ArticleJournal of computational biology : a journal of computational molecular cell biology2021
Scalable Reconstruction of SARS-CoV-2 Phylogeny with Recurrent Mutations.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Unlocking capacities of genomics for the COVID-19 response and future pandemics.Nature methods · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This article presents a novel scalable character-based phylogeny algorithm for dense viral sequencing data called SPHERE (Scalable PHylogEny with REcurrent mutations). The algorithm is based on an evolutionary model where recurrent mutations are allowed, but backward mutations are prohibited. The algorithm creates rooted character-based phylogeny trees, wherein all leaves and internal nodes are labeled by observed taxa. We show that SPHERE phylogeny is more stable than Nextstrain's, and that it accurately infers known transmission links from the early pandemic. SPHERE is a fast algorithm that can process >200,000 sequences in <2 hours, which offers a compact phylogenetic visualization of Global Initiative on Sharing All Influenza Data (GISAID).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.