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1. The Global Virome in One Network (VIRION): an Atlas of Vertebrate-Virus Associations

   https://doi.org/10.1128/mbio.02985-21  

   Carlson, Colin J. ; Gibb, Rory J. ; Albery, Gregory F. ; Brierley, Liam ; Connor, Ryan P. ; Dallas, Tad A. ; Eskew, Evan A. ; Fagre, Anna C. ; Farrell, Maxwell J. ; Frank, Hannah K. ; et al ( April 2022 , mBio)

   Pickett, Brett E. ; Jurado, Kellie (Ed.)

   ABSTRACT Data that catalogue viral diversity on Earth have been fragmented across sources, disciplines, formats, and various degrees of open sharing, posing challenges for research on macroecology, evolution, and public health. Here, we solve this problem by establishing a dynamically maintained database of vertebrate-virus associations, called The Global Virome in One Network (VIRION). The VIRION database has been assembled through both reconciliation of static data sets and integration of dynamically updated databases. These data sources are all harmonized against one taxonomic backbone, including metadata on host and virus taxonomic validity and higher classification; additional metadata on sampling methodology and evidence strength are also available in a harmonized format. In total, the VIRION database is the largest open-source, open-access database of its kind, with roughly half a million unique records that include 9,521 resolved virus “species” (of which 1,661 are ICTV ratified), 3,692 resolved vertebrate host species, and 23,147 unique interactions between taxonomically valid organisms. Together, these data cover roughly a quarter of mammal diversity, a 10th of bird diversity, and ∼6% of the estimated total diversity of vertebrates, and a much larger proportion of their virome than any previous database. We show how these data can be used to testmore »hypotheses about microbiology, ecology, and evolution and make suggestions for best practices that address the unique mix of evidence that coexists in these data. IMPORTANCE Animals and their viruses are connected by a sprawling, tangled network of species interactions. Data on the host-virus network are available from several sources, which use different naming conventions and often report metadata in different levels of detail. VIRION is a new database that combines several of these existing data sources, reconciles taxonomy to a single consistent backbone, and reports metadata in a format designed by and for virologists. Researchers can use VIRION to easily answer questions like “Can any fish viruses infect humans?” or “Which bats host coronaviruses?” or to build more advanced predictive models, making it an unprecedented step toward a full inventory of the global virome.« less
2. A complete reference genome improves analysis of human genetic variation

   https://doi.org/10.1126/science.abl3533  

   Aganezov, Sergey ; Yan, Stephanie M. ; Soto, Daniela C. ; Kirsche, Melanie ; Zarate, Samantha ; Avdeyev, Pavel ; Taylor, Dylan J. ; Shafin, Kishwar ; Shumate, Alaina ; Xiao, Chunlin ; et al ( April 2022 , Science)

