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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. [ABSTRACT FROM AUTHOR]

Viruses are the cause of a considerable burden to human, animal and plant health, while on the other hand playing an important role in regulating entire ecosystems. The power of new sequencing technologies combined with new tools for processing "Big Data" offers unprecedented opportunities to answer fundamental questions in virology. Virologists have an urgent need for virus-specific bioinformatics tools. These developments have led to the formation of the European Virus Bioinformatics Center, a network of experts in virology and bioinformatics who are joining forces to enable extensive exchange and collaboration between these research areas. The EVBC strives to provide talented researchers with a supportive environment free of gender bias, but the gender gap in science, especially in math-intensive fields such as computer science, persists. To bring more talented women into research and keep them there, we need to highlight role models to spark their interest, and we need to ensure that female scientists are not kept at lower levels but are given the opportunity to lead the field. Here we showcase the work of the EVBC and highlight the achievements of some outstanding women experts in virology and viral bioinformatics. [ABSTRACT FROM AUTHOR]

Non-coding RNAs (ncRNAs) play a central and regulatory role in almost all cells, organs, and species, which has been broadly recognized since the human ENCODE project and several other genome projects. Nevertheless, a small fraction of ncRNAs have been identified, and in the placenta they have been investigated very marginally. To date, most examples of ncRNAs which have been identified to be specific for fetal tissues, including placenta, are members of the group of microRNAs (miRNAs). Due to their quantity, it can be expected that the fairly larger group of other ncRNAs exerts far stronger effects than miRNAs. The syncytiotrophoblast of fetal origin forms the interface between fetus and mother, and releases permanently extracellular vesicles (EVs) into the maternal circulation which contain fetal proteins and RNA, including ncRNA, for communication with neighboring and distant maternal cells. Disorders of ncRNA in placental tissue, especially in trophoblast cells, and in EVs seem to be involved in pregnancy disorders, potentially as a cause or consequence. This review summarizes the current knowledge on placental ncRNA, their transport in EVs, and their involvement and pregnancy pathologies, as well as their potential for novel diagnostic tools. [ABSTRACT FROM AUTHOR]

The International Virus Bioinformatics Meeting 2022 took place online, on 23–25 March 2022, and has attracted about 380 participants from all over the world. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The participants created a highly interactive scientific environment even without physical face-to-face interactions. This meeting is a focal point to gain an insight into the state-of-the-art of the virus bioinformatics research landscape and to interact with researchers in the forefront as well as aspiring young scientists. The meeting featured eight invited and 18 contributed talks in eight sessions on three days, as well as 52 posters, which were presented during three virtual poster sessions. The main topics were: SARS-CoV-2, viral emergence and surveillance, virus–host interactions, viral sequence analysis, virus identification and annotation, phages, and viral diversity. This report summarizes the main research findings and highlights presented at the meeting. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact: evbc@unj-jena.de [ABSTRACT FROM AUTHOR]

Mass spectrometry (MS) is a key technology for the analysis of small molecules. For the identification and structural elucidation of novel molecules, new approaches beyond straightforward spectral comparison are required. In this review, we will cover computational methods that help with the identification of small molecules by analyzing fragmentation MS data. We focus on the four main approaches to mine a database of metabolite structures, that is rule-based fragmentation spectrum prediction, combinatorial fragmentation, competitive fragmentation modeling, and molecular fingerprint prediction. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:624-633, 2017 [ABSTRACT FROM AUTHOR]

