The effect of variant interference on de novo assembly for viral deep sequencing

A comparative study of flow cytometry-sorted communities and shotgun viral metagenomics in a Singapore municipal wastewater treatment plant

Traditional or "bulk" viral enrichment and amplification methods used in viral metagenomics introduce unavoidable bias in viral diversity. This bias is due to shortcomings in existing viral enrichment methods and overshadowing by the more abundant viral populations. To reduce the complexity and improve the resolution of viral diversity, we developed a strategy coupling fluorescence-activated cell sorting (FACS) with random amplification and compared this to bulk metagenomics. This strategy was validated on both influent and effluent samples from a municipal wastewater treatment plant using the Modified Ludzack-Ettinger (MLE) process as the treatment method. We found that DNA and RNA communities generated using bulk samples were mostly different from those derived following FACS for both treatments before and after MLE. Before MLE treatment, FACS identified five viral families and 512 viral annotated contigs. Up to 43% of mapped reads were not detected in bulk samples. Nucleo-cytoplasmic large DNA viral families were enriched to a greater extent in the FACS-coupled subpopulations compared with bulk samples. FACS-coupled viromes captured a single-contig viral genome associated with Anabaena phage, which was not observed in bulk samples or in FACS-sorted samples after MLE. These short metagenomic reads, which were assembled into a high-quality draft genome of 46 kbp, were found to be highly dominant in one of the pre-MLE FACS annotated virome fractions (57.4%). Using bulk metagenomics, we identified that between Primary Settling Tank and Secondary Settling Tank viromes, Virgaviridae, Astroviridae, Parvoviridae, Picobirnaviridae, Nodaviridae, and Iridoviridae were susceptible to MLE treatment. In all, bulk and FACS-coupled metagenomics are complementary approaches that enable a more thorough understanding of the community structure of DNA and RNA viruses in complex environmental samples, of which the latter is critical for increasing the sensitivity of detection of viral signatures that would otherwise be lost through bulk viral metagenomics.

VPipe: an Automated Bioinformatics Platform for Assembly and Management of Viral Next-Generation Sequencing Data

Next-generation sequencing (NGS) is a powerful tool for detecting and investigating viral pathogens; however, analysis and management of the enormous amounts of data generated from these technologies remains a challenge. Here, we present VPipe (the Viral NGS Analysis Pipeline and Data Management System), an automated bioinformatics pipeline optimized for whole-genome assembly of viral sequences and identification of diverse species. VPipe automates the data quality control, assembly, and contig identification steps typically performed when analyzing NGS data. Users access the pipeline through a secure web-based portal, which provides an easy-to-use interface with advanced search capabilities for reviewing results. In addition, VPipe provides a centralized system for storing and analyzing NGS data, eliminating common bottlenecks in bioinformatics analyses for public health laboratories with limited on-site computational infrastructure. The performance of VPipe was validated through the analysis of publicly available NGS data sets for viral pathogens, generating high-quality assemblies for 12 data sets. VPipe also generated assemblies with greater contiguity than similar pipelines for 41 human respiratory syncytial virus isolates and 23 SARS-CoV-2 specimens. IMPORTANCE Computational infrastructure and bioinformatics analysis are bottlenecks in the application of NGS to viral pathogens. As of September 2021, VPipe has been used by the U.S. Centers for Disease Control and Prevention (CDC) and 12 state public health laboratories to characterize >17,500 and 1,500 clinical specimens and isolates, respectively. VPipe automates genome assembly for a wide range of viruses, including high-consequence pathogens such as SARS-CoV-2. Such automated functionality expedites public health responses to viral outbreaks and pathogen surveillance.

A Computational Framework for Pattern Detection on Unaligned Sequences: An Application on SARS-CoV-2 Data

The exponential growth of genome sequences available has spurred research on pattern detection with the aim of extracting evolutionary signal. Traditional approaches, such as multiple sequence alignment, rely on positional homology in order to reconstruct the phylogenetic history of taxa. Yet, mining information from the plethora of biological data and delineating species on a genetic basis, still proves to be an extremely difficult problem to consider. Multiple algorithms and techniques have been developed in order to approach the problem multidimensionally. Here, we propose a computational framework for identifying potentially meaningful features based on k-mers retrieved from unaligned sequence data. Specifically, we have developed a process which makes use of unsupervised learning techniques in order to identify characteristic k-mers of the input dataset across a range of different k-values and within a reasonable time frame. We use these k-mers as features for clustering the input sequences and identifying differences between the distributions of k-mers across the dataset. The developed algorithm is part of an innovative and much promising approach both to the problem of grouping sequence data based on their inherent characteristic features, as well as for the study of changes in the distributions of k-mers, as the k-value is fluctuating within a range of values. Our framework is fully developed in Python language as an open source software licensed under the MIT License, and is freely available at https://github.com/BiodataAnalysisGroup/kmerAnalyzer.

A novel vieuvirus from multidrug-resistant Acinetobacter baumannii

Bacteriophages are considered the most abundant biological entities on earth, and they are able to modulate the populations of their bacterial hosts. Although the potential of bacteriophages has been accepted as an alternative strategy to combat multidrug-resistant pathogenic bacteria, there still exists a considerable knowledge gap regarding their genetic diversity, which hinders their use as antimicrobial agents. In this study, we undertook a genomic and phylogenetic characterization of the phage Ab11510-phi, which was isolated from a multidrug-resistant Acinetobacter baumannii strain (Ab11510). We found that Ab11510-phi has a narrow host range and belongs to a small group of transposable phages of the genus Vieuvirus that have only been reported to infect Acinetobacter bacteria. Finally, we showed that Ab11510-phi (as well as other vieuvirus phages) has a high level of mosaicism. On a broader level, we demonstrate that comparative genomics and phylogenetic analysis are necessary tools for the proper characterization of phage diversity.