Topological data analysis identifies emerging adaptive mutations in SARS-CoV-2
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Abstract
The COVID-19 pandemic has initiated an unprecedented worldwide effort to characterize its evolution through the mapping of mutations of the coronavirus SARS-CoV-2. The early identification of mutations that could confer adaptive advantages to the virus, such as higher infectivity or immune evasion, is of paramount importance. However, the large number of currently available genomes precludes the efficient use of phylogeny-based methods. Here we present CoVtRec, a fast and scalable Topological Data Analysis approach for the surveillance of emerging adaptive mutations in large genomic datasets. Our method overcomes limitations of state-of-the-art phylogeny-based approaches by quantifying the potential adaptiveness of mutations merely by their topological footprint in the genome alignment, without resorting to the reconstruction of a single optimal phylogenetic tree. Analyzing millions of SARS-CoV-2 genomes from GISAID, we find a correlation between topological signals and adaptation to the human host. By leveraging the stratification by time in sequence data, our method enables the high-resolution longitudinal analysis of topological signals of adaptation. We characterize the convergent evolution of the coronavirus throughout the whole pandemic to date, report on emerging potentially adaptive mutations, and pinpoint mutations in Variants of Concern that are likely associated with positive selection. Our approach can improve the surveillance of mutations of concern and guide experimental studies.
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SciScore for 10.1101/2021.06.10.21258550: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Second, a subset of 128,347 sequences that deviated from the reference sequence in genome length, or whose genetic distance to the reference sequence was greater than 20nt, was aligned with MUSCLE [8], iteratively in blocks of 20 sequences each. MUSCLEsuggested: (MUSCLE, RRID:SCR_011812)Ancestral state reconstruction analysis: For the study of the evolutionary histories of topologically highly recurrent mutations, we performed ancestral state reconstruction analyses using Mesquite Version 3.61 [26]. Mesquitesuggested: (Mesquite, RRID:SCR_017994)Results from OddPub: We did not detect open …
SciScore for 10.1101/2021.06.10.21258550: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Second, a subset of 128,347 sequences that deviated from the reference sequence in genome length, or whose genetic distance to the reference sequence was greater than 20nt, was aligned with MUSCLE [8], iteratively in blocks of 20 sequences each. MUSCLEsuggested: (MUSCLE, RRID:SCR_011812)Ancestral state reconstruction analysis: For the study of the evolutionary histories of topologically highly recurrent mutations, we performed ancestral state reconstruction analyses using Mesquite Version 3.61 [26]. Mesquitesuggested: (Mesquite, RRID:SCR_017994)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
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.
Results from scite Reference Check: We found no unreliable references.
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