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Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-based vaccine design program using reverse vaccinology with machine learning. Vaxign2 is a comprehensive web server for rational vaccine design, consisting of predictive and computational workflow components. The predictive part includes the original Vaxign filtering-based method and a new machine learning-based method, Vaxign-ML. The benchmarking results using a validation dataset showed that Vaxign-ML had superior prediction performance compared to other RV tools. Besides the prediction component, Vaxign2 implemented various post-prediction analyses to significantly enhance users’ capability to refine the prediction results based on different vaccine design rationales and considerably reduce user time to analyze the Vaxign/Vaxign-ML prediction results. Users provide proteome sequences as input data, select candidates based on Vaxign outputs and Vaxign-ML scores, and perform post-prediction analysis. Vaxign2 also includes precomputed results from approximately 1 million proteins in 398 proteomes of 36 pathogens. As a demonstration, Vaxign2 was used to effectively analyse SARS-CoV-2, the coronavirus causing COVID-19. The comprehensive framework of Vaxign2 can support better and more rational vaccine design. Vaxign2 is publicly accessible at http://www.violinet.org/vaxign2., Graphical Abstract Graphical AbstractVaxign2 is the first comprehensive web server application for vaccine design using reverse vaccinology and machine learning, showing superior prediction performance compared to other similar tools. Vaxign2 consists of (i) protective antigen prediction from a user-provided protein(s) or proteome of the target pathogen and (ii) post-prediction analyses of protein functions and immunogenicity.

In this chapter, we have introduced in details the miniTUBA system, and how to apply the miniTUBA dynamic Bayesian network (DBN) approach to analyze a typical use case in the areas of host-pathogen interactions using high throughput microarray data. The DBNs are powerful to model the stochastic evoluation of a set of random variables over time. Since the biological processes and various measurement errors are stochastic in nature, DBN has been considered as a suitable technique to study biological networks and pathways. Bayesian networks (BNs) and DBNs are based on a multinomial distribution. This distribution is very flexible, and each node has a different parameterization. Therefore, it is very feasible to use DBNs to model the dynamics of biological systems and responses to parameter perturbations. However, although a few applications for both Bayesian network and DBNs to modeling gene expression data have been discussed and reported, their usefullness remains to be shown with more well-understood pathways (Xia, et al., 2004). Programmed cell death (i.e., apoptosis) pathways are well studied and important for all plant and animal organisms. We first demonstrated in this report how the DBN analysis can be used to predict crucial genes for a cell death pathway, which led to correct experimental verification. Two major challenges in DBN analysis for biological network modeling exist. First, continuous gene expression data has to be descretized, leading to the loss of information. The descretization simplifies the computation and stablizes the predicated results. However, current equal quantile and interval descretization methods do not often reflect the biological realities. The customized descretization method is too time consuming and may not correlate with the unknown truth either. Therefore, alternative approaches will need to be explored to improve the descretization and minimize loss of information. How to find reliable ways to model continuous data remains to be a major challenge in the DBN and other modeling studies. Second, it is a big challenge to identify the correct time steps (i.e., Markov lags) for a DBN modeling. By default, we require all variables have the same time step size. However, it might be possible to allow a mixture of different time step sizes. The time scale likely differ between variables. To identify the relevant time scale, we may allow different discretization schemes. While more finely discretized variables offer slower changes, it might be difficult to determine how many are appropriate. The generation of very large sizes of discretizations is also time consuming. One solution is to allow mixtures of time steps in the learning step. However, it is in practice very difficult because the current step depends on a range of past experiences. If the previous time steps are not multiplies of each other, a complex splining function is usually needed to dynamically interpolate the missing data. Alternatively, we can explicitly search for an optimum informative time step. A DBN search will favor small time steps because it means more data to be used. However, if the data represents only more interpolated data, it would not help. While DBN analysis can be improved in different directions, the two areas of DBN research with the largest impact are probably the discretization and correct time step setting. Besides addressing the above challenges, dynamic Bayesian networks can further be improved through different directions: (i) those strong links (or edges) are conserved among top networks and can be detected by consensus analysis (Fig. 4). (ii) cross-species