The determination of the molecular formula is one of the earliest and most important steps when investigating the chemical nature of an unknown compound. Common approaches use the isotopic pattern of a compound measured using mass spectrometry. Computational methods to determine the molecular formula from this isotopic pattern require a fixed set of elements. Considering all possible elements severely increases running times and more importantly the chance for false positive identifications as the number of candidate formulas for a given target mass rises significantly if the constituting elements are not prefiltered. This negative effect grows stronger for compounds of higher molecular mass as the effect of a single atom on the overall isotopic pattern grows smaller. On the other hand, hand-selected restrictions on this set of elements may prevent the identification of the correct molecular formula. Thus, it is a crucial step to determine the set of elements most likely comprising the compound prior to the assignment of an elemental formula to an exact mass. In this paper, we present a method to determine the presence of certain elements (sulfur, chlorine, bromine, boron, and selenium) in the compound from its (high mass accuracy) isotopic pattern. We limit ourselves to biomolecules, in the sense of products from nature or synthetic products with potential bioactivity. The classifiers developed here predict the presence of an element with a very high sensitivity and high specificity. We evaluate classifiers on three real-world data sets with 663 isotope patterns in total: 184 isotope patterns containing sulfur, 187 containing chlorine, 14 containing bromine, one containing boron, one containing selenium. In no case do we make a false negative prediction; for chlorine, bromine, boron, and selenium, we make ten false positive predictions in total. We also demonstrate the impact of our method on the identification of molecular formulas, in particular on the number of considered candidates and running time. The element prediction will be part of the next SIRIUS release, available from https://bio.informatik.uni-jena.de/software/sirius/. [ABSTRACT FROM AUTHOR]

The article offers information on virologists and bioinformaticians, and discuss the European Virus Bioinformatics Center (EVBC) founded by the authors. Topics discussed include the need of bioinformatics tools for virologists to tackle diseases, the role of viruses in human genome, and the molecular evolution in viruses.

Covering: 2008 to 2014 Mass spectrometry is a key technology for the identification and structural elucidation of natural products. Manual interpretation of the resulting data is tedious and time-consuming, so methods for automated analysis are highly sought after. In this review, we focus on four recently developed methods for the detection and investigation of small molecules, namely MetFrag/MetFusion, ISIS, FingerID, and FT-BLAST. These methods have the potential to significantly advance the field of computational mass spectrometry for the research of natural products. For example, they may help with the dereplication of compounds at an early stage of the drug discovery process; that is, the detection of molecules that are identical or highly similar to known drugs or drug leads. Furthermore, when a potential drug lead has been determined, these tools may help to identify it and elucidate its structure. [ABSTRACT FROM AUTHOR]

Highlights: [•] Identifying small molecules from mass spectrometry data remains a major challenge. [•] Computational analysis of fragmentation data requires novel strategies. [•] Five fundamental strategies for computational analysis of fragmentation data. [•] Searching spectral libraries, fragmentation spectrum prediction, fragmentation trees. [•] Combinatorial fragmentation, and predicting structural features. [Copyright &y& Elsevier]

The identification of smallmolecules from mass spectrometry (MS) data remains amajor challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline [ABSTRACT FROM AUTHOR]

Abstract: The automated fragmentation analysis of high resolution EI mass spectra based on a fragmentation tree algorithm is introduced. Fragmentation trees are constructed from EI spectra by automated signal extraction and evaluation. These trees explain relevant fragmentation reactions and assign molecular formulas to fragments. The method enables the identification of the molecular ion and the molecular formula of a metabolite if the molecular ion is present in the spectrum. These identifications are independent of existing library knowledge and, thus, support assignment and structural elucidation of unknown compounds. The method works even if the molecular ion is of very low abundance or hidden under contaminants with higher masses. We apply the algorithm to a selection of 50 derivatized and underivatized metabolites and demonstrate that in 78% of cases the molecular ion can be correctly assigned. The automatically constructed fragmentation trees correspond very well to published mechanisms and allow the assignment of specific relevant fragments and fragmentation pathways even in the most complex EI-spectra in our dataset. This method will be very helpful in the automated analysis of metabolites that are not included in common libraries and it thus has the potential to support the explorative character of metabolomics studies. [Copyright &y& Elsevier]

