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

Area of Science:

• Transcriptomics
• Bioinformatics
• Neuroscience

Background:

• Gene misregulation and metabolic reprogramming are key in Alzheimer's disease (AD).
• Existing AD transcript databases lack comprehensive identification of splicing isoforms and long non-coding RNAs (lncRNAs).

Purpose of the Study:

• To generate a comprehensive and accurate transcriptome resource for AD.
• To improve existing AD transcript databases by refining annotation of splicing isoforms and lncRNAs.

Main Methods:

• Utilized publicly available RNA-sequencing data from pre-AD and AD tissues.
• Applied advanced bioinformatics tools for transcript assembly and annotation.
• Focused on correcting errors and enhancing annotation accuracy for full-length transcripts, splicing isoforms, and lncRNAs.

Main Results:

• Generated a significantly improved transcriptome dataset for AD.
• Included detailed annotations of splicing isoforms and lncRNAs, expanding existing AD transcript databases.
• The refined dataset captures more relevant details of AD progression compared to publicly available data.

Conclusions:

• The developed transcriptome resource offers valuable insights into AD molecular mechanisms.
• Supports future research on gene regulation and metabolic reprogramming in AD.
• Provides a foundation for identifying novel therapeutic targets for Alzheimer's disease.