title: Detection of Genomic Structural Variants from Next-Generation Sequencing Data.
abstract: Structural variants are genomic rearrangements larger than 50 bp accounting for around 1% of the variation among human genomes. They impact on phenotypic diversity and play a role in various diseases including neurological/neurocognitive disorders and cancer development and progression. Dissecting structural variants from next-generation sequencing data presents several challenges and a number of approaches have been proposed in the literature. In this mini review, we describe and summarize the latest tools - and their underlying algorithms - designed for the analysis of whole-genome sequencing, whole-exome sequencing, custom captures, and amplicon sequencing data, pointing out the major advantages/drawbacks. We also report a summary of the most recent applications of third-generation sequencing platforms. This assessment provides a guided indication - with particular emphasis on human genetics and copy number variants - for researchers involved in the investigation of these genomic events.
authors.affiliation: Department of Neurosciences, Psychology, Pharmacology and Child Health, University of Florence , Florence , Italy.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Tattini
authors.affiliation: Laboratory of Integrative Systems Medicine (LISM), Institute of Informatics and Telematics and Institute of Clinical Physiology, National Research Council , Pisa , Italy.
authors.forename: Romina
authors.initials: R
authors.lastname: D'Aurizio
authors.affiliation: Department of Clinical and Experimental Medicine, University of Florence , Florence , Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Magi
keywords: amplicon sequencing; copy number variants; next generation sequencing; statistical methods; structural variants; whole-exome sequencing; whole-genome sequencing