Xeml Lab: a tool that supports the design of experiments at a graphical interface and generates computer-readable metadata files, which capture information about genotypes, growth conditions, environmental perturbations and sampling strategy

Data mining depends on the ability to access machine-readable metadata that describe genotypes, environmental conditions, and sampling times and strategy. This article presents Xeml Lab. The Xeml Interactive Designer provides an interactive graphical interface at which complex experiments can be designed, and concomitantly generates machine-readable metadata files. It uses a new eXtensible Mark-up Language (XML)-derived dialect termed XEML. Xeml Lab includes a new ontology for environmental conditions, called Xeml Environment Ontology. However, to provide versatility, it is designed to be generic and also accepts other commonly used ontology formats, including OBO and OWL. A review summarizing important environmental conditions that need to be controlled, monitored and captured as metadata is posted in a Wiki (http://www.codeplex.com/ XeO) to promote community discussion. The usefulness of Xeml Lab is illustrated by two meta-analyses of a large set of experiments that were performed with Arabidopsis thaliana during 5 years. The first reveals sources of noise that affect measurements of metabolite levels and enzyme activities. The second shows that Arabidopsis maintains remarkably stable levels of sugars and amino acids across a wide range of photoperiod treatments, and that adjustment of starch turnover and the leaf protein content contribute to this metabolic homeostasis

Understanding protein import in diverse non-green plastids

The spectacular diversity of plastids in non-green organs such as flowers, fruits, roots, tubers, and senescing leaves represents a Universe of metabolic processes in higher plants that remain to be completely characterized. The endosymbiosis of the plastid and the subsequent export of the ancestral cyanobacterial genome to the nuclear genome, and adaptation of the plants to all types of environments has resulted in the emergence of diverse and a highly orchestrated metabolism across the plant kingdom that is entirely reliant on a complex protein import and translocation system. The TOC and TIC translocons, critical for importing nuclear-encoded proteins into the plastid stroma, remain poorly resolved, especially in the case of TIC. From the stroma, three core pathways (cpTat, cpSec, and cpSRP) may localize imported proteins to the thylakoid. Non-canonical routes only utilizing TOC also exist for the insertion of many inner and outer membrane proteins, or in the case of some modified proteins, a vesicular import route. Understanding this complex protein import system is further compounded by the highly heterogeneous nature of transit peptides, and the varying transit peptide specificity of plastids depending on species and the developmental and trophic stage of the plant organs. Computational tools provide an increasingly sophisticated means of predicting protein import into highly diverse non-green plastids across higher plants, which need to be validated using proteomics and metabolic approaches. The myriad plastid functions enable higher plants to interact and respond to all kinds of environments. Unraveling the diversity of non-green plastid functions across the higher plants has the potential to provide knowledge that will help in developing climate resilient crops

Mercator: A fast and simple web server for genome scale functional annotation of plant sequence data

Next-generation technologies generate an overwhelming amount of gene sequence data. Efficient annotation tools are required, to make this data amenable to functional genomics analyses. The Mercator pipeline automatically assigns functional terms to protein or nucleotide sequences. It uses the MapMan “BIN” ontology, which is tailored for functional annotation of plant “omics” data. The classification procedure performs parallel sequence searches against reference databases, compiles the results, and computes the most likely MapMan BINs for each query. In the current version, the pipeline relies on manually curated reference classifications originating from the three reference organisms (Arabidopsis, Chlamydomonas, rice), various other plant species that have a reviewed SwissProt annotation, and more than 2000 protein domain and family profiles at InterPro, CDD and KOG. Functional annotations predicted by Mercator achieve accuracies above 90% when benchmarked against manual annotation. In addition to mapping files for direct use in the visualization software MapMan, Mercator provides graphical overview charts, detailed annotation information in a convenient web browser interface and a MapMan-to-GO translation Table to export results as GO terms. Mercator is available free of charge via http://mapman.gabipd.org/web/guest/app/Mercator

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

Wibberg D, Batut B, Belmann P, et al. The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR. F1000Research. 2019;8: 1877 .The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) 'Training & Education', coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics. Copyright: © 2020 Wibberg D et al

Co-expression tools for plant biology : opportunities for hypothesis generation and caveats

Gene co‐expression analysis has emerged in the past 5 years as a powerful tool for gene function prediction. In essence, co‐expression analysis asks the question ‘what are the genes that are co‐expressed, that is, those that show similar expression profiles across many experiments, with my gene of interest?’. Genes that are highly co‐expressed may be involved in the biological process or processes of the query gene. This review describes the tools that are available for performing such analyses, how each of these perform, and also discusses statistical issues including how normalization of gene expression data can influence co‐expression results, calculation of co‐expression scores and P values, and the influence of data sets used for co‐expression analysis. Finally, examples from the literature will be presented, wherein co‐expression has been used to corroborate and discover various aspects of plant biology