Ontologies are critical to data/metadata and knowledge standardization, sharing, and analysis. With hundreds of biological and biomedical ontologies developed, it has become critical to ensure ontology interoperability and the usage of interoperable ontologies for standardized data representation and integration. The suite of web-based Ontoanimal tools (e.g., Ontofox, Ontorat, and Ontobee) support different aspects of extensible ontology development. By summarizing the common features of Ontoanimal and other similar tools, we identified and proposed an “eXtensible Ontology Development” (XOD) strategy and its associated four principles. These XOD principles reuse existing terms and semantic relations from reliable ontologies, develop and apply well-established ontology design patterns (ODPs), and involve community efforts to support new ontology development, promoting standardized and interoperable data and knowledge representation and integration. The adoption of the XOD strategy, together with robust XOD tool development, will greatly support ontology interoperability and robust ontology applications to support data to be Findable, Accessible, Interoperable and Reusable (i.e., FAIR).

Highlights • Reverse vaccinology was applied to identify and rank ASFV immunogenic candidates . • Selected ASFV immunogenic candidate proteins were expressed in HEK-293 mammalian cells and MVA constructs . • Immunizations with antigens purified from HEK-293 cells and MVA constructs in swine were safe . • Immunizations with selected antigens induced ASFV-specific antibodies and T-cell responses in swine., A reverse vaccinology system, Vaxign, was used to identify and select a subset of five African Swine Fever (ASF) antigens that were successfully purified from human embryonic kidney 293 (HEK) cells and produced in Modified vaccinia virus Ankara (MVA) viral vectors. Three HEK-purified antigens [B646L (p72), E183L (p54), and O61R (p12)], and three MVA-vectored antigens [B646L, EP153R, and EP402R (CD2v)] were evaluated using a prime-boost immunization regimen swine safety and immunogenicity study. Antibody responses were detected in pigs following prime-boost immunization four weeks apart with the HEK-293-purified p72, p54, and p12 antigens. Notably, sera from the vaccinees were positive by immunofluorescence on ASFV (Georgia 2007/1)-infected primary macrophages. Although MVA-vectored p72, CD2v, and EP153R failed to induce antibody responses, interferon-gamma (IFN-γ+) spot forming cell responses against all three antigens were detected one week post-boost. The highest IFN-γ+ spot forming cell responses were detected against p72 in pigs primed with MVA-p72 and boosted with the recombinant p72. Antigen-specific (p12, p72, CD2v, and EP153R) T-cell proliferative responses were also detected post-boost. Collectively, these results are the first demonstration that ASFV subunit antigens purified from mammalian cells or expressed in MVA vectors are safe and can induce ASFV-specific antibody and T-cell responses following a prime-boost immunization regimen in swine.

© The Author(s) 2016. Linked Data (LD) aims to achieve interconnected data by representing entities using Unified Resource Identifiers (URIs), and sharing information using Resource Description Frameworks (RDFs) and HTTP. Ontologies, which logically represent entities and relations in specific domains, are the basis of LD. Ontobee (http://www.ontobee.org/) is a linked ontology data server that stores ontology information using RDF triple store technology and supports query, visualization and linkage of ontology terms. Ontobee is also the default linked data server for publishing and browsing biomedical ontologies in the Open Biological Ontology (OBO) Foundry (http://obofoundry.org) library. Ontobee currently hosts more than 180 ontologies (including 131 OBO Foundry Library ontologies) with over four million terms. Ontobee provides a user-friendly web interface for querying and visualizing the details and hierarchy of a specific ontology term. Using the eXtensible Stylesheet Language Transformation (XSLT) technology, Ontobee is able to dereference a single ontology term URI, and then output RDF/eXtensible Markup Language (XML) for computer processing or display the HTML information on a web browser for human users. Statistics and detailed information are generated and displayed for each ontology listed in Ontobee. In addition, a SPARQL web interface is provided for custom advanced SPARQL queries of one or multiple ontologies.

