Construction of a Dataset for All Expressed Transcripts for Alzheimer’s Disease Research

Accurate identification and functional annotation of splicing isoforms and non-coding RNAs (lncRNAs), alongside full-length protein-encoding transcripts, are critical for understanding gene (mis)regulation and metabolic reprogramming in Alzheimer's disease (AD). This study aims to provide a comprehensive and accurate transcriptome resource to improve existing AD transcript databases. <b>Background/Objectives</b>: Gene mis-regulation and metabolic reprogramming play a key role in AD, yet existing transcript databases lack accurate and comprehensive identification of splicing isoforms and lncRNAs. This study aims to generate a refined transcriptome dataset, expanding the understanding of AD onset and progression. <b>Methods</b>: Publicly available RNA-seq data from pre-AD and AD tissues were utilized. Advanced bioinformatics tools were applied to assemble and annotate full-length transcripts, including splicing isoforms and lncRNAs, with an emphasis on correcting errors and enhancing annotation accuracy. <b>Results</b>: A significantly improved transcriptome dataset was generated, which includes detailed annotations of splicing isoforms and lncRNAs. This dataset expands the scope of existing AD transcript databases and provides new insights into the molecular mechanisms underlying AD. The findings demonstrate that the refined dataset captures more relevant details about AD progression compared to publicly available data. <b>Conclusions</b>: The newly developed transcriptome resource and the associated analysis tools offer a valuable contribution to AD research, providing deeper insights into the disease's molecular mechanisms. This work supports future research into gene regulation and metabolic reprogramming in AD and serves as a foundation for exploring novel therapeutic targets.