Since metabolites cannot be predicted from the genome sequence, high-throughput de novo identification of small molecules is highly sought. Mass spectrometry (MS) in combination with a fragmentation technique is commonly used for this task. Unfortunately, automated analysis of such data is in its infancy. Recently, fragmentation trees have been proposed as an analysis tool for such data. Additional fragmentation steps (MS n) reveal more information about the molecule. We propose to use MS n data for the computation of fragmentation trees, and present the C olorful S ubtree C losure problem to formalize this task: There, we search for a colorful subtree inside a vertex-colored graph, such that the weight of the transitive closure of the subtree is maximal. We give several negative results regarding the tractability and approximability of this and related problems. We then present an exact dynamic programming algorithm, which is parameterized by the number of colors in the graph and is swift in practice. Evaluation of our method on a dataset of 45 reference compounds showed that the quality of constructed fragmentation trees is improved by using MS n instead of MS2 measurements. [ABSTRACT FROM AUTHOR]

Background: The center string (or closest string) problem is a classic computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a string within Hamming distance at most d to each input string. This problem is NP complete. Results: In this paper, we focus on exact methods for the problem that are also swift in application. We first introduce data reduction techniques that allow us to infer that certain instances have no solution, or that a center string must satisfy certain conditions. We describe how to use this information to speed up two previously published search tree algorithms. Then, we describe a novel iterative search strategy that is effecient in practice, where some of our reduction techniques can also be applied. Finally, we present results of an evaluation study for two different data sets from a biological application. Conclusions: We find that the running time for computing the optimal center string is dominated by the subroutine calls for d = dopt -1 and d = dopt. Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions. We find that this speeds up computations considerably. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Third Annual Meeting of the European Virus Bioinformatics Center (EVBC) took place in Glasgow, United Kingdom, 28–29 March 2019. Virus bioinformatics has become central to virology research, and advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks, being successfully used to detect, control, and treat infections of humans and animals. This active field of research has attracted approximately 110 experts in virology and bioinformatics/computational biology from Europe and other parts of the world to attend the two-day meeting in Glasgow to increase scientific exchange between laboratory- and computer-based researchers. The meeting was held at the McIntyre Building of the University of Glasgow; a perfect location, as it was originally built to be a place for "rubbing your brains with those of other people", as Rector Stanley Baldwin described it. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The meeting featured eight invited and twelve contributed talks, on the four main topics: (1) systems virology, (2) virus-host interactions and the virome, (3) virus classification and evolution and (4) epidemiology, surveillance and evolution. Further, the meeting featured 34 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. [ABSTRACT FROM AUTHOR]

Despite the recognized excellence of virology and bioinformatics, these two communities have interacted surprisingly sporadically, aside from some pioneering work on HIV-1 and influenza. Bringing together the expertise of bioinformaticians and virologists is crucial, since very specific but fundamental computational approaches are required for virus research, particularly in an era of big data. Collaboration between virologists and bioinformaticians is necessary to improve existing analytical tools, cloud-based systems, computational resources, data sharing approaches, new diagnostic tools, and bioinformatic training. Here, we highlight current progress and discuss potential avenues for future developments in this promising era of virus bioinformatics. We end by presenting an overview of current technologies, and by outlining some of the major challenges and advantages that bioinformatics will bring to the field of virology. [ABSTRACT FROM AUTHOR]

Motivation: Mass spectrometry allows sensitive, automated and high-throughput analysis of small molecules such as metabolites. One major bottleneck in metabolomics is the identification of ‘unknown’ small molecules not in any database. Recently, fragmentation tree alignments have been introduced for the automated comparison of the fragmentation patterns of small molecules. Fragmentation pattern similarities are strongly correlated with the chemical similarity of the molecules, and allow us to cluster compounds based solely on their fragmentation patterns.Results: Aligning fragmentation trees is computationally hard. Nevertheless, we present three exact algorithms for the problem: a dynamic programming (DP) algorithm, a sparse variant of the DP, and an Integer Linear Program (ILP). Evaluation of our methods on three different datasets showed that thousands of alignments can be computed in a matter of minutes using DP, even for ‘challenging’ instances. Running times of the sparse DP were an order of magnitude better than for the classical DP. The ILP was clearly outperformed by both DP approaches. We also found that for both DP algorithms, computing the 1% slowest alignments required as much time as computing the 99% fastest.Contact: sebastian.boecker@uni-jena.de [ABSTRACT FROM PUBLISHER]

