Scalable, accessible, and reproducible reference genome assembly and evaluation in Galaxy.

Improvements in genome sequencing and assembly are enabling high-quality reference genomes for all species. However, the assembly process is still laborious, computationally and technically demanding, lacks standards for reproducibility, and is not readily scalable. Here we present the latest Vertebrate Genomes Project assembly pipeline and demonstrate that it delivers high-quality reference genomes at scale across a set of vertebrate species arising over the last ~500 million years. The pipeline is versatile and combines PacBio HiFi long-reads and Hi-C-based haplotype phasing in a new graph-based paradigm. Standardized quality control is performed automatically to troubleshoot assembly issues and assess biological complexities. We make the pipeline freely accessible through Galaxy, accommodating researchers even without local computational resources and enhanced reproducibility by democratizing the training and assembly process. We demonstrate the flexibility and reliability of the pipeline by assembling reference genomes for 51 vertebrate species from major taxonomic groups (fish, amphibians, reptiles, birds, and mammals).

Fast and accurate genome-wide predictions and structural modeling of protein-protein interactions using Galaxy.

BACKGROUND: 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. RESULTS: 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. We also produced models of SARS-CoV2's spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4. CONCLUSIONS: 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.

The Planemo toolkit for developing, deploying, and executing scientific data analyses in Galaxy and beyond.

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.

RASCL: Rapid Assessment of Selection in CLades through molecular sequence analysis.

An important unmet need revealed by the COVID-19 pandemic is the near-real-time identification of potentially fitness-altering mutations within rapidly growing SARS-CoV-2 lineages. Although powerful molecular sequence analysis methods are available to detect and characterize patterns of natural selection within modestly sized gene-sequence datasets, the computational complexity of these methods and their sensitivity to sequencing errors render them effectively inapplicable in large-scale genomic surveillance contexts. Motivated by the need to analyze new lineage evolution in near-real time using large numbers of genomes, we developed the Rapid Assessment of Selection within CLades (RASCL) pipeline. RASCL applies state of the art phylogenetic comparative methods to evaluate selective processes acting at individual codon sites and across whole genes. RASCL is scalable and produces automatically updated regular lineage-specific selection analysis reports: even for lineages that include tens or hundreds of thousands of sampled genome sequences. Key to this performance is (i) generation of automatically subsampled high quality datasets of gene/ORF sequences drawn from a selected "query" viral lineage; (ii) contextualization of these query sequences in codon alignments that include high-quality "background" sequences representative of global SARS-CoV-2 diversity; and (iii) the extensive parallelization of a suite of computationally intensive selection analysis tests. Within hours of being deployed to analyze a novel rapidly growing lineage of interest, RASCL will begin yielding JavaScript Object Notation (JSON)-formatted reports that can be either imported into third-party analysis software or explored in standard web-browsers using the premade RASCL interactive data visualization dashboard. By enabling the rapid detection of genome sites evolving under different selective regimes, RASCL is well-suited for near-real-time monitoring of the population-level selective processes that will likely underlie the emergence of future variants of concern in measurably evolving pathogens with extensive genomic surveillance.

Detection of SARS-CoV-2 intra-host recombination during superinfection with Alpha and Epsilon variants in New York City.

Recombination is an evolutionary process by which many pathogens generate diversity and acquire novel functions. Although a common occurrence during coronavirus replication, detection of recombination is only feasible when genetically distinct viruses contemporaneously infect the same host. Here, we identify an instance of SARS-CoV-2 superinfection, whereby an individual was 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 reveals that Alpha variant alleles comprise around 75% of the genomes, whereas the Epsilon variant alleles comprise around 20% of the sample. Further investigation reveals 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.

Selection Analysis Identifies Clusters of Unusual Mutational Changes in Omicron Lineage BA.1 That Likely Impact Spike Function.

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.

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space.

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types.

Selection analysis identifies unusual clustered mutational changes in Omicron lineage BA.1 that likely impact Spike function.

Among the 30 non-synonymous 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 (i) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (ii) interactions of Spike with ACE2 receptors, and (iii) 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 genomes 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 adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron over all 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.

RASCL: Rapid Assessment Of SARS-CoV-2 Clades Through Molecular Sequence Analysis.

