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Detection of Genomic Structural Variants from Next-Generation Sequencing Data.
Lorenzo Tattini1, Romina D'Aurizio2, Alberto Magi3
1Department of Neurosciences, Psychology, Pharmacology and Child Health, University of Florence , Florence , Italy.
Frontiers in Bioengineering and Biotechnology
|July 11, 2015
Summary
This review covers tools for analyzing structural variants, which are large genomic changes impacting human health and disease. It highlights the latest methods for whole-genome sequencing and other data types, aiding researchers in genomic investigations.
Area of Science:
- Genomics
- Bioinformatics
- Human Genetics
Background:
- Structural variants (SVs) are genomic rearrangements >50 bp, comprising ~1% of human genome variation.
- SVs influence phenotypic diversity and are implicated in diseases like cancer and neurocognitive disorders.
- Analyzing SVs from next-generation sequencing (NGS) data presents significant computational and methodological challenges.
Purpose of the Study:
- To review and summarize the latest computational tools and algorithms for structural variant detection.
- To assess the advantages and drawbacks of different SV analysis approaches across various sequencing data types.
- To provide an overview of emerging applications of third-generation sequencing for SV analysis in human genetics.
Main Methods:
- Systematic review of recent literature on structural variant detection tools.
- Analysis of algorithms underlying tools for whole-genome sequencing (WGS), whole-exome sequencing (WES), and amplicon sequencing.
- Summary of applications utilizing third-generation sequencing (TGS) platforms for SV identification.
Main Results:
- Detailed comparison of current SV detection tools, highlighting their strengths and limitations for different data types.
- Evaluation of algorithms used in SV analysis, including those for copy number variants (CNVs).
- Overview of TGS capabilities in resolving complex structural variants and large genomic rearrangements.
Conclusions:
- The choice of SV detection tool depends on the sequencing data type, specific research question, and desired resolution.
- Third-generation sequencing offers promising advancements for comprehensive structural variant analysis.
- Accurate identification of structural variants is crucial for understanding human genetic variation and disease pathogenesis.
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