Biology subjects

Turakhia, Y.

Publications and source records attributed to Turakhia, Y..

29 records · Page 2Linked to original sources

bioRxiv2021

Pandemic-Scale Phylogenomics Reveals Elevated Recombination Rates in the SARS-CoV-2 Spike Region

Accurate and timely detection of recombinant lineages is crucial for interpreting genetic variation, reconstructing epidemic spread, identifying selection and variants of interest, and accurately performing phylogenetic analyses. During the SARS-CoV-2 pandemic, genomic data generation has exceeded the capacities of existing analysis platforms, thereby crippling real-time analysis of viral recombination. Low SARS-CoV-2 mutation rates make detecting recombination difficult. Here, we develop and apply a novel phylogenomic method to exhaustively search a nearly comprehensive SARS-CoV-2 phylogeny for recombinant lineages. We investigate a 1.6M sample tree, and identify 606 recombination events. Approximately 2.7% of sequenced SARS-CoV-2 genomes have recombinant ancestry. Recombination breakpoints occur disproportionately in the Spike protein region. Our method empowers comprehensive real time tracking of viral recombination during the SARS-CoV-2 pandemic and beyond.

evolutionary biology↗

bioRxiv2021

A new SARS-CoV-2 lineage that shares mutations with known Variants of Concern is rejected by automated sequence repository quality control

We report a SARS-CoV-2 lineage that shares N501Y, P681H, and other mutations with known variants of concern, such as B.1.1.7. This lineage, which we refer to as B.1.x (COG-UK sometimes references similar samples as B.1.324.1), is present in at least 20 states across the USA and in at least six countries. However, a large deletion causes the sequence to be automatically rejected from repositories, suggesting that the frequency of this new lineage is underestimated using public data. Recent dynamics based on 339 samples obtained in Santa Cruz County, CA, USA suggest that B.1.x may be increasing in frequency at a rate similar to that of B.1.1.7 in Southern California. At present the functional differences between this variant B.1.x and other circulating SARS-CoV-2 variants are unknown, and further studies on secondary attack rates, viral loads, immune evasion and/or disease severity are needed to determine if it poses a public health concern. Nonetheless, given what is known from well-studied circulating variants of concern, it seems unlikely that the lineage could pose larger concerns for human health than many already globally distributed lineages. Our work highlights a need for rapid turnaround time from sequence generation to submission and improved sequence quality control that removes submission bias. We identify promising paths toward this goal.

genomics↗

bioRxiv2021

matUtils: Tools to Interpret and Manipulate Mutation Annotated Trees

The vast scale of SARS-CoV-2 sequencing data has made it increasingly challenging to comprehensively analyze all available data using existing tools and file formats. To address this, we present a database of SARS-CoV-2 phylogenetic trees inferred with unrestricted public sequences, which we update daily to incorporate new sequences. Our database uses the recently-proposed mutation-annotated tree (MAT) format to efficiently encode the tree with branches labeled with parsimony-inferred mutations as well as Nextstrain clade and Pango lineage labels at clade roots. As of June 9, 2021, our SARS-CoV-2 MAT consists of 834,521 sequences and provides a comprehensive view of the virus evolutionary history using public data. We also present matUtils - a command-line utility for rapidly querying, interpreting and manipulating the MATs. Our daily-updated SARS-CoV-2 MAT database and matUtils software are available at http://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/ and https://github.com/yatisht/usher, respectively.

bioinformatics↗

bioRxiv2021

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

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 indel models and new hypermutatability models that we developed to more realistically represent SARS-CoV-2 genome evolution. Author summaryOne of the most influential responses to the SARS-CoV-2 pandemic has been the widespread adoption of genome sequencing to keep track of viral spread and evolution. This has resulted in vast availability of genomic sequence data, that, while extremely useful and promising, is also increasingly hard to store and process efficiently. An important task in the processing of this genetic data is simulation, that is, recreating potential histories of past and future virus evolution, to benchmark data analysis methods and make statistical inference. Here, we address the problem of efficiently simulating large numbers of closely related genomes, similar to those sequenced during SARS-CoV-2 pandemic, or indeed to most scenarios in genomic epidemiology. We develop a new algorithm to perform this task, that provides not only computational efficiency, but also extreme flexibility in terms of possible evolutionary models, allowing variation in mutation rates, non-stationary evolution, and indels; all phenomena that play an important role in SARS-CoV-2 evolution, as well as many other real-life epidemiological scenarios.

