BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree.
ABSTRACT: The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.
Project description:Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use.We describe BiGG, a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.BiGG addresses a need in the systems biology community to have access to high quality curated metabolic models and reconstructions. It is freely available for academic use at http://bigg.ucsd.edu.
Project description:Essential reactions are vital components of cellular networks. They are the foundations of synthetic biology and are potential candidate targets for antimetabolic drug design. Especially if a single reaction is catalyzed by multiple enzymes, then inhibiting the reaction would be a better option than targeting the enzymes or the corresponding enzyme-encoding gene. The existing databases such as BRENDA, BiGG, KEGG, Bio-models, Biosilico, and many others offer useful and comprehensive information on biochemical reactions. But none of these databases especially focus on essential reactions. Therefore, building a centralized repository for this class of reactions would be of great value.Here, we present a species-specific essential reactions database (SSER). The current version comprises essential biochemical and transport reactions of twenty-six organisms which are identified via flux balance analysis (FBA) combined with manual curation on experimentally validated metabolic network models. Quantitative data on the number of essential reactions, number of the essential reactions associated with their respective enzyme-encoding genes and shared essential reactions across organisms are the main contents of the database.SSER would be a prime source to obtain essential reactions data and related gene and metabolite information and it can significantly facilitate the metabolic network models reconstruction and analysis, and drug target discovery studies. Users can browse, search, compare and download the essential reactions of organisms of their interest through the website http://cefg.uestc.edu.cn/sser .
Project description:Risk prediction models can assist clinicians in making decisions. To boost the uptake of these models in clinical practice, it is important that end-users understand how the model works and can efficiently communicate its results. We introduce novel methods for interpretable model visualization.The proposed visualization techniques are applied to two prediction models from the Framingham Heart Study for the prediction of intermittent claudication and stroke after atrial fibrillation. We represent models using color bars, and visualize the risk estimation process for a specific patient using patient-specific contribution charts.The color-based model representations provide users with an attractive tool to instantly gauge the relative importance of the predictors. The patient-specific representations allow users to understand the relative contribution of each predictor to the patient's estimated risk, potentially providing insightful information on which to base further patient management. Extensions towards non-linear models and interactions are illustrated on an artificial dataset.The proposed methods summarize risk prediction models and risk predictions for specific patients in an alternative way. These representations may facilitate communication between clinicians and patients.
Project description:Barley (Hordeum vulgare) is one of the world's most important cereal crops. Although its large and complex genome has held back barley genomics for quite a while, the whole genome sequence was released in 2012 by the International Barley Genome Sequencing Consortium (IBSC). Moreover, more than 30,000 barley full-length cDNAs (FLcDNAs) are now available in the public domain. Here we present the Barley Gene Expression Database (bex-db: http://barleyflc.dna.affrc.go.jp/bexdb/index.html) as a repository of transcriptome data including the sequences and the expression profiles of barley genes resulting from microarray analysis. In addition to FLcDNA sequences, bex-db also contains partial sequences of more than 309,000 novel expressed sequence tags (ESTs). Users can browse the data via keyword, sequence homology and expression profile search options. A genome browser was also developed to display the chromosomal locations of barley FLcDNAs and wheat (Triticum aestivum) transcripts as well as Aegilops tauschii gene models on the IBSC genome sequence for future comparative analysis of orthologs among Triticeae species. The bex-db should provide a useful resource for further genomics studies and development of genome-based tools to enhance the progress of the genetic improvement of cereal crops.
