Computational modelling has become increasingly common in life science research. To provide a platform to support universal sharing, easy accessibility and model reproducibility, BioModels (https://www.ebi.ac.uk/biomodels/), a repository for mathematical models, was established in 2005. The current BioModels platform allows submission of models encoded in diverse modelling formats, including SBML, CellML, PharmML, COMBINE archive, MATLAB, Mathematica, R, Python or C++. The models submitted to BioModels are curated to verify the computational representation of the biological process and the reproducibility of the simulation results in the reference publication. The curation also involves encoding models in standard formats and annotation with controlled vocabularies following MIRIAM (minimal information required in the annotation of biochemical models) guidelines. BioModels now accepts large-scale submission of auto-generated computational models. With gradual growth in content over 15 years, BioModels currently hosts about 2000 models from the published literature. With about 800 curated models, BioModels has become the world’s largest repository of curated models and emerged as the third most used data resource after PubMed and Google Scholar among the scientists who use modelling in their research. Thus, BioModels benefits modellers by providing access to reliable and semantically enriched curated models in standard formats that are easy to share, reproduce and reuse.

Two pairs of degenerate primers were designed from sequences within the potyviral CI (CIFor/CIRev) and HC-Pro-coding regions (HPFo/HPRev), and these were shown to be highly specific to members of the genus Potyvirus. Using the CIFor and CIRev primers, three novel potyviruses infecting crop and weed species from Vietnam were detected, namely telosma mosaic virus (TelMV) infecting telosma (Telosma cordata, Asclepiadaceae), peace lily mosaic virus (PeLMV) infecting peace lily (Spathiphyllum patinii, Araceae) and wild tomato mosaic virus (WTMV) infecting wild tomato (Solanum torvum, Solanaceae). The fragments amplified by the two sets of primers enabled additional PCR and complete genomic sequencing of these viruses and a banana bract mosaic virus (BBrMV) isolate from the Philippines. All four viruses shared genomic features typical of potyviruses. Sequence comparisons and phylogenetic analyses indicated that WTMV was most closely related to chilli veinal mottle virus (ChiVMV) and pepper veinal mottle virus (PVMV), while PeLMV, TelMV and BBrMV were related to different extents to members of the bean common mosaic virus (BCMV) subgroup.

Sixteen viruses, belonging to 16 species of begomovirus, that infect crops and weeds in Vietnam were identified. Sequence analysis of the complete genomes showed that nine of the viruses (six monopartite and three bipartite) belong to novel species and five of them were identified in Vietnam for the first time. Additionally, eight DNA-β and three nanovirus-like DNA-1 molecules were also found associated with some of the monopartite viruses. Five of the DNA-β molecules were novel. Importantly, a second bipartite begomovirus, Corchorus golden mosaic virus, shared several features with the previously characterized virus Corchorus yellow vein virus and with other bipartite begomoviruses from the New World, supporting the hypothesis that New World-like viruses were present in the Old World. This, together with a high degree of virus diversity that included putative recombinant viruses, satellite molecules and viruses with previously undescribed variability in the putative stem–loop sequences, suggested that South-East Asia, and Vietnam in particular, is one of the origins of begomovirus diversity.

The amount of omics data in the public domain is increasing every year. Modern science has become a data-intensive discipline. Innovative solutions for data management, data sharing, and for discovering novel datasets are therefore increasingly required. In 2016, we released the first version of the Omics Discovery Index (OmicsDI) as a light-weight system to aggregate datasets across multiple public omics data resources. OmicsDI aggregates genomics, transcriptomics, proteomics, metabolomics and multiomics datasets, as well as computational models of biological processes. Here, we propose a set of novel metrics to quantify the attention and impact of biomedical datasets. A complete framework (now integrated into OmicsDI) has been implemented in order to provide and evaluate those metrics. Finally, we propose a set of recommendations for authors, journals and data resources to promote an optimal quantification of the impact of datasets.