A Daily-Updated Database and Tools for Comprehensive SARS-CoV-2 Mutation-Annotated Trees

McBroome, Jakob; Thornlow, Bryan; Hinrichs, Angie S; Krämer, Alexander; Maio, Nicola De; Goldman, Nick; Haussler, David; Corbett-Detig, Russell; Turakhia, Yatish

doi:10.1093/molbev/msab264

Cited by 89 publications

(135 citation statements)

References 25 publications

Supporting

Mentioning

124

Contrasting

Order By: Relevance

“…To avoid duplicate entries, we removed the Russian sequences present in the UShER tree, and then added the Russian GISAID sequences to the tree using UShER [ 19 ]. Branch lengths were corrected using mutation paths obtained by matUtils [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

The rise and spread of the SARS-CoV-2 AY.122 lineage in Russia

Klink

Safina

Nabieva

et al. 2021

Preprint

View full text Add to dashboard Cite

BackgroundDelta has outcompeted most preexisting variants of SARS-CoV-2, becoming the globally predominant lineage by mid-2021. Its subsequent evolution has led to emergence of multiple sublineages, many of which are well-mixed between countries.AimHere, we aim to study the emergence and spread of the Delta lineage in Russia.MethodsWe use a phylogeographic approach to infer imports of Delta sublineages into Russia, and phylodynamic models to assess the rate of their spread.ResultsWe show that nearly the entire Delta epidemic in Russia has probably descended from a single import event despite genetic evidence of multiple Delta imports. Indeed, over 90% of Delta samples in Russia are characterized by the nsp2:K81N+ORF7a:P45L pair of mutations which is rare outside Russia, putting them in the AY.122 sublineage. The AY.122 lineage was frequent in Russia among Delta samples from the start, and has not increased in frequency in other countries where it has been observed, suggesting that its high prevalence in Russia has probably resulted from a random founder effect.ConclusionThe apartness of the genetic composition of the Delta epidemic in Russia makes Russia somewhat unusual, although not exceptional, among other countries.

show abstract

Section: Methodsmentioning

confidence: 99%

The rise and spread of the SARS-CoV-2 AY.122 lineage in Russia

Klink

Safina

Nabieva

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This format is richer than the others, as it provides information regarding each mutation event, even those that might be over-written by other mutations at the same position; it is also more efficient than multiple sequence alignment formats in the scenario of short branch lengths considered here. We also allow a binary analogue of this annotated Newick tree, called a MAT (mutation annotated tree) [37], which is compatible with the phylogenetic software UShER [27].…”

Section: Output Formatsmentioning

confidence: 99%

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

Maio

Weilguny

Walker

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, as well as being part 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 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 is available from https://github.com/NicolaDM/phastSim and allows easy integration with other Python packages as well as a variety of evolutionary models, including new ones that we developed to more realistically model SARS-CoV-2 genome evolution.

show abstract

“…Therefore, from the early days of the pandemic, the global scientific community mobilized to monitor the viral mutations and the evolutionary dynamics with the help of genome sequencing ( Lo and Jamrozy, 2020 ; Maxmen, 2021 ). The first SARS-CoV-2 genome sequence was deposited on an online database in January 2020 ( Wu et al, 2020 ), and since then, over 4 million additional sequences have been shared through an extraordinary worldwide effort, with tens of thousands more being shared every day ( Maxmen, 2021 ; McBroome et al, 2021 ). This vast volume of genomic data has provided invaluable insights into the evolution and spread of the virus, and has allowed public health officials and governments to respond to it in a timely fashion ( Lam-Hine et al, 2021 ; Oude Munnink et al, 2020 ).…”

Section: Overview Of the Problemmentioning

confidence: 99%

“…We also parallelized our tools for use in a CPU cluster. For parallelizing UShER over CPU nodes, we had to implement a merge operation in matUtils ( McBroome et al, 2021 ). Briefly, this operation accepts two input MATs, checks if the subtrees resulting from common samples are consistent (i.e.…”

Section: Innovations Realizedmentioning

confidence: 99%

“…We collected SARS-CoV-2 genome sequence data from major online databases: GISAID ( Shu and McCauley, 2017 ), COG-UK ( Nicholls et al, 2020 ), GenBank ( Clark et al, 2016 ) and CNCB ( https://bigd.big.ac.cn/ncov/release_genome ), and used it to build a comprehensive SARS-CoV-2 phylogeny based on the methodology described in ( McBroome et al, 2021 ). We used the GenBank MN908947.3 (RefSeq NC_045512.2) sequence as the reference for rooting the tree, as well as for calling variants in individual samples.…”

Section: How Performance Was Measuredmentioning

confidence: 99%

See 1 more Smart Citation

Pandemic-scale phylogenetics

Cheng

Thornlow

Krämer

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Phylogenetics has been central to the genomic surveillance, epidemiology and contact tracing efforts during the COVD-19 pandemic. But the massive scale of genomic sequencing has rendered the pre-pandemic tools inadequate for comprehensive phylogenetic analyses. Here, we discuss the phylogenetic package that we developed to address the needs imposed by this pandemic. The package incorporates several pandemic-specific optimization and parallelization techniques and comprises four programs: UShER, matOptimize, RIPPLES and matUtils. Using high-performance computing, UShER and matOptimize maintain and refine daily a massive mutation-annotated phylogenetic tree consisting of all SARS-CoV-2 sequences available in online repositories. With UShER and RIPPLES, individual labs -- even with modest compute resources -- incorporate newly-sequenced SARS-CoV-2 genomes on this phylogeny and discover evidence for recombination in real-time. With matUtils, they rapidly query and visualize massive SARS-CoV-2 phylogenies. These tools have empowered scientists worldwide to study the SARS-CoV-2 evolution and transmission at an unprecedented scale, resolution and speed.

show abstract

A Daily-Updated Database and Tools for Comprehensive SARS-CoV-2 Mutation-Annotated Trees

Cited by 89 publications

References 25 publications

The rise and spread of the SARS-CoV-2 AY.122 lineage in Russia

The rise and spread of the SARS-CoV-2 AY.122 lineage in Russia

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

Pandemic-scale phylogenetics

Contact Info

Product

Resources

About