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A globally implemented unified phylogenetic classification for human respiratory syncytial virus (HRSV) below the subgroup level remains elusive. We formulated global consensus of HRSV classification on the basis of the challenges and limitations of our previous proposals and the future of genomic surveillance. From a high-quality curated dataset of 1,480 HRSV-A and 1,385 HRSV-B genomes submitted to GenBank and GISAID (https://www.gisaid.org) public sequence databases through March 2023, we categorized HRSV-A/B sequences into lineages based on phylogenetic clades and amino acid markers. We defined 24 lineages within HRSV-A and 16 within HRSV-B and provided guidelines for defining prospective lineages. Our classification demonstrated robustness in its applicability to both complete and partial genomes. We envision that this unified HRSV classification proposal will strengthen HRSV molecular epidemiology on a global scale.

Pathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets., (© 2024. The Author(s).)

Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 data sets do not fit this mold. There are currently over 14 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) and pseudo-ML methods may be more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger data sets. Here, we evaluate the performance of de novo and online phylogenetic approaches, as well as ML, pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimization with UShER and matOptimize produces equivalent SARS-CoV-2 phylogenies to some of the most popular ML and pseudo-ML inference tools. MP optimization with UShER and matOptimize is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo inference. Our results therefore suggest that parsimony-based methods like UShER and matOptimize represent an accurate and more practical alternative to established ML implementations for large SARS-CoV-2 phylogenies and could be successfully applied to other similar data sets with particularly dense sampling and short branch lengths., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society of Systematic Biologists.)

Exposure to different mutagens leaves distinct mutational patterns that can allow inference of pathogen replication niches. We therefore investigated whether SARS-CoV-2 mutational spectra might show lineage-specific differences, dependent on the dominant site(s) of replication and onwards transmission, and could therefore rapidly infer virulence of emergent variants of concern (VOCs). Through mutational spectrum analysis, we found a significant reduction in G>T mutations in the Omicron variant, which replicates in the upper respiratory tract (URT), compared to other lineages, which replicate in both the URT and lower respiratory tract (LRT). Mutational analysis of other viruses and bacteria indicates a robust, generalizable association of high G>T mutations with replication within the LRT. Monitoring G>T mutation rates over time, we found early separation of Omicron from Beta, Gamma and Delta, while mutational patterns in Alpha varied consistent with changes in transmission source as social restrictions were lifted. Mutational spectra may be a powerful tool to infer niches of established and emergent pathogens.

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 1-4 . During the SARS-CoV-2 pandemic, genomic data generation has exceeded the capacities of existing analysis platforms, thereby crippling real-time analysis of viral evolution 5 . Here, we use a new phylogenomic method to search a nearly comprehensive SARS-CoV-2 phylogeny for recombinant lineages. In a 1.6 million sample tree from May 2021, we identify 589 recombination events, which indicate that around 2.7% of sequenced SARS-CoV-2 genomes have detectable recombinant ancestry. Recombination breakpoints are inferred to occur disproportionately in the 3' portion of the genome that contains the spike protein. Our results highlight the need for timely analyses of recombination for pinpointing the emergence of recombinant lineages with the potential to increase transmissibility or virulence of the virus. We anticipate that this approach will empower comprehensive real-time tracking of viral recombination during the SARS-CoV-2 pandemic and beyond., (© 2022. The Author(s).)