Mass spectrometry allows sensitive, automated, and high-throughput analysis of small molecules. In principle, tandem mass specirometry allows us to identify ~unknown small molecules not in any database, but the automated interpretation of such data is in its infancy. Fragmentation trees have recently been introduced for the automated analysis of the fragmentation patterns of small molecules. We present a method for the automated comparison of such fragmentation patterns, based on aligning the compounds' fragmentation trees. We cluster compounds based solely on their fragmentation patterns and show a good agreement with known compound dasses. Fragmentation pattern similarities are strongly correlated with the chemical similarity of molecules. We present a tool for searching a database for compounds with fragmentation pattern similar to an unknown sample compound. We apply this tool to metabolites from Icelandic poppy. Our method allows fully automated computational identification of small molecules that cannot be found in any database. [ABSTRACT FROM AUTHOR]

Experiments in which complex virome sequencing data is generated remain difficult to explore and unpack for scientists without a background in data science. The processing of raw sequencing data by high throughput sequencing workflows usually results in contigs in FASTA format coupled to an annotation file linking the contigs to a reference sequence or taxonomic identifier. The next step is to compare the virome of different samples based on the metadata of the experimental setup and extract sequences of interest that can be used in subsequent analyses. The viromeBrowser is an application written in the opensource R shiny framework that was developed in collaboration with end-users and is focused on three common data analysis steps. First, the application allows interactive filtering of annotations by default or custom quality thresholds. Next, multiple samples can be visualized to facilitate comparison of contig annotations based on sample specific metadata values. Last, the application makes it easy for users to extract sequences of interest in FASTA format. With the interactive features in the viromeBrowser we aim to enable scientists without a data science background to compare and extract annotation data and sequences from virome sequencing analysis results. [ABSTRACT FROM AUTHOR]

Hematophagous insects act as the major reservoirs of infectious agents due to their intimate contact with a large variety of vertebrate hosts. Lutzomyia longipalpis is the main vector of Leishmania chagasi in the New World, but its role as a host of viruses is poorly understood. In this work, Lu. longipalpis RNA libraries were subjected to progressive assembly using viral profile HMMs as seeds. A sequence phylogenetically related to fungal viruses of the genus Mitovirus was identified and this novel virus was named Lul-MV-1. The 2697-base genome presents a single gene coding for an RNA-directed RNA polymerase with an organellar genetic code. To determine the possible host of Lul-MV-1, we analyzed the molecular characteristics of the viral genome. Dinucleotide composition and codon usage showed profiles similar to mitochondrial DNA of invertebrate hosts. Also, the virus-derived small RNA profile was consistent with the activation of the siRNA pathway, with size distribution and 5′ base enrichment analogous to those observed in viruses of sand flies, reinforcing Lu. longipalpis as a putative host. Finally, RT-PCR of different insect pools and sequences of public Lu. longipalpis RNA libraries confirmed the high prevalence of Lul-MV-1. This is the first report of a mitovirus infecting an insect host. [ABSTRACT FROM AUTHOR]

Viruses belonging to the Coronaviridae family have a single-stranded positive-sense RNA with a poly-A tail. The genome has a length of ~29.9 kbps, which encodes for genes that are essential for cell survival and replication. Different evolutionary constraints constantly influence the codon usage bias (CUB) of different genes. A virus optimizes its codon usage to fit the host environment on which it savors. This study is a comprehensive analysis of the CUB for the different genes encoded by viruses of the Coronaviridae family. Different methods including relative synonymous codon usage (RSCU), an Effective number of codons (ENc), parity plot 2, and Neutrality plot, were adopted to analyze the factors responsible for the genetic evolution of the Coronaviridae family. Base composition and RSCU analyses demonstrated the presence of A-ended and U-ended codons being preferred in the 3rd codon position and are suggestive of mutational selection. The lesser ENc value for the spike 'S' gene suggests a higher bias in the codon usage of this gene compared to the other structural genes. Parity plot 2 and neutrality plot analyses demonstrate the role and the extent of mutational and natural selection towards the codon usage pattern. It was observed that the structural genes of the Coronaviridae family analyzed in this study were at the least under 84% influence of natural selection, implying a major role of natural selection in shaping the codon usage. [ABSTRACT FROM AUTHOR]

