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

Asmaa Samy1, Cheng Yong Tham2, Matthew Dyer1

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NanoVar is a free software tool that simplifies the detection and analysis of structural variants (SVs) using long-read sequencing data. This protocol enables researchers to efficiently identify genomic variations, aiding in disease and diversity studies.

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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.