hexaploid wheat. Thereby, besides enrichment of the genetic information available for wheat species, this study provides important elements driving both abiotic and biotic stress responses in wheat. In this study, we also applied machine learning approaches for the fast and accurate prediction of lncRNAs in wheat species. With annotated genomes of hexaploid and tetraploid wheats, we provided better accuracy scores (99.81%) over the most popular tools available. Finally, we conducted a comparative analysis of the tools used for variant discovery. Among eight aligners and three callers, we chose the best combination for the variant calling in wheat. Later, we performed variant calling in 48 lines of elite wheat cultivars using the best tool sets. Overall, this study focused on the improvements on the identification of miRNAs, lncRNAs and structural variations in wheat

Item Type:Thesis
Uncontrolled Keywords:Wheat. -- lncRNAs. -- miRNAs. -- SNPs. -- Machine learning. -- Buğday. -- Yapay zeka ile öğrenme.
Subjects:T Technology > TA Engineering (General). Civil engineering (General) > TA164 Bioengineering
ID Code:39269
Deposited By:IC-Cataloging
Deposited On:27 Sep 2019 10:37
Last Modified:27 Sep 2019 10:37

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