The International Virus Bioinformatics Meeting 2020

Abstract

The International Virus Bioinformatics Meeting 2020 was originally planned to take place in Bern, Switzerland, in March 2020. However, the COVID-19 pandemic put a spoke in the wheel of almost all conferences to be held in 2020. After moving the conference to 8-9 October 2020, we got hit by the second wave and finally decided at short notice to go fully online. On the other hand, the pandemic has made us even more aware of the importance of accelerating research in viral bioinformatics. Advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks. The International Virus Bioinformatics Meeting 2020 has attracted approximately 120 experts in virology and bioinformatics from all over the world to join the two-day virtual meeting. Despite concerns being raised that virtual meetings lack possibilities for face-to-face discussion, the participants from this small community created a highly interactive scientific environment, engaging in lively and inspiring discussions and suggesting new research directions and questions. The meeting featured five invited and twelve contributed talks, on the four main topics: (1) proteome and RNAome of RNA viruses, (2) viral metagenomics and ecology, (3) virus evolution and classification and (4) viral infections and immunology. Further, the meeting featured 20 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting.

Keywords: COVID-19; genome evolution; identification; metagenomics; software; viral diversity; viral taxonomy; virology; virome; virus bioinformatics.

Figure 1

The number of identified non-identical RdRp sequences. Each identified sequence with high score was trimmed to the RdRp core and then sorted into the genetically most similar group (the best HMM profile match). The 100% identical sequences within a group have been removed. Here, we only show groups, where the total number of sequences left are above 50. The genome type of each group is identified as follows: +s is positive single-stranded, -s is negative single-stranded and ds is double-stranded RNA genome. The source of the sequences is identified by different colours.

Figure 2

Ribosome profiling of PRRSV-2, displaying reads mapped to the polyprotein region of the genome. (A) Read density at each position, after application of a 27-codon sliding window running mean, coloured according to phase (codon position to which the 5${}^{\prime }$ end of a read maps). An increase in the proportion of reads in the −2 phase (blue) is visible in the transframe region directly following the nsp2 frameshift site. The dominant phase returns to 0 (purple) after the stop codon in the −2 frame causes termination of frameshifted ribosomes. After the ORF1ab frameshift site, the ribosome density decreases, as ribosomes which do not frameshift encounter a stop codon, and ribosomes which undergo −1 PRF continue translation in the −1 frame, resulting in reads predominantly in the −1 phase (yellow). (B) Frameshift efficiency at the ORF1ab site increases as infection progresses. (C) A small but highly expressed upstream ORF (uORF) in the 5${}^{\prime }$UTR of the PRRSV genome. The prominent peak corresponding to terminating ribosomes is characteristic of samples harvested without drug pre-treatment.

Figure 3

Growth of virus genome databases from 2003 to 2020. The total number of genomes from isolates was based on queries to the NCBI nucleotide database portal, while the number of uncultivated virus genomes (UViGs) was estimated by compiling data from the literature and from the IMG/VR database, as in Paez-Espino et al. [38].

Figure 4

Basic workflow of V-pipe for end-to-end analysis of viral NGS data. Starting from a heterogeneous virus population, amplification and sequencing will produce a set of sequencing reads, each derived from one of the molecules in the original mixed sample. Reads are aligned, possibly with the help of a reference genome, and filtered according to various quality control criteria. From the multiple alignment of all reads, genetic variants are called either position-wise (single-nucleotide variants, SNVs) or for longer genomic regions (viral haplotype reconstruction). The results, including all SNVs and their estimated frequencies in the virus population, are summarized and visualized in an electronic interactive report.

Figure 5

(A) The UpSet plot is summarizing the identification performance of each tool for a multi fasta sample. The total amount of identified phage-contigs per tool is shown in blue bars on the left. Black bars visualize the number of contigs that each tool or tool combination has uniquely identified. Each tool combination is shown below the barplot as a dot matrix. (B) Modified heatmap for phage sequences visualising the tool agreements per phage positive contig. (C) Visual annotation of phage contigs and annotated protein-coding genes via chromoMap. Annotations are coloured based on the categories of capsid genes (blue), tail genes (orange), baseplate genes (green) and other phage genes (red). For better readability, the contigs pos_phage_0 and pos_phage_9 were removed from the chromomap.

Figure 6

Maximum-likelihood phylogenies of viruses found in our study [54], classified viruses by ICTV, as well as selected unclassified viruses. Novel viruses from our study are identified by black circles. Abbreviations for the Rhabdoviridae phylogeny are: AAR—Almendra-related virus, CAR—Coleoptera-related viruses, HAR1 and −2—Hymenoptera-related viruses, DHRC—Diptera-, Hymenoptera-, and Coleoptera-related viruses, LAR—Lepidoptera-related viruses, MBAR—Mantodea-, and Blattodea-related viruses. The Orthomyxoviridae and the Mononegavirales excl. Rhabdoviridae phylogenies carry annotations in roman numbers for the different Orthomyxoviridae lineages, and Arlivirus, Orinovirus, and Anphevirus lineages that are defined after the addition of our data. Insect host orders relative to clades are watermarked wherever applicable.

Figure 7

(A) Workflow for extracting and testing different level feature sets for predicting host taxon information. (B) Comparison of the results (AUC) for all the bacteria datasets for all the feature sets. (C) A plot of sensitivity versus specificity for kernel combination applied to one dataset. Each point represents the results for a classifier, each with a different combination of kernel weights, with the number of kernels contributing shown by the point colour.

Figure 8

Identifying the hosts of metagenomic viruses in the Cressdnaviricota. (A) Recombination between single-stranded DNA viruses implies a shared cellular host, reducing the problem size from individual virus species to recombination networks. (B) Endogenous viral elements inside host genomes are used to anchor metagenomic viruses to specific host taxa. Created with BioRender.com.