In recent years, next generation sequencing (NGS) technology has been widely used for the discovery of novel human papillomavirus (HPV) genotypes, variant characterization and genotyping. Here, we compared the analytical performance of NGS with a commercial PCR-based assay (Anyplex II HPV28) in cervical samples of 744 women. Overall, HPV positivity was 50.2% by the Anyplex and 45.5% by the NGS. With the NGS, we detected 25 genotypes covered by Anyplex and 41 additional genotypes. Agreement between the two methods for HPV positivity was 80.8% (kappa = 0.616) and 84.8% (kappa = 0.652) for 28 HPV genotypes and 14 high-risk genotypes, respectively. We recovered and characterized 243 complete HPV genomes from 153 samples spanning 40 different genotypes. According to phylogenetic analysis and pairwise distance, we identified novel lineages and sublineages of four high-risk and 16 low-risk genotypes. In total, 17 novel lineages and 14 novel sublineages were proposed, including novel lineages of HPV45, HPV52, HPV66 and a novel sublineage of HPV59. Our study provides important genomic insights on HPV types and lineages, where few complete genomes were publicly available. [ABSTRACT FROM AUTHOR]

Baculoviruses are a group of insect viruses with large circular dsDNA genomes exploited in numerous biotechnological applications, such as the biological control of agricultural pests, the expression of recombinant proteins or the gene delivery of therapeutic sequences in mammals, among others. Their genomes encode between 80 and 200 proteins, of which 38 are shared by all reported species. Thanks to multi-omic studies, there is remarkable information about the baculoviral proteome and the temporality in the virus gene expression. This allows some functional elements of the genome to be very well described, such as promoters and open reading frames. However, less information is available about the transcription termination signals and, consequently, there are still imprecisions about what are the limits of the transcriptional units present in the baculovirus genomes and how is the processing of the 3′ end of viral mRNA. Regarding to this, in this review we provide an update about the characteristics of DNA signals involved in this process and we contribute to their correct prediction through an exhaustive analysis that involves bibliography information, data mining, RNA structure and a comprehensive study of the core gene 3′ ends from 180 baculovirus genomes. [ABSTRACT FROM AUTHOR]

Mycoses induced by C.albicans or A.fumigatus can cause important host damage either by deficient or exaggerated immune response. Regulation of chemokine and cytokine signaling plays a crucial role for an adequate inflammation, which can be modulated by vitamins A and D. Non-coding RNAs (ncRNAs) as transcription factors or cis-acting antisense RNAs are known to be involved in gene regulation. However, the processes during fungal infections and treatment with vitamins in terms of therapeutic impact are unknown. We show that in monocytes both vitamins regulate ncRNAs involved in amino acid metabolism and immune system processes using comprehensive RNA-Seq analyses. Compared to protein-coding genes, fungi and bacteria induced an expression change in relatively few ncRNAs, but with massive fold changes of up to 4000. We defined the landscape of long-ncRNAs (lncRNAs) in response to pathogens and observed variation in the isoforms composition for several lncRNA following infection and vitamin treatment. Most of the involved antisense RNAs are regulated and positively correlated with their sense protein-coding genes. We investigated lncRNAs with stimulus specific immunomodulatory activity as potential marker genes: LINC00595, SBF2-AS1 (A.fumigatus) and RP11-588G21.2, RP11-394l13.1 (C.albicans) might be detectable in the early phase of infection and serve as therapeutic targets in the future. [ABSTRACT FROM AUTHOR]