The unprecedented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) global sequencing effort has suffered from an analytical bottleneck. Many existing methods for phylogenetic analysis are designed for sparse, static datasets and are too computationally expensive to apply to densely sampled, rapidly expanding datasets when results are needed immediately to inform public health action. For example, public health is often concerned with identifying clusters of closely related samples, but the sheer scale of the data prevents manual inspection and the current computational models are often too expensive in time and resources. Even when results are available, intuitive data exploration tools are of critical importance to effective public health interpretation and action. To help address this need, we present a phylogenetic heuristic that quickly and efficiently identifies newly introduced strains in a region, resulting in clusters of infected individuals, and their putative geographic origins. We show that this approach performs well on simulated data and yields results largely congruent with more sophisticated Bayesian phylogeographic modeling approaches. We also introduce Cluster-Tracker (https://clustertracker.gi.ucsc.edu/), a novel interactive web-based tool to facilitate effective and intuitive SARS-CoV-2 geographic data exploration and visualization across the USA. Cluster-Tracker is updated daily and automatically identifies and highlights groups of closely related SARS-CoV-2 infections resulting from the transmission of the virus between two geographic areas by travelers, streamlining public health tracking of local viral diversity and emerging infection clusters. The site is open-source and designed to be easily configured to analyze any chosen region, making it a useful resource globally. The combination of these open-source tools will empower detailed investigations of the geographic origins and spread of SARS-CoV-2 and other densely sampled pathogens., (© The Author(s) 2022. Published by Oxford University Press.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Chromosomal inversions are among the primary drivers of genome structure evolution in a wide range of natural populations. Although there is an impressive array of theory and empirical analyses that have identified conditions under which inversions can be positively selected, comparatively little data are available on the fitness impacts of these genome structural rearrangements themselves. Because inversion breakpoints can disrupt functional elements and alter chromatin domains, the precise positioning of an inversion's breakpoints can strongly affect its fitness. Here, we compared the fine-scale distribution of low-frequency inversion breakpoints with those of high-frequency inversions and inversions that have gone to fixation between Drosophila species. We identified a number of differences among frequency classes that may influence inversion fitness. In particular, breakpoints that are proximal to insulator elements, generate large tandem duplications, and minimize impacts on gene coding spans which are more prevalent in high-frequency and fixed inversions than in rare inversions. The data suggest that natural selection acts to preserve both genes and larger cis-regulatory networks in the occurrence and spread of rearrangements. These factors may act to limit the availability of high-fitness arrangements when suppressed recombination is favorable., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

A high quality genome assembly is a vital first step for the study of an organism. Recent advances in technology have made the creation of high quality chromosome scale assemblies feasible and low cost. However, the amount of input DNA needed for an assembly project can be a limiting factor for small organisms or precious samples. Here we demonstrate the feasibility of creating a chromosome scale assembly using a hybrid method for a low input sample, a single outbred Drosophila melanogaster. Our approach combines an Illumina shotgun library, Oxford nanopore long reads, and chromosome conformation capture for long range scaffolding. This single fly genome assembly has a N50 of 26 Mb, a length that encompasses entire chromosome arms, contains 95% of expected single copy orthologs, and a nearly complete assembly of this individual's Wolbachia endosymbiont. The methods described here enable the accurate and complete assembly of genomes from small, field collected organisms as well as precious clinical samples., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Chromosomal inversions are fundamental drivers of genome evolution. In the main Afrotropical malaria vector species, belonging to the Anopheles gambiae species complex, inversions play an important role in local adaptation and have a rich history of cytological study. Despite the importance and ubiquity of some chromosomal inversions across the species complex, inversion breakpoints are often challenging to map molecularly due to the presence of large repetitive regions. Here, we develop an approach that uses Hi-C sequencing data to molecularly fine-map the breakpoints of inversions. We demonstrate that this approach is robust and likely to be widely applicable for both identification and fine-mapping inversion breakpoints in species whose inversions have heretofore been challenging to characterize. We apply our method to interrogate the previously unknown inversion breakpoints of 2Rbc and 2Rd in An. coluzzii We found that inversion breakpoints occur in large repetitive regions, and, strikingly, among three inversions analyzed, two breakpoints appear to be reused in two separate inversions. These breakpoint-adjacent regions are strongly enriched for the presence of a 30 bp satellite repeat sequence. Because low frequency inversion breakpoints are not correlated with genomic regions containing this satellite, we suggest that interrupting this particular repeat may result in arrangements with higher relative fitness. Additionally, we use heterozygous individuals to quantitatively investigate the impacts of somatic pairing in the regions immediately surrounding inversion breakpoints. Finally, we discuss important considerations for possible applications of this approach for inversion breakpoint identification in a range of organisms., (Copyright © 2019 by the Genetics Society of America.)

Africanized honey bees (Apis mellifera) arrived in the western hemisphere in the 1950s and quickly spread north reaching California in the 1990s. These bees are highly defensive and somewhat more difficult to manage for commercial purposes than the European honey bees traditionally kept. The arrival of these bees and their potentially replacing European bees over much of the state is thus of great concern. After a 25 year period of little systematic sampling, a recent small scale study found Africanized honey bees in the Bay Area of California, far north of their last recorded distribution. The purpose of the present study was to expand this study by conducting more intensive sampling of bees from across northern California. We found Africanized honey bees as far north as Napa and Sacramento. We also found Africanized bees in all counties south of these counties. Africanized honey bees were particularly abundant in parts of the central valley and Monterey. This work suggests the northern spread of Africanized honey bees may not have stopped. They may still be moving north at a slow rate, although due to the long gaps in sampling it is currently impossible to tell for certain. Future work should routinely monitor the distribution of these bees to distinguish between these two possibilities.