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There are thousands of well-maintained high-quality open-source software utilities for all aspects of scientific data analysis. For more than a decade, the Galaxy Project has been providing computational infrastructure and a unified user interface for these tools to make them accessible to a wide range of researchers. To streamline the process of integrating tools and constructing workflows as much as possible, we have developed Planemo, a software development kit for tool and workflow developers and Galaxy power users. Here we outline Planemo's implementation and describe its broad range of functionality for designing, testing, and executing Galaxy tools, workflows, and training material. In addition, we discuss the philosophy underlying Galaxy tool and workflow development, and how Planemo encourages the use of development best practices, such as test-driven development, by its users, including those who are not professional software developers.

Among the 30 nonsynonymous nucleotide substitutions in the Omicron S-gene are 13 that have only rarely been seen in other SARS-CoV-2 sequences. These mutations cluster within three functionally important regions of the S-gene at sites that will likely impact (1) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (2) interactions of Spike with ACE2 receptors, and (3) the priming of Spike for membrane fusion. We show here that, based on both the rarity of these 13 mutations in intrapatient sequencing reads and patterns of selection at the codon sites where the mutations occur in SARS-CoV-2 and related sarbecoviruses, prior to the emergence of Omicron the mutations would have been predicted to decrease the fitness of any virus within which they occurred. We further propose that the mutations in each of the three clusters therefore cooperatively interact to both mitigate their individual fitness costs, and, in combination with other mutations, adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron overall previously known SARS-CoV-2 lineages, it is crucial to determine both how such complex and highly adaptive mutation constellations were assembled within the Omicron S-gene, and why, despite unprecedented global genomic surveillance efforts, the early stages of this assembly process went completely undetected. ispartof: MOLECULAR BIOLOGY AND EVOLUTION vol:39 issue:4 ispartof: location:United States status: published

We describe a new method, siBar, which enables simultaneous tracking of plasmids and chromosomal lineages within a bacterial host. siBar involves integration of a linearized plasmid construct carrying a unique combination of two molecular barcodes. Upon recircularization one barcode remains integrated into the host chromosome, while the other remains on the plasmid allowing direct observation of expansion and contraction of adaptive lineages. We also performed a pilot evolution experiment that allowed us to assess the barcode complexity and establish an analytical framework for the analysis of siBar data.

There are thousands of well-maintained high-quality open-source software utilities for all aspects of scientific data analysis. For over a decade, the Galaxy Project has been providing computational infrastructure and a unified user interface for these tools to make them accessible to a wide range of researchers. In order to streamline the process of integrating tools and constructing workflows as much as possible, we have developed Planemo, a software development kit for tool and workflow developers and Galaxy power users. Here we outline Planemo’s implementation and describe its broad range of functionality for designing, testing and executing Galaxy tools, workflows and training material. In addition, we discuss the philosophy underlying Galaxy tool and workflow development, and how Planemo encourages the use of development best practices, such as test-driven development, by its users, including those who are not professional software developers. Planemo is a mature project widely used within the Galaxy community which has been downloaded over 80,000 times.

Recombination is an evolutionary process by which many pathogens generate diversity and acquire novel functions. Although a common occurrence during coronavirus replication, recombination can only be detected when two genetically distinct viruses contemporaneously infect the same host. Here, we identify an instance of SARS-CoV-2 superinfection, whereby an individual was simultaneously infected with two distinct viral variants: Alpha (B.1.1.7) and Epsilon (B.1.429). This superinfection was first noted when an Alpha genome sequence failed to exhibit the classic S gene target failure behavior used to track this variant. Full genome sequencing from four independent extracts revealed that Alpha variant alleles comprised between 70-80% of the genomes, whereas the Epsilon variant alleles comprised between 20-30% of the sample. Further investigation revealed the presence of numerous recombinant haplotypes spanning the genome, specifically in the spike, nucleocapsid, and ORF 8 coding regions. These findings support the potential for recombination to reshape SARS-CoV-2 genetic diversity.

The programmed frameshift element (PFE) rerouting translation from ORF1a to ORF1b is essential for the propagation of coronaviruses. The combination of genomic features that make up PFE—the overlap between the two reading frames, a slippery sequence, as well as an ensemble of complex secondary structure elements—places severe constraints on this region as most possible nucleotide substitution may disrupt one or more of these elements. The vast amount of SARS-CoV-2 sequencing data generated within the past year provides an opportunity to assess the evolutionary dynamics of PFE in great detail. Here, we performed a comparative analysis of all available coronaviral genomic data available to date. We show that the overlap between ORF1a and ORF1b evolved as a set of discrete 7, 16, 22, 25, and 31 nucleotide stretches with a well-defined phylogenetic specificity. We further examined sequencing data from over 1,500,000 complete genomes and 55,000 raw read data sets to demonstrate exceptional conservation and detect signatures of selection within the PFE region.

