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Background <p>Investigating genetic progression and lineage using sequence data is a fundamental focus within the realm of viral genome research. Given the substantial increase in publicly accessible genome sequences, tools designed for extensive data analytics, particularly insightful visualizations play an important role in elucidating the spatio-temporal evolution as well as the diversity of viral lineages. Existing genome data analysis methods frequently rely on multiple sequence alignment (MSA) and phylogeny computations, making integration of new sequences computationally expensive. Such methods are often metadata agnostic and typically utilize a global distance metric between candidate sequences. We present BOVIZ, a fast and inexpensive method for rich visualization of large-scale genome collections. BOVIZ leverages variant-level features from candidate sequences and employs a novel Bag-Of-Variants (BOV) method that transforms high-dimensional variant feature vectors into a 2D-space, effectively capturing sequence (dis)similarities. BOVIZ circumvents the need for expensive MSA and phylogeny analyses, enabling efficient integration of novel sequences and faster visualization of large-scale sequences on a standard desktop. Furthermore, BOVIZ supports a variety of filters, including metadata filters and selection of genomic regions of interest for effectively capturing localized similarities and dissimilarities on-the-fly. We perform extensive benchmarking of BOVIZ with state-of-the-art visualization methods using the SARS-CoV-2 viral genome dataset.</p> Results <p>BOVIZ consistently outperformed these methods in capturing inter-sequence similarity and diversity, offering superior visualization of spatio-temporal and clade-level evolution. It is able to effectively distinguish SARS-CoV-2 lineages into distinct clusters, visualize the evolution of specific sublineages like Omicron and their variant patterns, as well as visualize potential sub-cluster within the SARS-CoV-2 Delta variant.</p> Conclusion <p>BOVIZ is an effective visualization tool for genome surveillance, complementing resource-intensive deep analytics by providing key insights about the underlying genome landscape in a fast and scalable manner.</p>

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Fast and inexpensive visualization of genome collection at scale

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Sujatha Kotte,
  • Naina Tiwari,
  • Kavya Vaddadi,
  • Vangala Govindakrishnan Saipradeep,
  • Thomas Joseph,
  • Aditya Rao,
  • Rajgopal Srinivasan,
  • Naveen Sivadasan

摘要

Background

Investigating genetic progression and lineage using sequence data is a fundamental focus within the realm of viral genome research. Given the substantial increase in publicly accessible genome sequences, tools designed for extensive data analytics, particularly insightful visualizations play an important role in elucidating the spatio-temporal evolution as well as the diversity of viral lineages. Existing genome data analysis methods frequently rely on multiple sequence alignment (MSA) and phylogeny computations, making integration of new sequences computationally expensive. Such methods are often metadata agnostic and typically utilize a global distance metric between candidate sequences. We present BOVIZ, a fast and inexpensive method for rich visualization of large-scale genome collections. BOVIZ leverages variant-level features from candidate sequences and employs a novel Bag-Of-Variants (BOV) method that transforms high-dimensional variant feature vectors into a 2D-space, effectively capturing sequence (dis)similarities. BOVIZ circumvents the need for expensive MSA and phylogeny analyses, enabling efficient integration of novel sequences and faster visualization of large-scale sequences on a standard desktop. Furthermore, BOVIZ supports a variety of filters, including metadata filters and selection of genomic regions of interest for effectively capturing localized similarities and dissimilarities on-the-fly. We perform extensive benchmarking of BOVIZ with state-of-the-art visualization methods using the SARS-CoV-2 viral genome dataset.

Results

BOVIZ consistently outperformed these methods in capturing inter-sequence similarity and diversity, offering superior visualization of spatio-temporal and clade-level evolution. It is able to effectively distinguish SARS-CoV-2 lineages into distinct clusters, visualize the evolution of specific sublineages like Omicron and their variant patterns, as well as visualize potential sub-cluster within the SARS-CoV-2 Delta variant.

Conclusion

BOVIZ is an effective visualization tool for genome surveillance, complementing resource-intensive deep analytics by providing key insights about the underlying genome landscape in a fast and scalable manner.