nano-ID

nano-ID characterizes the synthesis and stability of RNA isoforms by combining metabolic RNA labeling with long-read nanopore native RNA sequencing and machine learning to estimate stability and identify determinants such as sequence composition, poly(A)-tail length, secondary structure, translation efficiency, and interactions with RNA-binding proteins.

Key Features:

• Metabolic RNA Labeling: Tags newly synthesized RNA molecules to distinguish nascent transcripts from pre-existing RNA.
• Long-Read Nanopore Sequencing: Captures full-length native RNA molecules via long-read nanopore sequencing to resolve individual isoforms and preserve structural information.
• Machine Learning Integration: Applies machine learning algorithms to process sequencing data, estimate RNA stability, and identify key determinants of stability.
• Isoform-Resolved Stability Estimation: Provides synthesis and stability estimates at the level of individual RNA isoforms and molecules.
• Determinant Analysis: Quantifies contributions of sequence composition, poly(A)-tail length, secondary structure, translation efficiency, and RNA-binding protein interactions to RNA stability.

Scientific Applications:

• Isoform-Resolved RNA Metabolism: Enables study of synthesis and decay dynamics for individual RNA isoforms and molecules.
• Cell-State and Condition Studies: Compares how different cell states and environmental conditions influence RNA isoform synthesis and stability.
• Heat Shock Response in Human Cells: Detects changes in stability of various RNA isoforms during the heat shock response in human cells.
• Gene Expression Regulation and RNA Processing: Dissects regulatory mechanisms affecting gene expression, RNA processing, and cellular responses to environmental changes.

Methodology:

nano-ID combines metabolic labeling, long-read nanopore native RNA sequencing, and machine learning analysis of sequencing data to derive RNA synthesis and stability estimates and identify determinants of RNA metabolism.