An important component of efforts to manage the ongoing COVID19 pandemic is the R apid A ssessment of how natural selection contributes to the emergence and proliferation of potentially dangerous S ARS-CoV-2 lineages and CL ades (RASCL). The RASCL pipeline enables continuous comparative phylogenetics-based selection analyses of rapidly growing clade-focused genome surveillance datasets, such as those produced following the initial detection of potentially dangerous variants. From such datasets RASCL automatically generates down-sampled codon alignments of individual genes/ORFs containing contextualizing background reference sequences, analyzes these with a battery of selection tests, and outputs results as both machine readable JSON files, and interactive notebook-based visualizations. AVAILABILITY: RASCL is available from a dedicated repository at https://github.com/veg/RASCL and as a Galaxy workflow https://usegalaxy.eu/u/hyphy/w/rascl . Existing clade/variant analysis results are available here: https://observablehq.com/@aglucaci/rascl . CONTACT: Dr. Sergei L Kosakovsky Pond ( spond@temple.edu ). SUPPLEMENTARY INFORMATION: N/A.

Stepwise Evolution and Exceptional Conservation of ORF1a/b Overlap in Coronaviruses.

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.

Reproducible and accessible analysis of transposon insertion sequencing in Galaxy for qualitative essentiality analyses.

BACKGROUND: Significant progress has been made in advancing and standardizing tools for human genomic and biomedical research. Yet, the field of next-generation sequencing (NGS) analysis for microorganisms (including multiple pathogens) remains fragmented, lacks accessible and reusable tools, is hindered by local computational resource limitations, and does not offer widely accepted standards. One such "problem areas" is the analysis of Transposon Insertion Sequencing (TIS) data. TIS allows probing of almost the entire genome of a microorganism by introducing random insertions of transposon-derived constructs. The impact of the insertions on the survival and growth under specific conditions provides precise information about genes affecting specific phenotypic characteristics. A wide array of tools has been developed to analyze TIS data. Among the variety of options available, it is often difficult to identify which one can provide a reliable and reproducible analysis. RESULTS: Here we sought to understand the challenges and propose reliable practices for the analysis of TIS experiments. Using data from two recent TIS studies, we have developed a series of workflows that include multiple tools for data de-multiplexing, promoter sequence identification, transposon flank alignment, and read count repartition across the genome. Particular attention was paid to quality control procedures, such as determining the optimal tool parameters for the analysis and removal of contamination. CONCLUSIONS: Our work provides an assessment of the currently available tools for TIS data analysis. It offers ready to use workflows that can be invoked by anyone in the world using our public Galaxy platform ( https://usegalaxy.org ). To lower the entry barriers, we have also developed interactive tutorials explaining details of TIS data analysis procedures at https://bit.ly/gxy-tis .

Stepwise evolution and exceptional conservation of ORF1a/b overlap in coronaviruses.

The programmed frameshift element (PFE) rerouting translation from ORF1a to ORF1b is essential for propagation of coronaviruses. A 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-puts 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 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 b 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 350,000 complete genomes and 55,000 raw read datasets to demonstrate exceptional conservation of the PFE region.

Fostering accessible online education using Galaxy as an e-learning platform.

The COVID-19 pandemic is shifting teaching to an online setting all over the world. The Galaxy framework facilitates the online learning process and makes it accessible by providing a library of high-quality community-curated training materials, enabling easy access to data and tools, and facilitates sharing achievements and progress between students and instructors. By combining Galaxy with robust communication channels, effective instruction can be designed inclusively, regardless of the students' environments.

Freely accessible ready to use global infrastructure for SARS-CoV-2 monitoring.

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 .

Sequencing error profiles of Illumina sequencing instruments.

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.

Erratum: Increased yields of duplex sequencing data by a series of quality control tools.

[This corrects the article DOI: 10.1093/nargab/lqab002.].

Using Galaxy to Perform Large-Scale Interactive Data Analyses-An Update.

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.

Increased yields of duplex sequencing data by a series of quality control tools.

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 (DCS), and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics toolset 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 polymerase chain reaction and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which likely reflect barcode collisions. Finally, we also developed a tool 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. With this tool, we can include reads without a family and check the reliability of the call, that increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.