Sequoia: A Framework for Visual Analysis of RNA Modifications from Direct RNA Sequencing Data.

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology
• Computational Biology

Background:

• Long-read direct RNA sequencing offers single-molecule insights into RNA metabolism.
• Existing tools lack detailed analysis of individual signals and signal clustering.
• Advancements in nanopore sequencing necessitate new analytical approaches.

Purpose of the Study:

• Introduce Sequoia, a visual analytics application for nanopore sequencing data.
• Enable interactive analysis of raw nanopore signals for RNA and DNA.
• Facilitate the study of RNA dynamics and posttranscriptional modifications.

Main Methods:

• Developed a Python-based backend with a multi-view graphical interface.
• Implemented data ingestion for Fast5 format nanopore sequencing data.
• Utilized signal similarity clustering, dimensionality reduction, and parameter tuning for visualization.

Main Results:

• Sequoia allows interactive analysis and clustering of nanopore sequencing signals.
• Visualizations can effectively separate modified RNA sequences from unmodified ones.
• The tool enhances the exploration of dynamic RNA characteristics.

Conclusions:

• Sequoia provides a novel visual analytics approach for nanopore sequencing data.
• The application supports hypothesis generation and insight discovery in RNA biology.
• Sequoia is extensible to both RNA and DNA datasets and available for use.