Abstract
Transfer RNA (tRNA), a key component of the translation machinery, plays critical roles in stress conditions and various diseases. While knowledge regarding the importance of tRNA function is increasing, its biological roles are still not well understood. There is currently no comprehensive database or web server providing the expression landscape of tRNAs across a variety of human tissues and diseases. Here, we constructed a user-friendly and interactive database, DBtRend, which provides a profile of mature tRNA expression across various biological conditions by reanalyzing the small RNA or microRNA sequencing data from the Cancer Genome Atlas (TCGA) and NCBI’s Gene Expression Omnibus (GEO) in humans. Users can explore not only the expression values of mature individual tRNAs in the human genome, but also those of isodecoders and isoacceptors based on our specific pipelines. DBtRend provides the expressed patterns of tRNAs, the differentially expressed tRNAs in different biological conditions, and the information of samples or patients, tissue types, and molecular subtype of cancers. The database is expected to help researchers interested in functional discoveries of tRNAs.
Highlights
Transfer RNAs are key components of the translation machinery that function as adaptors to deliver amino acids to the ribosome by matching mRNA codons with their corresponding amino acids
We identified 118 individual Transfer RNA (tRNA) (61 upregulated upregulated and 43 downregulated isodecoders), and 14 isoacceptors, 72 isodecoders (29 upregulated and 43 downand eight downregulated isoacceptors) in the late stage of Alzheimer’s disease (AD) compared with the early regulated isodecoders), and 14 isoacceptors
DBtRend is an interactive web-based database to explore and investigate tRNA expression under various biological conditions retrieved from the the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets
Summary
Transfer RNAs (tRNAs) are key components of the translation machinery that function as adaptors to deliver amino acids to the ribosome by matching mRNA codons with their corresponding amino acids. The abundance of tRNAs varies across biological conditions including tissue type [1], cell cycle [2], developmental stage [3], and disease status (e.g., diabetes mellitus [4], Huntington’s disease [5], and cancers [6]). These studies highlight the need to comprehensively and systemically identify how tRNA expression differs in tissues and biological conditions. Several studies have identified tRNA expression profiles, the field is currently limited by the small number of samples and studies and the lack of exploration of various biological conditions. Small RNA sequencing data presents limitations in the quantification accuracy of mature tRNA due to the quantification bias which caused the complexity of the tRNA structure and difficulties in discriminating
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