Haplotype Explorer: an infection cluster visualization tool for spatiotemporal dissection of the COVID-19 pandemic

  1. Tetsuro Kawano-Sugaya
  2. Koji Yatsu
  3. Tsuyoshi Sekizuka
  4. Kentaro Itokawa
  5. Masanori Hashino
  6. Rina Tanaka
  7. Makoto Kuroda

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Abstract

The worldwide eruption of COVID-19 that began in Wuhan, China in late 2019 reached 10 million cases by late June 2020. In order to understand the epidemiological landscape of the COVID-19 pandemic, many studies have attempted to elucidate phylogenetic relationships between collected viral genome sequences using haplotype networks. However, currently available applications for network visualization are not suited to understand the COVID-19 epidemic spatiotemporally, due to functional limitations That motivated us to develop Haplotype Explorer, an intuitive tool for visualizing and exploring haplotype networks. Haplotype Explorer enables people to dissect epidemiological consequences via interactive node filters to provide spatiotemporal perspectives on multimodal spectra of infectious diseases, including introduction, outbreak, expansion, and containment, for given regions and time spans. Here, we demonstrate the effectiveness of Haplotype Explorer by showing an example of its visualization and features. The demo using SARS-CoV-2 genome sequences is available at https://github.com/TKSjp/HaplotypeExplorer

Summary

A lot of software for network visualization are available, but existing software have not been optimized to infection cluster visualization against the current worldwide invasion of COVID-19 started since 2019. To reach the spatiotemporal understanding of its epidemics, we developed Haplotype Explorer. It is superior to other applications in the point of generating HTML distribution files with metadata searches which interactively reflects GISAID IDs, locations, and collection dates. Here, we introduce the features and products of Haplotype Explorer, demonstrating the time-dependent snapshots of haplotype networks inferred from total of 4,282 SARS-CoV-2 genomes.

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    SciScore for 10.1101/2020.07.19.179101: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We confirmed compatibilities of Haplotype Explorer and the bundled python scripts with the latest versions of Safari, Firefox, Edge, Chrome, and Python3 on macOS Catalina 10.15.3, respectively.
    python
    suggested: (IPython, RRID:SCR_001658)
    Python3
    suggested: None
    Passing sequences were aligned by MAFFT Katoh et al. 2002), clustered by CD-HIT (Fu et al. 2012) (threshold: 100% identical), and SNVs extracted by snp-sites Page et al. 2016).
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.07.19.179101 on bioRxiv