Significance Mitochondria frequently carry different DNA—a state called heteroplasmy. Heteroplasmic mutations can cause mitochondrial diseases and are involved in cancer and aging, but they are also common in healthy people. Here, we study heteroplasmy in 96 multigenerational healthy families. We show that mothers effectively transmit very few mitochondrial DNA to their offspring. Because of this bottleneck, which intensifies with increasing maternal age at childbirth, mutation frequencies can change dramatically between a mother and her child. Thus, a child might inherit a disease-causing mutation at high frequency from an asymptomatic carrier mother and might develop a disease. We also demonstrate that natural selection acts against disease-causing mutations during germline development. Our study has important implications for genetic counseling of mitochondrial diseases., Heteroplasmy—the presence of multiple mitochondrial DNA (mtDNA) haplotypes in an individual—can lead to numerous mitochondrial diseases. The presentation of such diseases depends on the frequency of the heteroplasmic variant in tissues, which, in turn, depends on the dynamics of mtDNA transmissions during germline and somatic development. Thus, understanding and predicting these dynamics between generations and within individuals is medically relevant. Here, we study patterns of heteroplasmy in 2 tissues from each of 345 humans in 96 multigenerational families, each with, at least, 2 siblings (a total of 249 mother–child transmissions). This experimental design has allowed us to estimate the timing of mtDNA mutations, drift, and selection with unprecedented precision. Our results are remarkably concordant between 2 complementary population-genetic approaches. We find evidence for a severe germline bottleneck (7–10 mtDNA segregating units) that occurs independently in different oocyte lineages from the same mother, while somatic bottlenecks are less severe. We demonstrate that divergence between mother and offspring increases with the mother’s age at childbirth, likely due to continued drift of heteroplasmy frequencies in oocytes under meiotic arrest. We show that this period is also accompanied by mutation accumulation leading to more de novo mutations in children born to older mothers. We show that heteroplasmic variants at intermediate frequencies can segregate for many generations in the human population, despite the strong germline bottleneck. We show that selection acts during germline development to keep the frequency of putatively deleterious variants from rising. Our findings have important applications for clinical genetics and genetic counseling.

Coadaptation between bacterial hosts and plasmids frequently results in adaptive changes restricted exclusively to host genome leaving plasmids unchanged. To better understand this remarkable stability, we transformed naïve Escherichia coli cells with a plasmid carrying an antibiotic-resistance gene and forced them to adapt in a turbidostat environment. We then drew population samples at regular intervals and subjected them to duplex sequencing—a technique specifically designed for identification of low-frequency mutations. Variants at ten sites implicated in plasmid copy number control emerged almost immediately, tracked consistently across the experiment’s time points, and faded below detectable frequencies toward the end. This variation crash coincided with the emergence of mutations on the host chromosome. Mathematical modeling of trajectories for adaptive changes affecting plasmid copy number showed that such mutations cannot readily fix or even reach appreciable frequencies. We conclude that there is a strong selection against alterations of copy number even if it can provide a degree of growth advantage. This incentive is likely rooted in the complex interplay between mutated and wild-type plasmids constrained within a single cell and underscores the importance of understanding of intracellular plasmid variability.

HYpothesis testing using PHYlogenies (HyPhy) is a scriptable, open-source package for fitting a broad range of evolutionary models to multiple sequence alignments, and for conducting subsequent parameter estimation and hypothesis testing, primarily in the maximum likelihood statistical framework. It has become a popular choice for characterizing various aspects of the evolutionary process: natural selection, evolutionary rates, recombination, and coevolution. The 2.5 release (available from www.hyphy.org) includes a completely re-engineered computational core and analysis library that introduces new classes of evolutionary models and statistical tests, delivers substantial performance and stability enhancements, improves usability, streamlines end-to-end analysis workflows, makes it easier to develop custom analyses, and is mostly backward compatible with previous HyPhy releases.

