Assemblytics: a web analytics tool for the detection of variants from an assembly

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• De novo genome assembly is crucial for understanding genomic variations.
• Detecting variants accurately, especially in repetitive regions, remains a challenge.
• Comparative genomics requires robust tools for identifying genomic differences.

Purpose of the Study:

• To develop and present Assemblytics, a web application for variant analysis in de novo genome assemblies.
• To introduce a novel anchor filtering approach for improved variant detection robustness.
• To facilitate the comparison of genomes, including aberrant genomes like human cancers, and related species.

Main Methods:

• Developed a web application, Assemblytics, for variant detection and analysis.
• Implemented a unique anchor filtering method to handle repetitive genomic elements.
• Classified six distinct variant types based on alignment signatures.
• Integrated interactive visualizations for exploring variant distributions.

Main Results:

• Assemblytics effectively detects and analyzes variants from de novo genome assemblies against a reference.
• The anchor filtering approach enhances robustness against repetitive sequences.
• Six classes of variants are identified based on alignment signatures.
• The tool supports comparisons for aberrant genomes (e.g., human cancers) and related species.

Conclusions:

• Assemblytics provides a robust and versatile platform for variant analysis in genome assemblies.
• The application aids in comparative genomics and the study of genomic abnormalities.
• Interactive visualizations enhance the exploration and understanding of variant data.