A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development.

The integrative Vaccine Investigation and Online Information Network (VIOLIN) vaccine research database and analysis system (http://www.violinet.org) curates, stores, analyses and integrates various vaccine-associated research data. Since its first publication in NAR in 2008, significant updates have been made. Starting from 211 vaccines annotated at the end of 2007, VIOLIN now includes over 3240 vaccines for 192 infectious diseases and eight noninfectious diseases (e.g. cancers and allergies). Under the umbrella of VIOLIN, >10 relatively independent programs are developed. For example, Protegen stores over 800 protective antigens experimentally proven valid for vaccine development. VirmugenDB annotated over 200 'virmugens', a term coined by us to represent those virulence factor genes that can be mutated to generate successful live attenuated vaccines. Specific patterns were identified from the genes collected in Protegen and VirmugenDB. VIOLIN also includes Vaxign, the first web-based vaccine candidate prediction program based on reverse vaccinology. VIOLIN collects and analyzes different vaccine components including vaccine adjuvants (Vaxjo) and DNA vaccine plasmids (DNAVaxDB). VIOLIN includes licensed human vaccines (Huvax) and veterinary vaccines (Vevax). The Vaccine Ontology is applied to standardize and integrate various data in VIOLIN. VIOLIN also hosts the Ontology of Vaccine Adverse Events (OVAE) that logically represents adverse events associated with licensed human vaccines.

Background Neuropathy often occurs following drug treatment such as chemotherapy. Severe instances of neuropathy can result in cessation of life-saving chemotherapy treatment. Results To support data representation and analysis of drug-associated neuropathy adverse events (AEs), we developed the Ontology of Drug Neuropathy Adverse Events (ODNAE). ODNAE extends the Ontology of Adverse Events (OAE). Our combinatorial approach identified 215 US FDA-licensed small molecule drugs that induce signs and symptoms of various types of neuropathy. ODNAE imports related drugs from the Drug Ontology (DrON) with their chemical ingredients defined in ChEBI. ODNAE includes 139 drug mechanisms of action from NDF-RT and 186 biological processes represented in the Gene Ontology (GO). In total ODNAE contains 1579 terms. Our analysis of the ODNAE knowledge base shows neuropathy-inducing drugs classified under specific molecular entity groups, especially carbon, pnictogen, chalcogen, and heterocyclic compounds. The carbon drug group includes 127 organic chemical drugs. Thirty nine receptor agonist and antagonist terms were identified, including 4 pairs (31 drugs) of agonists and antagonists that share targets (e.g., adrenergic receptor, dopamine, serotonin, and sex hormone receptor). Many drugs regulate neurological system processes (e.g., negative regulation of dopamine or serotonin uptake). SPARQL scripts were used to query the ODNAE ontology knowledge base. Conclusions ODNAE is an effective platform for building a drug-induced neuropathy knowledge base and for analyzing the underlying mechanisms of drug-induced neuropathy. The ODNAE-based methods used in this study can also be extended to the representation and study of other categories of adverse events. Electronic supplementary material The online version of this article (doi:10.1186/s13326-016-0069-x) contains supplementary material, which is available to authorized users.

Protective antigens are targeted by host acquired immunity and able to induce protection against infectious diseases. To identify enriched features that do not typically exist in non-protective protein antigens, this study analyzed 201 protective protein antigens from Gram-negative bacteria and 69 protective protein antigens from Gram-positive bacteria available in the manually curated Protegen protective antigen database. Our study found that 64% of Gram+ protective antigens are extracellular or cell wall proteins and 48% of protective antigens in Gram-bacteria belong to extracellular or outer membrane proteins. Approximately 54% and 40% protective antigens in Gram+ and Gram-, respectively, are adhesins or adhesin-like proteins. Many conserved domains (motifs), such as Autotransporter and TonB domains, are enriched in protective antigens. A protection method based on SVM (Support Vector Machine) classification demonstrates 92% of true positive rate of sequence-based protection. This study represents a pioneer effort in the identification and prediction of specific patterns in protective antigens.

