A holistic understanding of environmental communities is the new challenge of metagenomics. Accordingly, the amplicon-based or metabarcoding approach, largely applied to investigate bacterial microbiomes, is moving to the eukaryotic world too. Indeed, the analysis of metabarcoding data may provide a comprehensive assessment of both bacterial and eukaryotic composition in a variety of environments, including human body. In this respect, whereas hypervariable regions of the 16S rRNA are the de facto standard barcode for bacteria, the Internal Transcribed Spacer 1 (ITS1) of ribosomal RNA gene cluster has shown a high potential in discriminating eukaryotes at deep taxonomic levels. As metabarcoding data analysis rely on the availability of a well-curated barcode reference resource, a comprehensive collection of ITS1 sequences supplied with robust taxonomies, is highly needed. To address this issue, we created ITSoneDB (available at http://itsonedb.cloud.ba.infn.it/) which in its current version hosts 985 240 ITS1 sequences spanning over 134 000 eukaryotic species. Each ITS1 is mapped on the NCBI reference taxonomy with its start and end positions precisely annotated. ITSoneDB has been developed in agreement to the FAIR guidelines by enabling the users to query and download its content through a simple web-interface and access relevant metadata by cross-linking to European Nucleotide Archive.

Background: Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as "Web services") and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust "in silico" science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. Results: BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on- line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible 'virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. Conclusions: Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.

Background: Recent years have seen a surge in projects that produce large volumes of structured, machine-readable biodiversity data. To make these data amenable to processing by generic, open source “data enrichment” workflows, they are increasingly being represented in a variety of standards-compliant interchange formats. Here, we report on an initiative in which software developers and taxonomists came together to address the challenges and highlight the opportunities in the enrichment of such biodiversity data by engaging in intensive, collaborative software development: The Biodiversity Data Enrichment Hackathon. Results: The hackathon brought together 37 participants (including developers and taxonomists, i.e. scientific professionals that gather, identify, name and classify species) from 10 countries: Belgium, Bulgaria, Canada, Finland, Germany, Italy, the Netherlands, New Zealand, the UK, and the US. The participants brought expertise in processing structured data, text mining, development of ontologies, digital identification keys, geographic information systems, niche modeling, natural language processing, provenance annotation, semantic integration, taxonomic name resolution, web service interfaces, workflow tools and visualisation. Most use cases and exemplar data were provided by taxonomists. One goal of the meeting was to facilitate re-use and enhancement of biodiversity knowledge by a broad range of stakeholders, such as taxonomists, systematists, ecologists, niche modelers, informaticians and ontologists. The suggested use cases resulted in nine breakout groups addressing three main themes: i) mobilising heritage biodiversity knowledge; ii) formalising and linking concepts; and iii) addressing interoperability between service platforms. Another goal was to further foster a community of experts in biodiversity informatics and to build human links between research projects and institutions, in response to recent calls to further such integration in this research domain. Conclusions: Beyond deriving prototype solutions for each use case, areas of inadequacy were discussed and are being pursued further. It was striking how many possible applications for biodiversity data there were and how quickly solutions could be put together when the normal constraints to collaboration were broken down for a week. Conversely, mobilising biodiversity knowledge from their silos in heritage literature and natural history collections will continue to require formalisation of the concepts (and the links between them) that define the research domain, as well as increased interoperability between the software platforms that operate on these concepts.

Nucleotide sequences reference collections or databases are fundamental components in DNA barcoding and metabarcoding data analyses pipelines. In such analyses, the accurate taxonomic assignment is a crucial aspect, relying directly on the availability of comprehensive and curated reference sequence collection and its taxonomy information. The currently wide use of the mitochondrial cytochrome oxidase subunit-I (COXI) as a standard DNA barcode marker in metazoan biodiversity studies highlights the need to shed light on the availability of the related relevant information from different data sources and their eventual integration. To adequately address data integration process, many aspects should be markedly considered starting from DNA sequence curation followed by taxonomy alignment with solid reference backbone and metadata harmonization according to universal standards. Here, we present MetaCOXI, an integrated collection of curated metazoan COXI DNA sequences with their associated harmonized taxonomy and metadata. This collection was built on the two most extensive available data resources, namely the European Nucleotide Archive (ENA) and the Barcode of Life Data System (BOLD). The current release contains more than 5.6 million entries (39.1% unique to BOLD, 3.6% unique to ENA, and 57.2% shared between both), their related taxonomic classification based on NCBI reference taxonomy, and their available main metadata relevant to environmental DNA studies, such as geographical coordinates, sampling country and host species. MetaCOXI is available in standard universal formats (‘fasta’ for sequences & ‘tsv’ for taxonomy and metadata), which can be easily incorporated in standard or specific DNA barcoding and/or metabarcoding data analysis pipelines. Database URL: https://github.com/bachob5/MetaCOXI

Polyploidization as the consequence of 2n gamete formation is a prominent mechanism in plant evolution. Studying its effects on the genome and its expression has both basic and applied interest. We crossed two diploid (2n=2x=16) M. sativa plants, a subsp. Falcate seed parent and a coerulea x falcate pollen parent that form a mixture of n and 2n eggs and pollen, respectively. Such cross produced full-sib diploid and tetraploid (2n=4x=32) hybrids, the latter being the result of bilateral sexual polyploidization (BSP). These unique materials allowed us to investigate the effects of BSP, and separating the effect of intraspecific hybridization from those of polyploidization by comparing 2x with 4x full sib progeny plants. SSR marker segregation demonstrated tetrasomic inheritance for all chromosomes but one, demonstrating that these neotetraploids are true autotetraploids. BSP brought about increased biomass, earlier flowering, higher seed set and weight, larger leaves with larger cells. Microarray analyses with M. truncatula gene chips showed that several hundred genes, related to diverse metabolic functions, changed their expression level as a consequence of polyploidization. In addition, cytosine methylation increased in 2x but not in 4x hybrids. Our results indicate that sexual polyploidization induce significant transcriptional novelty, possibly mediated in part by DNA methylation, and phenotypic novelty that can be at the base of improved adaptation and reproductive success of tetraploid M. sativa with respect to its diploid progenitor. These polyploidy-induced changes may have promoted the adoption of tetraploid alfalfa in agriculture.