ORCID Profile
0000-0001-5600-2900
Current Organisation
University of California, San Diego
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Publisher: Cold Spring Harbor Laboratory
Date: 13-01-2022
DOI: 10.1101/2022.01.12.475688
Abstract: Phylogenetic tree optimization is necessary for precise analysis of evolutionary and transmission dynamics, but existing tools are inadequate for handling the scale and pace of data produced during the COVID-19 pandemic. One transformative approach, online phylogenetics, aims to incrementally add s les to an ever-growing phylogeny, but there are no previously-existing approaches that can efficiently optimize this vast phylogeny under the time constraints of the pandemic. Here, we present matOptimize, a fast and memory-efficient phylogenetic tree optimization tool based on parsimony that can be parallelized across multiple CPU threads and nodes, and provides orders of magnitude improvement in runtime and peak memory usage compared to existing state-of-the-art methods. We have developed this method particularly to address the pressing need during the COVID-19 pandemic for daily maintenance and optimization of a comprehensive SARS-CoV-2 phylogeny. Thus, our approach addresses an important need for daily maintenance and refinement of a comprehensive SARS-CoV-2 phylogeny. Phylogenetic trees have been central to genomic surveillance, epidemiology, and contact tracing efforts during the COVD-19 pandemic. With over 6 million SARS-CoV-2 genome sequences now available, maintaining an accurate, comprehensive phylogenetic tree of all available SARS-CoV-2 sequences is becoming computationally infeasible with existing software, but is essential for getting a detailed picture of the virus’ evolution and transmission. Our novel phylogenetic software, matOptimize, is helping refine possibly the largest-ever phylogenetic tree, containing millions of SARS-CoV-2 sequences, thus providing an unprecedented resolution for studying the pathogen’s evolutionary and transmission dynamics.
Publisher: Cold Spring Harbor Laboratory
Date: 07-05-2021
DOI: 10.1101/2021.05.06.442903
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 and spread globally to cause the COVID-19 pandemic. Despite the constant accumulation of genetic variation in the SARS-CoV-2 population, there was little evidence for the emergence of significantly more transmissible lineages in the first half of 2020. Starting around November 2020, several more contagious and possibly more virulent ‘Variants of Concern’ (VoCs) were reported in various regions of the world. These VoCs share some mutations and deletions that haven arisen recurrently in distinct genetic backgrounds. Here, we build on our previous work modelling the association of mutations to SARS-CoV-2 transmissibility and characterise the contribution of in idual recurrent mutations and deletions to estimated viral transmissibility. We then assess how patterns of estimated transmissibility in all SARS-CoV-2 clades have varied over the course of the COVID-19 pandemic by summing transmissibility estimates for all in idual mutations carried by any sequenced genome analysed. Such an approach recovers the Delta variant (21A) as the most transmissible clade currently in circulation, followed by the Alpha variant (20I). By assessing transmissibility over the time of s ling, we observe a tendency for estimated transmissibility within clades to slightly decrease over time in most clades. Although subtle, this pattern is consistent with the expectation of a decay in transmissibility in mainly non-recombining lineages caused by the accumulation of weakly deleterious mutations. SARS-CoV-2 remains a highly transmissible pathogen, though such a trend could conceivably play a role in the turnover of different global viral clades observed over the pandemic so far. This work is not about the severity of disease. We do not analyse the severity of disease. We do not present any evidence that SARS-CoV-2 has decreased in severity. Lineage replacement dynamics are affected by many factors. The trend we recover for a decrease in inferred transmissibility of a clade over time is a small effect. We caution against over-interpretation. This result would not affect the management of the SARS-CoV-2 pandemic: for ex le, we make no claims about any impact on the efficacy of particular non-pharmaceutical interventions (NPIs). Our phylogeny-based method to infer changes in estimated transmissibility due to recurrent mutations and deletions makes a number of simplifying assumptions. These may not all be valid. The consistent trend for the slight decrease we report might be due to an as-yet-unidentified systematic bias.