Brucella spp. are intracellular bacteria that cause an infectious disease called brucellosis in humans and many domestic and wildlife animals. B. suis primarily infects pigs and is pathogenic to humans. The macrophage- Brucella interaction is critical for the establishment of a chronic Brucella infection. Our studies showed that smooth virulent B. suis strain 1330 (S1330) prevented programmed cell death of infected macrophages and rough attenuated B. suis strain VTRS1 (a vaccine candidate) induced strong macrophage cell death. To further investigate the mechanism of VTRS1-induced macrophage cell death, microarrays were used to analyze temporal transcriptional responses of murine macrophage-like J774.A1 cells infected with S1330 or VTRS1. In total 17,685 probe sets were significantly regulated based on the effects of strain, time and their interactions. A miniTUBA dynamic Bayesian network analysis predicted that VTRS1-induced macrophage cell death was mediated by a proinflammatory gene (the tumor necrosis factor alpha [TNF-α] gene), an NF-κB pathway gene (the IκB-α gene), the caspase-2 gene, and several other genes. VTRS1 induced significantly higher levels of transcription of 40 proinflammatory genes than S1330. A Mann-Whitney U test confirmed the proinflammatory response in VTRS1-infected macrophages. Increased production of TNF-α and interleukin 1β (IL-1β) were also detected in the supernatants in VTRS1-infected macrophage cell culture. Hyperphosphorylation of IκB-α was observed in macrophages infected with VTRS1 but not S1330. The important roles of TNF-α and IκB-α in VTRS1-induced macrophage cell death were further confirmed by individual inhibition studies. VTRS1-induced macrophage cell death was significantly inhibited by a caspase-2 inhibitor but not a caspase-1 inhibitor. The role of caspase-2 in regulating the programmed cell death of VTRS1-infected macrophages was confirmed in another study using caspase-2-knockout mice. In summary, VTRS1 induces a proinflammatory, caspase-2- and NF-κB-mediated macrophage cell death. This unique cell death differs from apoptosis, which is not proinflammatory. It is also different from classical pyroptosis, which is caspase-1 mediated.

Protective antigens are specifically targeted by the acquired immune response of the host and are able to induce protection in the host against infectious and non-infectious diseases. Protective antigens play important roles in vaccine development, as biological markers for disease diagnosis, and for analysis of fundamental host immunity against diseases. Protegen is a web-based central database and analysis system that curates, stores and analyzes protective antigens. Basic antigen information and experimental evidence are curated from peer-reviewed articles. More detailed gene/ protein information (e.g. DNA and protein sequences, and COG classification) are automatically extracted from existing databases using internally developed scripts. Bioinformatics programs are also applied to compute different antigen features, such as protein weight and pI, and subcellular localizations of bacterial proteins. Presently, 590 protective antigens have been curated against over 100 infectious diseases caused by pathogens and non-infectious diseases (including cancers and allergies). A user-friendly web query and visualization interface is developed for interactive protective antigen search. A customized BLAST sequence similarity search is also developed for analysis of new sequences provided by the users. To support data exchange, the information of protective antigens is stored in the Vaccine Ontology (VO) in OWL format and can also be exported to FASTA and Excel files. Protegen is publically available at http://www.violinet.org/protegen.

