Huang Zhenyu, Shi Bocheng, Mu Xuechen, Qiao Siyu, Xiao Gangyi, Wang Yan, Xu Ying
College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Systems Biology Lab for Metabolic Reprogramming, Department of Human Genetics and Cell Biology, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.
Brain Sci. 2024 Nov 25;14(12):1180. doi: 10.3390/brainsci14121180.
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. : 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. : 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. : 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. : 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.
准确识别剪接异构体和非编码RNA（lncRNA）并对其进行功能注释，以及全长蛋白质编码转录本，对于理解阿尔茨海默病（AD）中的基因（错误）调控和代谢重编程至关重要。本研究旨在提供一个全面且准确的转录组资源，以改进现有的AD转录本数据库。：基因错误调控和代谢重编程在AD中起关键作用，但现有的转录本数据库缺乏对剪接异构体和lncRNA的准确且全面的识别。本研究旨在生成一个优化的转录组数据集，以加深对AD发病和进展的理解。：利用来自AD前期和AD组织的公开可用RNA测序数据。应用先进的生物信息学工具来组装和注释全长转录本，包括剪接异构体和lncRNA，重点是纠正错误并提高注释准确性。：生成了一个显著改进的转录组数据集，其中包括剪接异构体和lncRNA的详细注释。该数据集扩展了现有AD转录本数据库的范围，并为AD潜在的分子机制提供了新的见解。研究结果表明，与公开可用数据相比，优化后的数据集捕捉到了更多关于AD进展的相关细节。：新开发的转录组资源和相关分析工具为AD研究做出了有价值的贡献，为该疾病的分子机制提供了更深入的见解。这项工作支持未来对AD中基因调控和代谢重编程的研究，并为探索新的治疗靶点奠定了基础。