bioinformatics↗

bioRxiv2021

Mutation rates and selection on synonymous mutations in SARS-CoV-2

AO_SCPLOWBSTRACTC_SCPLOWThe COVID-19 pandemic has seen an unprecedented response from the sequencing community. Leveraging the sequence data from more than 140,000 SARS-CoV-2 genomes, we study mutation rates and selective pressures affecting the virus. Understanding the processes and effects of mutation and selection has profound implications for the study of viral evolution, for vaccine design, and for the tracking of viral spread. We highlight and address some common genome sequence analysis pitfalls that can lead to inaccurate inference of mutation rates and selection, such as ignoring skews in the genetic code, not accounting for recurrent mutations, and assuming evolutionary equilibrium. We find that two particular mutation rates, G[->]U and C[->]U, are similarly elevated and considerably higher than all other mutation rates, causing the majority of mutations in the SARS-CoV-2 genome, and are possibly the result of APOBEC and ROS activity. These mutations also tend to occur many times at the same genome positions along the global SARS-CoV-2 phylogeny (i.e., they are very homoplasic). We observe an effect of genomic context on mutation rates, but the effect of the context is overall limited. While previous studies have suggested selection acting to decrease U content at synonymous sites, we bring forward evidence suggesting the opposite.

evolutionary biology↗

bioRxiv2020

Ultrafast Sample Placement on Existing Trees (UShER) Empowers Real-Time Phylogenetics for the SARS-CoV-2 Pandemic

As the SARS-CoV-2 virus spreads through human populations, the unprecedented accumulation of viral genome sequences is ushering a new era of "genomic contact tracing" - that is, using viral genome sequences to trace local transmission dynamics. However, because the viral phylogeny is already so large - and will undoubtedly grow many fold - placing new sequences onto the tree has emerged as a barrier to real-time genomic contact tracing. Here, we resolve this challenge by building an efficient, tree-based data structure encoding the inferred evolutionary history of the virus. We demonstrate that our approach improves the speed of phylogenetic placement of new samples and data visualization by orders of magnitude, making it possible to complete the placements under real-time constraints. Our method also provides the key ingredient for maintaining a fully-updated reference phylogeny. We make these tools available to the research community through the UCSC SARS-CoV-2 Genome Browser to enable rapid cross-referencing of information in new virus sequences with an ever-expanding array of molecular and structural biology data. The methods described here will empower research and genomic contact tracing for laboratories worldwide. Software AvailabilityUSHER is available to users through the UCSC Genome Browser at https://genome.ucsc.edu/cgi-bin/hgPhyloPlace. The source code and detailed instructions on how to compile and run UShER are available from https://github.com/yatisht/usher.

genomics↗

bioRxiv2020

Stability of SARS-CoV-2 Phylogenies

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 samples 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-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 and/or recombination among viral lineages. We suggest how samples can be screened and problematic mutations removed. We also develop tools for comparing and visualizing differences among 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. ForewordWe wish to thank all groups that responded rapidly by producing these invaluable and essential sequence data. Their contributions have enabled an unprecedented, lightning-fast process of scientific discovery---truly an incredible benefit for humanity and for the scientific community. We emphasize that most lab groups with whom we associate specific suspicious alleles are also those who have produced the most sequence data at a time when it was urgently needed. We commend their efforts. We have already contacted each group and many have updated their sequences. Our goal with this work is not to highlight potential errors, but to understand the impacts of these and other kinds of highly recurrent mutations so as to identify commonalities among the suspicious examples that can improve sequence quality and analysis going forward.

