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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.

Background: Advances in high-throughput methods have brought new challenges for biological data analysis, often requiring many interdependent steps applied to a large number of samples. To address this challenge, workflow management systems, such as Watchdog, have been developed to support scientists in the (semi-)automated execution of large analysis workflows., Implementation: Here, we present Watchdog 2.0, which implements new developments for module creation, reusability, and documentation and for reproducibility of analyses and workflow execution. Developments include a graphical user interface for semi-automatic module creation from software help pages, sharing repositories for modules and workflows, and a standardized module documentation format. The latter allows generation of a customized reference book of public and user-specific modules. Furthermore, extensive logging of workflow execution, module and software versions, and explicit support for package managers and container virtualization now ensures reproducibility of results. A step-by-step analysis protocol generated from the log file may, e.g., serve as a draft of a manuscript methods section. Finally, 2 new execution modes were implemented. One allows resuming workflow execution after interruption or modification without rerunning successfully executed tasks not affected by changes. The second one allows detaching and reattaching to workflow execution on a local computer while tasks continue running on computer clusters., Conclusions: Watchdog 2.0 provides several new developments that we believe to be of benefit for large-scale bioinformatics analysis and that are not completely covered by other competing workflow management systems. The software itself, module and workflow repositories, and comprehensive documentation are freely available at https://www.bio.ifi.lmu.de/watchdog., (© The Author(s) 2020. Published by Oxford University Press.)

Background: The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates. To support these analyses, a number of workflow management systems (WMSs) have been developed to allow automated execution of corresponding analysis workflows. Major advantages of WMSs are the easy reproducibility of results as well as the reusability of workflows or their components., Results: In this article, we present Watchdog, a WMS for the automated analysis of large-scale experimental data. Main features include straightforward processing of replicate data, support for distributed computer systems, customizable error detection and manual intervention into workflow execution. Watchdog is implemented in Java and thus platform-independent and allows easy sharing of workflows and corresponding program modules. It provides a graphical user interface (GUI) for workflow construction using pre-defined modules as well as a helper script for creating new module definitions. Execution of workflows is possible using either the GUI or a command-line interface and a web-interface is provided for monitoring the execution status and intervening in case of errors. To illustrate its potentials on a real-life example, a comprehensive workflow and modules for the analysis of RNA-seq experiments were implemented and are provided with the software in addition to simple test examples., Conclusions: Watchdog is a powerful and flexible WMS for the analysis of large-scale high-throughput experiments. We believe it will greatly benefit both users with and without programming skills who want to develop and apply bioinformatical workflows with reasonable overhead. The software, example workflows and a comprehensive documentation are freely available at www.bio.ifi.lmu.de/watchdog.

High-throughput methods for screening of physical and functional interactions now provide the means to study virus-host interactions on a genome scale. The limited coverage of these methods and the large size and uncertain quality of the identified interaction sets, however, require sophisticated computational approaches to obtain novel insights and hypotheses on virus infection processes from these interactions. Here, we describe the central steps of bioinformatics methods applied most commonly for this task and highlight important aspects that need to be considered and potential pitfalls that should be avoided.

Unlabelled: Recent improvements in experimental technologies now allow measurements of de novo transcription and/or RNA decay at whole transcriptome level and determination of precise transcript half-lives. Such transcript half-lives provide important insights into the regulation of biological processes and the relative contributions of RNA decay and de novo transcription to differential gene expression. In this article, we present HALO (Half-life Organizer), the first software for the precise determination of transcript half-lives from measurements of RNA de novo transcription or decay determined with microarrays or RNA-seq. In addition, methods for quality control, filtering and normalization are supplied. HALO provides a graphical user interface, command-line tools and a well-documented Java application programming interface (API). Thus, it can be used both by biologists to determine transcript half-lives fast and reliably with the provided user interfaces as well as software developers integrating transcript half-life analysis into other gene expression profiling pipelines., Availability: Source code, executables and documentation are available at http://www.bio.ifi.lmu.de/software/halo.