   INTRODUCTION One of the central applications of the human reference genome has been to serve as a baseline for comparison in nearly all human genomic studies. Unfortunately, many difficult regions of the reference genome have remained unresolved for decades and are affected by collapsed duplications, missing sequences, and other issues. Relative to the current human reference genome, GRCh38, the Telomere-to-Telomere CHM13 (T2T-CHM13) genome closes all remaining gaps, adds nearly 200 million base pairs (Mbp) of sequence, corrects thousands of structural errors, and unlocks the most complex regions of the human genome for scientific inquiry. RATIONALE We demonstrate how the T2T-CHM13 reference genome universally improves read mapping and variant identification in a globally diverse cohort. This cohort includes all 3202 samples from the expanded 1000 Genomes Project (1KGP), sequenced with short reads, as well as 17 globally diverse samples sequenced with long reads. By applying state-of-the-art methods for calling single-nucleotide variants (SNVs) and structural variants (SVs), we document the strengths and limitations of T2T-CHM13 relative to its predecessors and highlight its promise for revealing new biological insights within technically challenging regions of the genome. RESULTS Across the 1KGP samples, we found more than 1 million additional high-quality variants genome-wide using T2T-CHM13more »than with GRCh38. Within previously unresolved regions of the genome, we identified hundreds of thousands of variants per sample—a promising opportunity for evolutionary and biomedical discovery. T2T-CHM13 improves the Mendelian concordance rate among trios and eliminates tens of thousands of spurious SNVs per sample, including a reduction of false positives in 269 challenging, medically relevant genes by up to a factor of 12. These corrections are in large part due to improvements to 70 protein-coding genes in >9 Mbp of inaccurate sequence caused by falsely collapsed or duplicated regions in GRCh38. Using the T2T-CHM13 genome also yields a more comprehensive view of SVs genome-wide, with a greatly improved balance of insertions and deletions. Finally, by providing numerous resources for T2T-CHM13 (including 1KGP genotypes, accessibility masks, and prominent annotation databases), our work will facilitate the transition to T2T-CHM13 from the current reference genome. CONCLUSION The vast improvements in variant discovery across samples of diverse ancestries position T2T-CHM13 to succeed as the next prevailing reference for human genetics. T2T-CHM13 thus offers a model for the construction and study of high-quality reference genomes from globally diverse individuals, such as is now being pursued through collaboration with the Human Pangenome Reference Consortium. As a foundation, our work underscores the benefits of an accurate and complete reference genome for revealing diversity across human populations. Genomic features and resources available for T2T-CHM13. Comparisons to GRCh38 reveal broad improvements in SNVs, indels, and SVs discovered across diverse human populations by means of short-read (1KGP) and long-read sequencing (LRS). These improvements are due to resolution of complex genomic loci (nonsyntenic and previously unresolved), duplication errors, and discordant haplotypes, including those in medically relevant genes.« less
3. DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins

   https://doi.org/10.1093/bioinformatics/btz863  

   Zhang, Chengxin ; Zheng, Wei ; Mortuza, S M ; Li, Yang ; Zhang, Yang ; Valencia, Alfonso ( November 2019 , Bioinformatics)

   Abstract Motivation The success of genome sequencing techniques has resulted in rapid explosion of protein sequences. Collections of multiple homologous sequences can provide critical information to the modeling of structure and function of unknown proteins. There are however no standard and efficient pipeline available for sensitive multiple sequence alignment (MSA) collection. This is particularly challenging when large whole-genome and metagenome databases are involved. Results We developed DeepMSA, a new open-source method for sensitive MSA construction, which has homologous sequences and alignments created from multi-sources of whole-genome and metagenome databases through complementary hidden Markov model algorithms. The practical usefulness of the pipeline was examined in three large-scale benchmark experiments based on 614 non-redundant proteins. First, DeepMSA was utilized to generate MSAs for residue-level contact prediction by six coevolution and deep learning-based programs, which resulted in an accuracy increase in long-range contacts by up to 24.4% compared to the default programs. Next, multiple threading programs are performed for homologous structure identification, where the average TM-score of the template alignments has over 7.5% increases with the use of the new DeepMSA profiles. Finally, DeepMSA was used for secondary structure prediction and resulted in statistically significant improvements in the Q3 accuracy. It is notedmore »that all these improvements were achieved without re-training the parameters and neural-network models, demonstrating the robustness and general usefulness of the DeepMSA in protein structural bioinformatics applications, especially for targets without homologous templates in the PDB library. Availability and implementation https://zhanglab.ccmb.med.umich.edu/DeepMSA/. Supplementary information Supplementary data are available at Bioinformatics online.« less
4. Towards quantitative viromics for both double-stranded and single-stranded DNA viruses

   https://doi.org/10.7717/peerj.2777  

   Roux, Simon ; Solonenko, Natalie E. ; Dang, Vinh T. ; Poulos, Bonnie T. ; Schwenck, Sarah M. ; Goldsmith, Dawn B. ; Coleman, Maureen L. ; Breitbart, Mya ; Sullivan, Matthew B. ( December 2016 , PeerJ)

   Viruses strongly influence microbial population dynamics and ecosystem functions. However, our ability to quantitatively evaluate those viral impacts is limited to the few cultivated viruses and double-stranded DNA (dsDNA) viral genomes captured in quantitative viral metagenomes (viromes). This leaves the ecology of non-dsDNA viruses nearly unknown, including single-stranded DNA (ssDNA) viruses that have been frequently observed in viromes, but not quantified due to amplification biases in sequencing library preparations (Multiple Displacement Amplification, Linker Amplification or Tagmentation).