Vaxign is the first web-based vaccine design system that predicts vaccine targets based on genome sequences using the strategy of reverse vaccinology. Predicted features in the Vaxign pipeline include protein subcellular location, transmembrane helices, adhesin probability, conservation to human and/or mouse proteins, sequence exclusion from genome(s) of nonpathogenic strain(s), and epitope binding to MHC class I and class II. The precomputed Vaxign database contains prediction of vaccine targets for genomes. Vaxign also performs dynamic vaccine target prediction based on input sequences. To demonstrate the utility of this program, the vaccine candidates against uropathogenic Escherichia coli (UPEC) were predicted using Vaxign and compared with various experimental studies. Our results indicate that Vaxign is an accurate and efficient vaccine design program.

In the post-genomic era, strategies of vaccine development have progressed dramatically from traditional Pasteur's principles of isolating, inactivating and injecting the causative agent of an infectious disease, to reverse vaccinology that starts from bioinformatics analysis of the genome information. Based on the reverse vaccinology strategy, we have developed a web-based vaccine design system called Vaxign ( http://www.violinet.org/vaxign/ ). Vaxign predicts possible vaccine targets based on various vaccine design criteria using microbial genomic and protein sequences as input data. Major predicted features in the Vaxign pipeline include subcellular location of a protein, transmembrane domain, adhesion probability, sequence conservation among genomes, sequence similarity to host (human or mouse) proteome, and epitope binding to MHC class I and class II. This pipeline integrates both existing open source tools and internally developed programs with user-friendly web interfaces. A user can either query pre-computed Vaxign results for one protein sequence(s) from one genome(s) or perform dynamic analysis on input protein sequence(s). Vaxign has stored pre-computed results from more than 40 genomes from various pathogens (e.g., Brucella ). The analysis results indicate that Vaxign specifically and sensitively predicts known vaccine targets and also provides new vaccine target candidates. Vaxign is part of the web-based system called Vaccine Investigation and Online Information Network (VIOLIN, http://www.violinet.org ). Vaxign is a freely available program that facilitates vaccine researchers to efficiently design vaccine targets and develop vaccines using rationale reverse vaccinology.

Vaccines are among the most efficacious and cost-effective tools for reducing morbidity and mortality caused by infectious diseases. The vaccine investigation and online information network (VIOLIN) is a web-based central resource, allowing easy curation, comparison and analysis of vaccine-related research data across various human pathogens (e.g. Haemophilus influenzae, human immunodeficiency virus (HIV) and Plasmodium falciparum) of medical importance and across humans, other natural hosts and laboratory animals. Vaccine-related peer-reviewed literature data have been downloaded into the database from PubMed and are searchable through various literature search programs. Vaccine data are also annotated, edited and submitted to the database through a web-based interactive system that integrates efficient computational literature mining and accurate manual curation. Curated information includes general microbial pathogenesis and host protective immunity, vaccine preparation and characteristics, stimulated host responses after vaccination and protection efficacy after challenge. Vaccine-related pathogen and host genes are also annotated and available for searching through customized BLAST programs. All VIOLIN data are available for download in an eXtensible Markup Language (XML)-based data exchange format. VIOLIN is expected to become a centralized source of vaccine information and to provide investigators in basic and clinical sciences with curated data and bioinformatics tools for vaccine research and development. VIOLIN is publicly available at http://www.violinet.org.

Motivation: Many biomedical and clinical research problems involve discovering causal relationships between observations gathered from temporal events. Dynamic Bayesian networks are a powerful modeling approach to describe causal or apparently causal relationships, and support complex medical inference, such as future response prediction, automated learning, and rational decision making. Although many engines exist for creating Bayesian networks, most require a local installation and significant data manipulation to be practical for a general biologist or clinician. No software pipeline currently exists for interpretation and inference of dynamic Bayesian networks learned from biomedical and clinical data.Results: miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data.Availability: miniTUBA is available at http://www.minituba.orgContact: yongqunh@med.umich.edu

Implementation of inference rules using the ProtĂŠgĂŠ platform. (PDF 383Â kb)

