The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics.

ABSTRACT: A global genome database of all of Earth’s species diversity could be a treasure trove of scientific discoveries. However, regardless of the major advances in genome sequencing technologies, only a tiny fraction of species have genomic information available. To contribute to a more complete planetary genomic database, scientists and institutions across the world have united under the Earth BioGenome Project (EBP), which plans to sequence and assemble high-quality reference genomes for all ∼1.5 million recognized eukaryotic species through a stepwise phased approach. As the initiative transitions into Phase II, where 150,000 species are to be sequenced in just four years, worldwide participation in the project will be fundamental to success. As the European node of the EBP, the European Reference Genome Atlas (ERGA) seeks to implement a new decentralised, accessible, equitable and inclusive model for producing high-quality reference genomes, which will inform EBP as it scales. To embark on this mission, ERGA launched a Pilot Project to establish a network across Europe to develop and test the first infrastructure of its kind for the coordinated and distributed reference genome production on 98 European eukaryotic species from sample providers across 33 European countries. Here we outline the process and challenges faced during the development of a pilot infrastructure for the production of reference genome resources, and explore the effectiveness of this approach in terms of high-quality reference genome production, considering also equity and inclusion. The outcomes and lessons learned during this pilot provide a solid foundation for ERGA while offering key learnings to other transnational and national genomic resource projects.info:eu-repo/semantics/publishedVersio

231 TOXICITY VS POTENCY: ELUCIDATION OF TOXICITY PROPERTIES DISCRIMINATING BETWEEN TOXINS, DRUGS, AND NATURAL COMPOUNDS SWANTJE STRUCK 1*

Within our everyday life we are confronted with a variety of toxic substances. A number of these compounds are already used as lead structures for the development of new drugs, but the amount of toxic substances is still a rich resource of new bioactive compounds. During the identification and development of new potential drugs, risk estimation of health hazards is an essential and topical subject in pharmaceutical industry. To face this challenge, an extensive investigation of known toxic compounds is going to be helpful to estimate the toxicity of potential drugs. “Toxicity properties” found during those investigations will also function as a guideline for the toxicological classification of other unknown substances. We have compiled a dataset of approximately 50,000 toxic compounds from literature and web sources. All compounds were classified according to their toxicity. During this study the collection of toxic compounds was investigated extensively regarding their chemical, functional, and structural properties and compaired with a dataset of drugs and natural compounds. We were able to identify differences in properties within the toxic compounds as well as in comparison to drugs and natural compounds. These properties include molecular weight, hydrogen bond donors and acceptors, and functional groups which can be regarded as “toxicity properties”, i.e. attributes defining toxicity

Survey of metaproteomics software tools for functional microbiome analysis

To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.Peer reviewe

Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy

Abstract Background Protein–protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein–protein interactions and produce high-quality multimeric structural models. Results Application of our method to the Human and Yeast genomes yield protein–protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2’s non-structural protein 3. We also produced models of SARS-CoV2’s spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4. Conclusions The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu

Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework

The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms. Characterizing proteins expressed by a complex microbiome and interpreting their functional signature requires sophisticated informatics tools and workflows tailored to metaproteomics. Additionally, there is a need to disseminate these informatics resources to researchers undertaking metaproteomic studies, who could use them to make new and important discoveries in microbiome research. The Galaxy for proteomics platform (Galaxy-P) offers an open source, web-based bioinformatics platform for disseminating metaproteomics software and workflows. Within this platform, we have developed easily-accessible and documented metaproteomic software tools and workflows aimed at training researchers in their operation and disseminating the tools for more widespread use. The modular workflows encompass the core requirements of metaproteomic informatics: (a) database generation; (b) peptide spectral matching; (c) taxonomic analysis and (d) functional analysis. Much of the software available via the Galaxy-P platform was selected, packaged and deployed through an online metaproteomics “Contribution Fest“ undertaken by a unique consortium of expert software developers and users from the metaproteomics research community, who have co-authored this manuscript. These resources are documented on GitHub and freely available through the Galaxy Toolshed, as well as a publicly accessible metaproteomics gateway Galaxy instance. These documented workflows are well suited for the training of novice metaproteomics researchers, through online resources such as the Galaxy Training Network, as well as hands-on training workshops. Here, we describe the metaproteomics tools available within these Galaxy-based resources, as well as the process by which they were selected and implemented in our community-based work. We hope this description will increase access to and utilization of metaproteomics tools, as well as offer a framework for continued community-based development and dissemination of cutting edge metaproteomics software.Peer reviewe

Impact of angiogenic activation and inhibition on miRNA profiles of human retinal endothelial cells

Background: Human retinal microvascular endothelial cells (HRMVECs) are involved in the pathogenesis of retinopathy of prematurity. In this study, the microRNA (miRNA) expression profiles of HRMVECs were investigated under resting conditions, angiogenic stimulation (VEGF treatment) and anti-VEGF treatment. Materials and methods: The miRNA profiles of HRMVECs under resting and angiogenic conditions (VEGF treatment), as well as after addition of aflibercept, bevacizumab or ranibizumab were evaluated by analyzing the transcriptome of small non-coding RNAs. Differentially expressed miRNAs were validated using qPCR and classified using Gene Ontology enrichment analysis. Results: Ten miRNAs were found to be significantly changed more than 2-fold. Seven of these miRNAs were changed between resting conditions and angiogenic stimulation. Four of these miRNAs (miR-139-5p/-3p and miR-335-5p/-3p) were validated by qPCR in independent experiments and were found to be associated with angiogenesis and cell migration in Gene Ontology analysis. In addition, analysis of the most abundant miRNAs in the HRMVEC miRNome (representing at least 1% of the miRNome) was conducted and identified miR-21-5p, miR-29a.3p, miR.100-5p and miR-126-5p/-3p to be differently expressed by at least 15% between resting conditions and angiogenic conditions. These miRNAs were found to be associated with apoptotic signaling, regulation of kinase activity, intracellular signal transduction, cell surface receptor signaling and positive regulation of cell differentiation in Gene Ontology analysis. No differentially regulated miRNAs between angiogenic stimulation and angiogenic stimulation plus anti-VEGF treatment were identified. Conclusion: In this study we characterized the miRNA profile of HRMVECs under resting, angiogenic and antiangiogenic conditions and identified several miRNAs of potential pathophysiologic importance for angioproli-ferative retinal diseases. Our results have implications for possible miRNA-targeted angiomodulatory approaches in diseases like diabetic retinopathy or retinopathy of prematurity

The bio.tools registry of software tools and data resources for the life sciences

Bioinformaticians and biologists rely increasingly upon workflows for the flexible utilization of the many life science tools that are needed to optimally convert data into knowledge. We outline a pan-European enterprise to provide a catalogue () of tools and databases that can be used in these workflows. bio.tools not only lists where to find resources, but also provides a wide variety of practical information