Galaxy is an open-source web-based framework that is widely used for performing computational analyses in diverse application domains, such as genome assembly, computational chemistry, ecology, and epigenetics, to name a few. The current Galaxy software framework runs on several high-performance computing platforms such as on-premise clusters, public data centers, and national lab supercomputers. These infrastructures also provide support for state-of-the-art accelerators like Graphical Processing Units (GPUs). When coupled with accelerator support, the tools executing in Galaxy can benefit from massive performance gains in terms of computation time, thereby allowing a more robust computational analysis environment for researchers. Despite tools having GPU capabilities, the current Galaxy framework does not support GPUs, and thus prevents tools from taking advantage of the performance benefits offered by GPUs. We present and experimentally evaluate GYAN, a GPU-aware computation mapping and orchestration functionality implemented in Galaxy that allows the Galaxy tools to be executed on a GPU-enabled cluster. GYAN has the capability of identifying GPU-supported tools and scheduling them on single or multiple GPU nodes based on the availability in the cluster. GYAN supports both native and containerized tool execution. We performed extensive evaluations of the implementation using popular bio-engineering tools to demonstrate the benefits of using GPU technologies. For example, the Racon consensus tool executes ~2× faster than the regular baseline CPU-only jobs, while the Bonito base calling tool shows ~50× speedup.

The COVID-19 pandemic is shifting the teaching paradigms to an online setting all over the world. The Galaxy framework caters to computational biologists a set of features to facilitate the online learning process and make it accessible to everyone. Besides the high-quality training materials, Galaxy provides easy access to data and the possibility to share the progress and achievements, both student to student and student to instructor. By combining the different features offered by the Galaxy framework and by choosing the adequate communication channels, effective training activities can be designed inclusively, regardless of the students' environments.

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

The COVID-19 pandemic is the first global health crisis to occur in the age of big genomic data.Although data generation capacity is well established and sufficiently standardized, analytical capacity is not. To establish analytical capacity it is necessary to pull together global computational resources and deliver the best open source tools and analysis workflows within a ready to use, universally accessible resource. Such a resource should not be controlled by a single research group, institution, or country. Instead it should be maintained by a community of users and developers who ensure that the system remains operational and populated with current tools. A community is also essential for facilitating the types of discourse needed to establish best analytical practices. Bringing together public computational research infrastructure from the USA, Europe, and Australia, we developed a distributed data analysis platform that accomplishes these goals. It is immediately accessible to anyone in the world and is designed for the analysis of rapidly growing collections of deep sequencing datasets. We demonstrate its utility by detecting allelic variants in high-quality existing SARS-CoV-2 sequencing datasets and by continuous reanalysis of COG-UK data. All workflows, data, and documentation is available at https://covid19.galaxyproject.org.

Protein-protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein-protein interactions and produce high-quality multimeric structural models.Application of our method to the Human and Yeast genomes yield protein-protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2’s non-structural protein 3 (Nsp3). We also produced models of SARS-CoV2’s spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor (MOG) and dipeptidyl peptidase-4 (DPP4). The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation.The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.

Modern biology continues to become increasingly computational. Datasets are becoming progressively larger, more complex, and more abundant. The computational savviness necessary to analyze these data creates an ongoing obstacle for experimental biologists. Galaxy (galaxyproject.org) provides access to computational biology tools in a web-based interface. It also provides access to major public biological data repositories, allowing private data to be combined with public datasets. Galaxy is hosted on high-capacity servers worldwide and is accessible for free, with an option to be installed locally. This article demonstrates how to employ Galaxy to perform biologically relevant analyses on publicly available datasets. These protocols use both standard and custom tools, serving as a tutorial and jumping-off point for more intensive and/or more specific analyses using Galaxy. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Finding human coding exons with highest SNP density Basic Protocol 2: Calling peaks for ChIP-seq data Basic Protocol 3: Compare datasets using genomic coordinates Basic Protocol 4: Working with multiple alignments Basic Protocol 5: Single cell RNA-seq.

Duplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences, and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics tool-set that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain PCR and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which result in unpaired families that do not form DCS. Finally, we also developed a tool called Variant Analyzer that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tier-based system. We demonstrate that this tool identified false positive variants tagged by the tier-based classification. Furthermore, with this tool we can include reads without a family and check the reliability of the call, which increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.

Sequencing technology has achieved great advances in the past decade. Studies have previously shown the quality of specific instruments in controlled conditions. Here, we developed a method able to retroactively determine the error rate of most public sequencing datasets. To do this, we utilized the overlaps between reads that are a feature of many sequencing libraries. With this method, we surveyed 1943 different datasets from seven different sequencing instruments produced by Illumina. We show that among public datasets, the more expensive platforms like HiSeq and NovaSeq have a lower error rate and less variation. But we also discovered that there is great variation within each platform, with the accuracy of a sequencing experiment depending greatly on the experimenter. We show the importance of sequence context, especially the phenomenon where preceding bases bias the following bases toward the same identity. We also show the difference in patterns of sequence bias between instruments. Contrary to expectations based on the underlying chemistry, HiSeq X Ten and NovaSeq 6000 share notable exceptions to the preceding-base bias. Our results demonstrate the importance of the specific circumstances of every sequencing experiment, and the importance of evaluating the quality of each one.