Network Analysis of Enzyme Activities and Metabolite Levels and Their Relationship to Biomass in a Large Panel of Arabidopsis Accessions[C][W][OA]

This work uses natural genetic diversity to study species-wide connectivity between metabolites, enzymes, and biomass. The resulting network analysis, based on 129 Arabidopsis accessions, shows that biomass can be predicted by two independent integrative metabolic biomarkers: preferential investment in photosynthetic machinery and optimization of carbon use

Identification of RNA-binding proteins in macrophages by interactome capture

Pathogen components, such as lipopolysaccharides of Gram-negative bacteria that activate Toll-like receptor 4, induce mitogen activated protein kinases and NFκB through different downstream pathways to stimulate pro- and anti-inflammatory cytokine expression. Importantly, post-transcriptional control of the expression of Toll-like receptor 4 downstream signaling molecules contributes to the tight regulation of inflammatory cytokine synthesis in macrophages. Emerging evidence highlights the role of RNA-binding proteins (RBPs) in the post-transcriptional control of the innate immune response. To systematically identify macrophage RBPs and their response to LPS stimulation, we employed RNA interactome capture in LPS-induced and untreated murine RAW 264.7 macrophages. This combines RBP-crosslinking to RNA, cell lysis, oligo(dT) capture of polyadenylated RNAs and mass spectrometry analysis of associated proteins. Our data revealed 402 proteins of the macrophage RNA interactome including 91 previously not annotated as RBPs. A comparison with published RNA interactomes classified 32 RBPs uniquely identified in RAW 264.7 macrophages. Of these, 19 proteins are linked to biochemical activities not directly related to RNA. From this group, we validated the HSP90 cochaperone P23 that was demonstrated to exhibit cytosolic prostaglandin E2 synthase 3 (PTGES3) activity, and the hematopoietic cell-specific LYN substrate 1 (HCLS1 or HS1), a hematopoietic cell-specific adapter molecule, as novel macrophage RBPs. Our study expands the mammalian RBP repertoire, and identifies macrophage RBPs that respond to LPS. These RBPs are prime candidates for the post-transcriptional regulation and execution of LPS-induced signaling pathways and the innate immune response. Macrophage RBP data have been deposited to ProteomeXchange with identifier PXD002890.This work was supported by a grant from the Deutsche Forschungsgemeinschaft (DFG) (OS 290/6–1) to A.O.-L.; and in part by grants from the DFG to I.S.N.-d.V. (NA 1273/1-1) and from the National Health and Medical Research Council of Australia (#1045417) to T.P. and M.W.H. Furthermore, M.W.H. acknowledges support by the ERC Advanced Grant ERC-2011-ADG_20110310

Towards smart and sustainable development of modern berry cultivars in Europe

International audienceFresh berries are a popular and important component of the human diet. The demand for high-quality berries and sustainable production methods is increasing globally, challenging breeders to develop modern berry cultivars that fulfill all desired characteristics. Since 1994, research projects have characterized genetic resources, developed modern tools for high-throughput screening, and published data in publicly available repositories. However, the key findings of different disciplines are rarely linked together and only a limited range of traits and genotypes has been investigated. The Horizon2020 project BreedingValue will address these challenges by studying a broader panel of strawberry, raspberry and blueberry genotypes in detail, in order to recover the lost genetic diversity that has limited the aroma and flavor intensity of recent cultivars. We will combine metabolic analysis with sensory panel tests and surveys to identify the key components of taste, flavor and aroma in berries across Europe, leading to a high-resolution map of quality requirements for future berry cultivars. Traits linked to berry yields and the effect of environmental stress will be investigated using modern image analysis methods and modeling. We will also use genetic analysis to determine the genetic basis of complex traits for the development and optimization of modern breeding technologies such as molecular marker arrays, genomic selection and genome wide association studies. Finally, the results, raw data and metadata will be made publicly available on the open platform Germinate in order to meet FAIR data principles and provide the basis for sustainable research in the future

EOSC-Life Report on the work of the initial demonstrators

Leitner F, Carazo JM, Bischof J, et al. EOSC-Life Report on the work of the initial demonstrators.This deliverable 3.2 is a report on the demonstrator projects, the eight scientific and technical pilot projects that were selected to provide concrete scientific use-cases and guide and structure the work done in EOSC-Life to build an open digital and collaborative space for biological and medical research. We report in this deliverable the process of integration of the demonstrators within EOSC-Life, the achievement of the demonstrators who developed and made available to the scientific community several valuable resources (databases, workflows, web platform...), the actions undertaken within EOSC-Life to disseminate the demonstrator achievement and finally the results of the demonstrator survey to learn from the demonstrator experience and improve the integration of the new pilot project within EOSC-Life. </ol