The International Virus Bioinformatics Meeting 2023

Abstract

The 2023 International Virus Bioinformatics Meeting was held in Valencia, Spain, from 24-26 May 2023, attracting approximately 180 participants worldwide. The primary objective of the conference was to establish a dynamic scientific environment conducive to discussion, collaboration, and the generation of novel research ideas. As the first in-person event following the SARS-CoV-2 pandemic, the meeting facilitated highly interactive exchanges among attendees. It served as a pivotal gathering for gaining insights into the current status of virus bioinformatics research and engaging with leading researchers and emerging scientists. The event comprised eight invited talks, 19 contributed talks, and 74 poster presentations across eleven sessions spanning three days. Topics covered included machine learning, bacteriophages, virus discovery, virus classification, virus visualization, viral infection, viromics, molecular epidemiology, phylodynamic analysis, RNA viruses, viral sequence analysis, viral surveillance, and metagenomics. This report provides rewritten abstracts of the presentations, a summary of the key research findings, and highlights shared during the meeting.

Keywords: RNA viruses; bacteriophages; bioinformatics; machine learning; metagenomics; molecular epidemiology; phylodynamic analysis; tools; viral infection; viral sequence analysis; viral surveillance; viromics; virus classification; virus discovery; virus visualization.

Figure 1

Unlocking Earth’s Virome via public data. Petabases of total (public and dbGAP) sequencing data in the Sequence Read Archive (SRA), collected over 15 years is growing exponentially (inlay) and spans the globe. Data geo-coded from SRA [16] and associated BioSamples [17] meta-data.

Figure 2

This is an overview of the pipeline utilized in NeoRdRp [22]. The input amino acid data and the resulting HMM profiles are shown in yellow and orange, respectively. First-round HMM profiles are created using curated RdRp seed datasets. These HMM profiles are then used to search for RdRp domain sequences using HMM search. Finally, second-round HMM profiles are created by the seed RdRp datasets with the obtained RdRp domain data.

Figure 3

Conventional confocal (left) and super-resolution STED (right) microscopy images (side x-z view) of a SARS-CoV-2 virus like-particle (green, GFP-labelled) with a supported lipid bilayer (fire scale, DOPC lipids decorated with ACE-2, membranes labelled with fluorescent lipid analog), highlighting the improved spatial resolution to investigate molecular details in such interaction. Data taken by Ziliang Zhao (Jena).

Figure 4

Dynamics of virus accumulation and cellular responses along infection. (a) Virus accumulation as a function of doubling times. Blue lines represent the number of viral transcripts in infected cells. Red lines the baseline stablished for non-infected cells. (b) Triphasic pattern of gene expression observed for 90% of cellular genes. (c) Clustering of gene expression profiles for each one of the three cell types included in the study.

Figure 5

Conceptual composition of protected extracellular DNA. (A) Microbial entities present in a water body: microbial cells, viruses containing viral and microbial genetic material, gene transfer agents and extracellular vesicles containing host DNA. After size filtration (0.22 $\mathsf{\mu }$m) and DNase treatment and, if applicable, purification via density gradients, microbial cells and free DNA are removed. (B) The remaining DNA makes up the sequence space of protected extracellular DNA, peDNA.

Figure 6

A phylogeny for HPAI H5N8 and descendant H5N1 viruses sampled in Europe since 2020 alongside schematic showing reassortment profile. Haemagglutinin phylogenetic tree generated from stratified sample of haemagglutinin nucleotide sequences sampled in Europe. Sampling date is indicated by tip node color according to the color key. To the right, a schematic shows clusters for each genomic segment for viruses of the H5N1 subtype, while the absence of such information indicates an H5N8 genome. For each segment/column, colors are assigned independently therefore the same color in two different columns should not be interpreted as indicating a shared evolutionary history between segments. Viruses with different combinations of colors across the 8 genomic segments are interpreted as arising from reassortment events.

Figure 7

Alignment-based RNA secondary structure prediction upstream of the VP40 gene in Filoviridae. (A) The alignment calculated using LocARNA [63] reveals structural conservation with the presence of compensatory mutations. Up to three different base pair types are observed, as indicated by the color scheme (shown in the bottom right), within the non-coding region upstream of the VP40 gene. However, the alignment also shows disruption of the conserved transcription start site (TSS) sequence motif (illustrated by the black box). (B) Through manual curation, both the sequence and structure were preserved in the alignment, accurately representing the conserved elements. This refinement process led to fewer compensatory mutations, as illustrated by the base pair color scheme.

Figure 8

RNAswarm is a reproducible nextflow pipeline that analyzes datasets from RNA proximity ligation experiments. RNAswarm first pre-processes raw reads from RNA proximity ligation experiments, maps them to reference genomes, then identifies RRIs, and finally analyzes differential RRI representation across different strains or experimental conditions.

Figure 9

By analysing the raw signals of samples signal differences can be detected. Magnipore can read these differences and discriminate between mutations differences and possible differential RNA modifications.

Figure 10

Low testosterone levels are a hallmark of severe and even fatal avian H7N9 influenza infection in men. *—p < $0.05$; ****—p < $0.0001$. Modified from [117].