Project description:BACKGROUND:Respiratory syncytial virus (RSV) infection is the second most important cause of death in the first year of life, and early RSV infections are associated with the development of asthma. Breastfeeding and serum IgG have been shown to protect against RSV infection. Yet, many infants depend on bovine milk-based nutrition, which at present lacks intact immunoglobulins. OBJECTIVE:To investigate whether IgG purified from bovine milk (bIgG) can modulate immune responses against human RSV. METHODS:ELISAs were performed to analyse binding of bIgG to human respiratory pathogens. bIgG or hRSV was coated to plates to assess dose-dependent binding of bIgG to human Fc? receptors (Fc?R) or bIgG-mediated binding of myeloid cells to hRSV respectively. S. Epidermidis and RSV were used to test bIgG-mediated binding and internalisation of pathogens by myeloid cells. Finally, the ability of bIgG to neutralise infection of HEp2 cells by hRSV was evaluated. RESULTS:bIgG recognised human RSV, influenza haemagglutinin and Haemophilus influenza. bIgG bound to Fc?RII on neutrophils, monocytes and macrophages, but not to Fc?RI and Fc?RIII, and could bind simultaneously to hRSV and human Fc?RII on neutrophils. In addition, human neutrophils and dendritic cells internalised pathogens that were opsonised with bIgG. Finally, bIgG could prevent infection of HEp2 cells by hRSV. CONCLUSIONS:The data presented here show that bIgG binds to hRSV and other human respiratory pathogens and induces effector functions through binding to human Fc?RII on phagocytes. Thus bovine IgG may contribute to immune protection against RSV.
Project description:<h4>Background</h4>Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem.<h4>Results</h4>Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches.<h4>Conclusion</h4>Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.
Project description:Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models' structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried. Database URL: https://sems.uni-rostock.de/projects/masymos/
Project description:Eukaryotic DNA methylation has been receiving increasing attention for its crucial epigenetic regulatory function. The recently developed single-molecule real-time (SMRT) sequencing technology provides an efficient way to detect DNA N6-methyladenine (6mA) and N4-methylcytosine (4mC) modifications at a single-nucleotide resolution. The family Rosaceae contains horticultural plants with a wide range of economic importance. However, little is currently known regarding the genome-wide distribution patterns and functions of 6mA and 4mC modifications in the Rosaceae. In this study, we present an integrated DNA 6mA and 4mC modification database for the Rosaceae (MDR, http://mdr.xieslab.org). MDR, the first repository for displaying and storing DNA 6mA and 4mC methylomes from SMRT sequencing data sets for Rosaceae, includes meta and statistical information, methylation densities, Gene Ontology enrichment analyses, and genome search and browse for methylated sites in NCBI. MDR provides important information regarding DNA 6mA and 4mC methylation and may help users better understand epigenetic modifications in the family Rosaceae.
Project description:In addition to encoding RNA primary structures, genomes also encode RNA secondary and tertiary structures that play roles in gene regulation and, in the case of RNA viruses, genome replication. Methods for the identification of functional RNA structures in genomes typically rely on scanning analysis windows, where multiple partially-overlapping windows are used to predict RNA structures and folding metrics to deduce regions likely to form functional structure. Separate structural models are produced for each window, where the step size can greatly affect the returned model. This makes deducing unique local structures challenging, as the same nucleotides in each window can be alternatively base paired. We are presenting here a new approach where all base pairs from analysis windows are considered and weighted by favorable folding. This results in unique base pairing throughout the genome and the generation of local regions/structures that can be ranked by their propensity to form unusually thermodynamically stable folds. We applied this approach to the Zika virus (ZIKV) and HIV-1 genomes. ZIKV is linked to a variety of neurological ailments including microcephaly and Guillain-Barré syndrome and its (+)-sense RNA genome encodes two, previously described, functionally essential structured RNA regions. HIV, the cause of AIDS, contains multiple functional RNA motifs in its genome, which have been extensively studied. Our approach is able to successfully identify and model the structures of known functional motifs in both viruses, while also finding additional regions likely to form functional structures. All data have been archived at the RNAStructuromeDB (www.structurome.bb.iastate.edu), a repository of RNA folding data for humans and their pathogens.
Project description:Population modeling of tumor size dynamics has recently emerged as an important tool in pharmacometric research. A series of new mixed-effects models have been reported recently, and we present herein a synthetic view of models with published mathematical equations aimed at describing the dynamics of tumor size in cancer patients following anticancer drug treatment. This selection of models will constitute the basis for the Drug Disease Model Resources (DDMoRe) repository for models on oncology.