Abstract
Summary: Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS). Different SV detection methods have been developed; however, each is limited to specific kinds of SVs with varying accuracy and resolution. Previous works have attempted to combine different methods, but they still suffer from poor accuracy particularly for insertions. We propose MetaSV, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution. MetaSV proceeds by merging SVs from multiple tools for all types of SVs. It also analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs. Local assembly in combination with dynamic programming is used to improve breakpoint resolution. Paired-end and coverage information is used to predict SV genotypes. Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes.Availability and implementation: Code in Python is at http://bioinform.github.io/metasv/.Contact: rd@bina.comSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
Structural variations (SVs) have been implicated in contributing to genomic diversity as well as genomic disorders (Stankiewicz and Lupski, 2010)
We demonstrate the effectiveness of MetaSV using the VarSim simulation and validation framework (Mu et al, 2014)
Our results show that each method has varying performance in different SV size ranges
Summary
SVs have been implicated in contributing to genomic diversity as well as genomic disorders (Stankiewicz and Lupski, 2010). Prior work (Lam et al, 2012; Mills et al, 2011) has shown that variant calls made by multiple tools and methods generally are more accurate. For this reason, tools have been developed to employ multiple methods, e.g. DELLY (Rausch et al, 2012), LUMPY (Layer et al, 2014) and those that merge the results from multiple tools, such as SVMerge (Wong et al, 2010). Attempts to address the limitations of existing SV merging tools for detecting SVs of different types and sizes with high accuracy and resolution
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