Motivation: Recent advances in high-throughput technologies have made it possible to investigate not only individual protein interactions, but also the association of these proteins in complexes. So far the focus has been on the prediction of complexes as sets of proteins from the experimental results. The modular substructure and the physical interactions within the protein complexes have been mostly ignored., Results: We present an approach for identifying the direct physical interactions and the subcomponent structure of protein complexes predicted from affinity purification assays. Our algorithm calculates the union of all maximum spanning trees from scoring networks for each protein complex to extract relevant interactions. In a subsequent step this network is extended to interactions which are not accounted for by alternative indirect paths. We show that the interactions identified with this approach are more accurate in predicting experimentally derived physical interactions than baseline approaches. Based on these networks, the subcomponent structure of the complexes can be resolved more satisfactorily and subcomplexes can be identified. The usefulness of our method is illustrated on the RNA polymerases for which the modular substructure can be successfully reconstructed., Availability: A Java implementation of the prediction methods and supplementary material are available at http://www.bio.ifi.lmu.de/Complexes/Substructures/.

Motivation To date, no methods are available for the targeted identification of genomic subregions with differences in sequencing read distributions between two conditions. Existing approaches either only determine absolute read number changes, require predefined subdivisions of input windows or average across multiple genes. Results Here, we present RegCFinder, which automatically identifies subregions of input windows with differences in read density between two conditions. For this purpose, the problem is defined as an instance of the all maximum scoring subsequences problem, which can be solved in linear time. Subsequently, statistical significance and differential usage of identified subregions are determined with DEXSeq. RegCFinder allows flexible definition of input windows to target the analysis to any regions of interests, e.g. promoters, gene bodies, peak regions and more. Furthermore, any type of sequencing assay can be used as input; thus, RegCFinder lends itself to a wide range of applications. We illustrate the usefulness of RegCFinder on two applications, where we can both confirm previous results and identify interesting gene subgroups with distinctive changes in read distributions. Availability and implementation RegCFinder is implemented as a workflow for the workflow management system Watchdog and available at: https://github.com/watchdog-wms/watchdog-wms-workflows/ Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

RNA-seq reads containing part of the poly(A) tail of transcripts (denoted as poly(A) reads) provide the most direct evidence for the position of poly(A) sites in the genome. However, due to reduced coverage of poly(A) tails by reads, poly(A) reads are not routinely identified during RNA-seq mapping. Nevertheless, recent studies for several herpesviruses successfully employed mapping of poly(A) reads to identify herpesvirus poly(A) sites using different strategies and customized programs. To more easily allow such analyses without requiring additional programs, we integrated poly(A) read mapping and prediction of poly(A) sites into our RNA-seq mapping program ContextMap 2. The implemented approach essentially generalizes previously used poly(A) read mapping approaches and combines them with the context-based approach of ContextMap 2 to take into account information provided by other reads aligned to the same location. Poly(A) read mapping using ContextMap 2 was evaluated on real-life data from the ENCODE project and compared against a competing approach based on transcriptome assembly (KLEAT). This showed high positive predictive value for our approach, evidenced also by the presence of poly(A) signals, and considerably lower runtime than KLEAT. Although sensitivity is low for both methods, we show that this is in part due to a high extent of spurious results in the gold standard set derived from RNA-PET data. Sensitivity improves for poly(A) sites of known transcripts or determined with a more specific poly(A) sequencing protocol and increases with read coverage on transcript ends. Finally, we illustrate the usefulness of the approach in a high read coverage scenario by a re-analysis of published data for herpes simplex virus 1. Thus, with current trends towards increasing sequencing depth and read length, poly(A) read mapping will prove to be increasingly useful and can now be performed automatically during RNA-seq mapping with ContextMap 2. [ABSTRACT FROM AUTHOR]

Novel high-throughput technologies such as yeast two-hybrid and RNA interference (RNAi) screens provide the tools to study interactions between viral proteins and the host on a genomic scale. In this review, we provide an overview of studies in which these technologies were applied and of computational approaches for the analysis of the identified viral interactors in the context of the host cell. The results of these studies illustrate the advantages of integrative systems biology approaches in the investigation of viral pathogens. [ABSTRACT FROM AUTHOR]