The visualization of SVs is a critical step for interpreting their potential impacts. Because the outputs of SV callers still include many false positives/negatives, the manual inspection of tens of thousands of SVs using read alignments and genomic annotations is often needed to filter out false positive SVs. Further, whole-genome sequencing using the third-generation sequencing technology identified around 20,000 SVs against the reference genome per human genome. Indeed, recent studies using long-read sequencing even revealed 3-hop fusion genes and the long-range structure of chromothripsis. With the advent of third-generation sequencing technologies that provide us with long reads, more complex SVs are expected to be identified because longer reads are more easily aligned to the reference genome. Although SV identification using the second-generation sequencing (often referred to as the next-generation sequencing) technologies has suffered from an overwhelmingly large amount of false positives due to their short read length, comprehensive de novo SV detection are realistic, which was impossible with the older sequencing technologies. Massively parallel sequencing technologies have enabled the de novo detection of SVs of varying sizes. The last step, SV visualization, is a critical step in the SV analysis below, we explain the importance of SV visualization tools in the entire SV analysis. To identify SVs in a whole genome, the following steps are usually performed: (1) library preparation and whole-genome shotgun sequencing, (2) aligning the shotgun reads, (3) SV identification (SV call), (4) SV annotation, and (5) SV visualization. SVs are known to be associated with human traits, genetic diseases, or cancers, and therefore identifying SVs plays an important role in genome analysis. Structural variations (SVs) are defined as large variations, which are often 50 bp or longer. We hope that this review will serve as a guide for readers on the currently available SV visualization tools and lead to the development of new SV visualization tools in the near future. Next, we introduce the features of individual SV visualization tools from several aspects, including whether SV views are integrated with annotations, whether long-read alignment is displayed, whether underlying data structures are graph-based, the type of SVs shown, whether auditing is possible, whether bird’s eye view is available, sequencing platforms, and the number of samples. View modules allow readers to understand the features of each SV visualization tool quickly. We first categorize the ways in which SV visualization tools display SVs into ten major categories, which we denote as view modules. This review targets users who wish to visualize a set of SVs identified from the massively parallel sequencing reads of an individual human genome. Here, we provide a comprehensive survey of over 30 SV visualization tools to help users choose which tools to use. Given that there are many sequencing platforms used for SV identification and given that how best to visualize SVs together with other data, such as read alignments and annotations, depends on research goals, there are dozens of SV visualization tools designed for different research goals and sequencing platforms. Visualizing structural variations (SVs) is a critical step for finding associations between SVs and human traits or diseases.
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