genomics↗

bioRxiv2019

Loss of critical developmental and human disease-causing genes in 58 mammals

Gene losses provide an insightful route for studying the morphological and physiological adaptations of species, but their discovery is challenging. Existing genome annotation tools and protein databases focus on annotating intact genes and do not attempt to distinguish nonfunctional genes from genes missing annotation due to sequencing and assembly artifacts. Previous attempts to annotate gene losses have required significant manual curation, which hampers their scalability for the ever-increasing deluge of newly sequenced genomes. Using extreme sequence erosion (deletion and non-synonymous substitution) as an unambiguous signature of loss, we developed an automated approach for detecting high-confidence protein-coding gene loss events across a species tree. Our approach relies solely on gene annotation in a single reference genome, raw assemblies for the remaining species to analyze, and the associated phylogenetic tree for all organisms involved. Using the hg38 human assembly as a reference, we discovered over 500 unique human genes affected by such high-confidence erosion events in different clades across 58 mammals. While most of these events likely have benign consequences, we also found dozens of clade-specific gene losses that result in early lethality in outgroup mammals or are associated with severe congenital diseases in humans. Our discoveries yield intriguing potential for translational medical genetics and for evolutionary biology, and our approach is readily applicable to large-scale genome sequencing efforts across the tree of life.

genomics↗

bioRxiv2019

Champagne: Whole-genome phylogenomic character matrix method places Myomorpha basal in Rodentia

We present Champagne, a whole-genome method for generating character matrices for phylogenomic analysis using large genomic events that, by rigorously picking orthologous genes and locating large, virtually homoplasy-free insertion and deletion events, delivers a character matrix that outperforms existing morphological and nucleotide-based matrices on both established phylogenies, and difficult-to-resolve nodes in the mammalian tree. Champagne harbors distinct theoretical advantages, and can easily be run on any clade of related species, of the many currently being sequenced. Champagne considerably improves the retention index in the parsimony analysis of a number of widely established topologies, observes incomplete lineage sorting (ILS) at the root of Paenungulata, finds little evidence for human-chimp-gorilla ILS, and most surprisingly, offers convincing evidence for a reconsideration of squirrels position in the rodent tree.

evolutionary biology↗

bioRxiv2017

A novel unbiased test for molecular convergent evolution and discoveries in echolocating, aquatic and high-altitude mammals

Distantly related species entering similar biological niches often adapt by evolving similar morphological and physiological characters. The extent to which genomic molecular convergence, and the extent to which coding mutations underlie this convergent phenotypic evolution remain unknown. Using a novel test, we ask which group of functionally coherent genes is most affected by convergent amino acid substitutions between phenotypically convergent lineages. This most affected sets reveals 75 novel coding convergences in important genes that pattern a highly adapted organ: the cochlea, skin and lung in echolocating, aquatic and high-altitude mammals, respectively. Our test explicitly requires the enriched converged term to not be simultaneously enriched for divergent mutations, and correctly dismisses relaxation-based signals, such as those produced by vision genes in subterranean mammals. This novel test can be readily applied to birds, fish, flies, worms etc., to discover more of the fascinating contribution of protein coding convergence to phenotype convergence.

evolutionary biology↗

bioRxiv2017

Darwin: A Hardware-acceleration Framework for Genomic Sequence Alignment

Genomics is set to transform medicine and our understanding of life in fundamental ways. But the growth in genomics data has been overwhelming - far outpacing Moores Law. The advent of third generation sequencing technologies is providing new insights into genomic contribution to diseases with complex mutation events, but have prohibitively high computational costs. Over 1,300 CPU hours are required to align reads from a 54x coverage of the human genome to a reference (estimated using [1]), and over 15,600 CPU hours to assemble the reads de novo [2]. This paper proposes \"Darwin\" - a hardware-accelerated framework for genomic sequence alignment that, without sacrificing sensitivity, provides 125x and 15.6x speedup over the state-of-the-art software counterparts for reference-guided and de novo assembly of third generation sequencing reads, respectively. For pairwise alignment of sequences, Darwin is over 39,000x more energy-efficient than software. Darwin uses (i) a novel filtration strategy, called D-SOFT, to reduce the search space for sequence alignment at high speed, and (ii) a hardware-accelerated version of GACT, a novel algorithm to generate near-optimal alignments of arbitrarily long genomic sequences using constant memory for trace-back. Darwin is adaptable, with tunable speed and sensitivity to match emerging sequencing technologies and to meet the requirements of genomic applications beyond read assembly.

genomics↗