   Here we designed mock viral communities including both ssDNA and dsDNA viruses to evaluate the capability of a sequencing library preparation approach including an Adaptase step prior to Linker Amplification for quantitative amplification of both dsDNA and ssDNA templates. We then surveyed aquatic samples to provide first estimates of the abundance of ssDNA viruses.

   Mock community experiments confirmed the biased nature of existing library preparation methods for ssDNA templates (either largely enriched or selected against) and showed that the protocol using Adaptase plus Linker Amplification yielded viromes that were ±1.8-fold quantitative for ssDNA and dsDNA viruses. Application of this protocol to community virus DNA from three freshwater and three marine samples revealed that ssDNA viruses as a whole represent only a minor fraction (<5%)more »of DNA virus communities, though individual ssDNA genomes, both eukaryote-infecting Circular Rep-Encoding Single-Stranded DNA (CRESS-DNA) viruses and bacteriophages from theMicroviridaefamily, can be among the most abundant viral genomes in a sample.

   Together these findings provide empirical data for a new virome library preparation protocol, and a first estimate of ssDNA virus abundance in aquatic systems.

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5. Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation

   https://doi.org/10.7717/peerj.11447  

   Pratama, Akbar Adjie ; Bolduc, Benjamin ; Zayed, Ahmed A. ; Zhong, Zhi-Ping ; Guo, Jiarong ; Vik, Dean R. ; Gazitúa, Maria Consuelo ; Wainaina, James M. ; Roux, Simon ; Sullivan, Matthew B. ( January 2021 , PeerJ)

   Background Viruses influence global patterns of microbial diversity and nutrient cycles. Though viral metagenomics (viromics), specifically targeting dsDNA viruses, has been critical for revealing viral roles across diverse ecosystems, its analyses differ in many ways from those used for microbes. To date, viromics benchmarking has covered read pre-processing, assembly, relative abundance, read mapping thresholds and diversity estimation, but other steps would benefit from benchmarking and standardization. Here we use in silico-generated datasets and an extensive literature survey to evaluate and highlight how dataset composition (i.e., viromes vs bulk metagenomes) and assembly fragmentation impact (i) viral contig identification tool, (ii) virus taxonomic classification, and (iii) identification and curation of auxiliary metabolic genes (AMGs). Results The in silico benchmarking of five commonly used virus identification tools show that gene-content-based tools consistently performed well for long (≥3 kbp) contigs, while k -mer- and blast-based tools were uniquely able to detect viruses from short (≤3 kbp) contigs. Notably, however, the performance increase of k -mer- and blast-based tools for short contigs was obtained at the cost of increased false positives (sometimes up to ∼5% for virome and ∼75% bulk samples), particularly when eukaryotic or mobile genetic element sequences were included in the test datasets.more »For viral classification, variously sized genome fragments were assessed using gene-sharing network analytics to quantify drop-offs in taxonomic assignments, which revealed correct assignations ranging from ∼95% (whole genomes) down to ∼80% (3 kbp sized genome fragments). A similar trend was also observed for other viral classification tools such as VPF-class, ViPTree and VIRIDIC, suggesting that caution is warranted when classifying short genome fragments and not full genomes. Finally, we highlight how fragmented assemblies can lead to erroneous identification of AMGs and outline a best-practices workflow to curate candidate AMGs in viral genomes assembled from metagenomes. Conclusion Together, these benchmarking experiments and annotation guidelines should aid researchers seeking to best detect, classify, and characterize the myriad viruses ‘hidden’ in diverse sequence datasets.« less