Giraffe: A tool for comprehensive processing and visualization of multiple long-read sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Third-generation sequencing offers long, high-quality reads crucial for biological discovery.
• Comparative analysis of diverse sequencing data is vital for establishing baselines and understanding biological mechanisms.
• Current tools primarily focus on single-sample quality control, hindering multi-dataset comparisons and visualization.

Purpose of the Study:

• To introduce Giraffe, a Python3 command-line tool for comparative analysis and visualization of long-read sequencing data.
• To address the limitations of existing tools in handling multiple samples and platforms.
• To provide researchers with an accessible solution for assessing read quality, sequencing bias, and methylation proportions.

Main Methods:

• Development of Giraffe, a Python3-based command-line interface.
• Implementation of comparative analysis features for read quality, sequencing bias, and methylation.
• Application of Giraffe to diverse datasets including different sequencing methods, platforms, tissues, and biological replicates.

Main Results:

• Giraffe enables effective comparison of long-read sequencing data across various samples and platforms.
• The tool facilitates assessment of read quality, sequencing bias, and genomic regional methylation proportions.
• Demonstrated utility in comparing whole genome amplification vs. shotgun methods, ONT vs. PacBio platforms, different tissues, and biological replicates.

Conclusions:

• Giraffe provides a comprehensive solution for comparative analysis and visualization of long-read sequencing data.
• The tool simplifies complex bioinformatics workflows, making multi-dataset comparison accessible.
• Giraffe is valuable for researchers studying biological mechanisms and establishing sequencing benchmarks.