Publisher: Oxford University Press (OUP)
Date: 08-07-2023
DOI: 10.1093/BIOINFORMATICS/BTAD536
Abstract: Neighbour-Joining is one of the most widely used distance-based phylogenetic inference methods. However, current implementations do not scale well for datasets with more than 10,000 sequences. Given the increasing pace of generating new sequence data, particularly in outbreaks of emerging diseases, and the already enormous existing databases of sequence data for which NJ is a useful approach, new implementations of existing methods are warranted. Here we present DecentTree, which provides highly optimised and parallel implementations of Neighbour-Joining and several of its variants. DecentTree is designed as a stand-alone application and a header-only library easily integrated with other phylogenetic software (e.g., it is integral in the popular IQ-TREE software). We show that DecentTree shows similar or improved performance over existing software (BIONJ, Quicktree, FastME, and RapidNJ), especially for handling very large alignments. For ex le, DecentTree is up to 6-fold faster than the fastest existing Neighbour-Joining software (e.g., RapidNJ) when generating a tree of 64,000 SARS-CoV-2 genomes. DecentTree is open source and freely available at qtree/decenttree. All code and data used in this analysis are available on Github (sdcid/Comparison-of-neighbour-joining-software). Supplementary data are available at Bioinformatics online.
Publisher: Oxford University Press (OUP)
Date: 22-06-2022
DOI: 10.1093/BIOINFORMATICS/BTAC401
Abstract: Phylogenetic tree optimization is necessary for precise analysis of evolutionary and transmission dynamics, but existing tools are inadequate for handling the scale and pace of data produced during the coronavirus disease 2019 (COVID-19) pandemic. One transformative approach, online phylogenetics, aims to incrementally add s les to an ever-growing phylogeny, but there are no previously existing approaches that can efficiently optimize this vast phylogeny under the time constraints of the pandemic. Here, we present matOptimize, a fast and memory-efficient phylogenetic tree optimization tool based on parsimony that can be parallelized across multiple CPU threads and nodes, and provides orders of magnitude improvement in runtime and peak memory usage compared to existing state-of-the-art methods. We have developed this method particularly to address the pressing need during the COVID-19 pandemic for daily maintenance and optimization of a comprehensive SARS-CoV-2 phylogeny. matOptimize is currently helping refine on a daily basis possibly the largest-ever phylogenetic tree, containing millions of SARS-CoV-2 sequences. The matOptimize code is freely available as part of the UShER package (atisht/usher) and can also be installed via bioconda (bioconda.github.io/recipes/usher/README.html). All scripts we used to perform the experiments in this manuscript are available at ceh/matOptimize-experiments. Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 10-05-2021
Publisher: Public Library of Science (PLoS)
Date: 18-11-2020
DOI: 10.1371/JOURNAL.PGEN.1009175
Abstract: The SARS-CoV-2 pandemic has led to unprecedented, nearly real-time genetic tracing due to the rapid community sequencing response. Researchers immediately leveraged these data to infer the evolutionary relationships among viral s les and to study key biological questions, including whether host viral genome editing and recombination are features of SARS-CoV-2 evolution. This global sequencing effort is inherently decentralized and must rely on data collected by many labs using a wide variety of molecular and bioinformatic techniques. There is thus a strong possibility that systematic errors associated with lab—or protocol—specific practices affect some sequences in the repositories. We find that some recurrent mutations in reported SARS-CoV-2 genome sequences have been observed predominantly or exclusively by single labs, co-localize with commonly used primer binding sites and are more likely to affect the protein-coding sequences than other similarly recurrent mutations. We show that their inclusion can affect phylogenetic inference on scales relevant to local lineage tracing, and make it appear as though there has been an excess of recurrent mutation or recombination among viral lineages. We suggest how s les can be screened and problematic variants removed, and we plan to regularly inform the scientific community with our updated results as more SARS-CoV-2 genome sequences are shared ( /issues-with-sars-cov-2-sequencing-data/473 and /masking-strategies-for-sars-cov-2-alignments/480 ). We also develop tools for comparing and visualizing differences among very large phylogenies and we show that consistent clade- and tree-based comparisons can be made between phylogenies produced by different groups. These will facilitate evolutionary inferences and comparisons among phylogenies produced for a wide array of purposes. Building on the SARS-CoV-2 Genome Browser at UCSC, we present a toolkit to compare, analyze and combine SARS-CoV-2 phylogenies, find and remove potential sequencing errors and establish a widely shared, stable clade structure for a more accurate scientific inference and discourse.
Location: United States of America
Location: United States of America
No related grants have been discovered for Yatish Turakhia.