NanoVar: a comprehensive workflow for structural variant detection to uncover the genome's hidden patterns

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Structural variants (SVs) are crucial for genomic diversity and disease development but are challenging to characterize due to their complexity.
• Advancements in third-generation sequencing necessitate improved analytical strategies for SV detection.

Purpose of the Study:

• To provide a detailed protocol for using NanoVar, an open-source package for efficient and reliable structural variant detection in long-read sequencing data.
• To enable researchers, even those with limited command-line experience, to perform comprehensive SV analysis.

Main Methods:

• Detailed step-by-step instructions for the NanoVar protocol.
• Integration guidance with other SV calling platforms for whole-genome long-read sequencing data.
• Instructions tailored for single-sample, cohort, and genome instability analyses.

Main Results:

• NanoVar facilitates efficient and reliable SV detection using long-read sequencing.
• The protocol supports various study designs, including population genomics and non-model organism analysis.
• SV visualization, filtering, and annotation are integral parts of the protocol.

Conclusions:

• The NanoVar protocol empowers researchers to easily identify and analyze structural variants.
• Comprehensive SV analysis, including visualization and annotation, is achievable with conventional computational resources.
• The entire process, from read mapping to SV analysis, can be completed in approximately 2-5 hours for a typical human dataset.