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Open Access Research

The fine-scale architecture of structural variants in 17 mouse genomes

Binnaz Yalcin12*, Kim Wong3, Amarjit Bhomra1, Martin Goodson1, Thomas M Keane3, David J Adams3 and Jonathan Flint1

Author Affiliations

1 The Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK

2 The Center for Integrative Genomics, Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland

3 The Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH, UK

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Genome Biology 2012, 13:R18  doi:10.1186/gb-2012-13-3-r18

Published: 20 March 2012

Abstract

Background

Accurate catalogs of structural variants (SVs) in mammalian genomes are necessary to elucidate the potential mechanisms that drive SV formation and to assess their functional impact. Next generation sequencing methods for SV detection are an advance on array-based methods, but are almost exclusively limited to four basic types: deletions, insertions, inversions and copy number gains.

Results

By visual inspection of 100 Mbp of genome to which next generation sequence data from 17 inbred mouse strains had been aligned, we identify and interpret 21 paired-end mapping patterns, which we validate by PCR. These paired-end mapping patterns reveal a greater diversity and complexity in SVs than previously recognized. In addition, Sanger-based sequence analysis of 4,176 breakpoints at 261 SV sites reveal additional complexity at approximately a quarter of structural variants analyzed. We find micro-deletions and micro-insertions at SV breakpoints, ranging from 1 to 107 bp, and SNPs that extend breakpoint micro-homology and may catalyze SV formation.

Conclusions

An integrative approach using experimental analyses to train computational SV calling is essential for the accurate resolution of the architecture of SVs. We find considerable complexity in SV formation; about a quarter of SVs in the mouse are composed of a complex mixture of deletion, insertion, inversion and copy number gain. Computational methods can be adapted to identify most paired-end mapping patterns.