phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets

  1. Nicola De Maio
  2. William Boulton
  3. Lukas Weilguny
  4. Conor R. Walker
  5. Yatish Turakhia
  6. Russell Corbett-Detig
  7. Nick Goldman

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Abstract

Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, and are an essential component of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here, we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. > 100, 000 tips) when the branches of the tree are short, as is typical in genomic epidemiology. Our algorithm is based on the Gillespie approach, and it implements an efficient multi-layered search tree structure that provides high computational efficiency by taking advantage of the fact that only a small proportion of the genome is likely to mutate at each branch of the considered phylogeny. Our open source software allows easy integration with other Python packages as well as a variety of evolutionary models, including indel models and new hypermutability models that we developed to more realistically represent SARS-CoV-2 genome evolution.

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  1. Version published to 10.1371/journal.pcbi.1010056
  2. Version published to 10.1101/2021.03.15.435416v2 on bioRxiv
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    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

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    Table 2: Resources

    Software and Algorithms
    SentencesResources
    For this reason, we provide the option for the user to generate a FASTA or PHYLIP alignment output, but by default we only generate the more concise version consisting of a list of differences, which usually leads to a very considerable reduction in time and memory demand.
    PHYLIP
    suggested: (PHYLIP, RRID:SCR_006244)

    Results from OddPub: Thank you for sharing your code and data.

    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.

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  4. Version published to 10.1101/2021.03.15.435416v1 on bioRxiv