SPARQL query of IDOBRU for Brucella mutants that are attenuated inside macrophages during the macrophage- Brucella interactions. Since each mutant is associated with one gene and one protein, these queries also allow us extract those virulence genes and protein virulence factors that participate in various macrophage-Brucella interactions. (PDF 353Â kb)

The response regulator OtpR is critical for the growth, morphology and virulence of Brucella melitensis. Compared to its wild type strain 16 M, B. melitensis 16 MΔotpR mutant has decreased tolerance to acid stress. To analyze the genes regulated by OtpR under acid stress, we performed RNA-seq whole transcriptome analysis of 16 MΔotpR and 16 M. In total, 501 differentially expressed genes were identified, including 390 down-regulated and 111 up-regulated genes. Among these genes, 209 were associated with bacterial metabolism, including 54 genes involving carbohydrate metabolism, 13 genes associated with nitrogen metabolism and seven genes associated with iron metabolism. The 16 MΔotpR also decreased capacity to utilize different carbon sources and to tolerate iron limitation in culture experiments. Notably, OtpR regulated many Brucella virulence factors essential for B. melitensis intracellular survival. For instance, the virB operon encoding type IV secretion system was significantly down-regulated and 36 known transcriptional regulators (e.g., vjbR and blxR) were differentially expressed in 16 MΔotpR. Selected RNA-seq results were experimentally confirmed by RT-PCR and RT-qPCR. Overall, these results deciphered differential phenomena associated with virulence, environmental stresses and cell morphology in 16 MΔotpR and 16 M, which provided important information for understanding the detailed OtpR-regulated interaction networks and Brucella pathogenesis.

Background Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. Methods In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher’s exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. Results INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with ‘INO_’ prefix. A new annotation property, ‘has literature mining keywords’, was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher’s exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these interaction types and their associated gene-gene pairs uncovered many scientific insights. Conclusions INO provides a novel approach for defining hierarchical interaction types and related keywords for literature mining. The ontology-based literature mining, in combination with an INO-based statistical interaction enrichment test, provides a new platform for efficient mining and analysis of topic-specific gene interaction networks.

Background: It is time-consuming to build an ontology with many terms and axioms. Thus it is desired to automate the process of ontology development. Ontology Design Patterns (ODPs) provide a reusable solution to solve a recurrent modeling problem in the context of ontology engineering. Because ontology terms often follow specific ODPs, the Ontology for Biomedical Investigations (OBI) developers proposed a Quick Term Templates (QTTs) process targeted at generating new ontology classes following the same pattern, using term templates in a spreadsheet format. Results: Inspired by the ODPs and QTTs, the Ontorat web application is developed to automatically generate new ontology terms, annotations of terms, and logical axioms based on a specific ODP(s). The inputs of an Ontorat execution include axiom expression settings, an input data file, ID generation settings, and a target ontology (optional). The axiom expression settings can be saved as a predesigned Ontorat setting format text file for reuse. The input data file is generated based on a template file created by a specific ODP (text or Excel format). Ontorat is an efficient tool for ontology expansion. Different use cases are described. For example, Ontorat was applied to automatically generate over 1,000 Japan RIKEN cell line cell terms with both logical axioms and rich annotation axioms in the Cell Line Ontology (CLO). Approximately 800 licensed animal vaccines were represented and annotated in the Vaccine Ontology (VO) by Ontorat. The OBI team used Ontorat to add assay and device terms required by ENCODE project. Ontorat was also used to add missing annotations to all existing Biobank specific terms in the Biobank Ontology. A collection of ODPs and templates with examples are provided on the Ontorat website and can be reused to facilitate ontology development. Conclusions: With ever increasing ontology development and applications, Ontorat provides a timely platform for generating and annotating a large number of ontology terms by following design patterns. Availability: http://ontorat.hegroup.org/

Background: Cell lines have been widely used in biomedical research. The community-based Cell Line Ontology (CLO) is a member of the OBO Foundry library that covers the domain of cell lines. Since its publication two years ago, significant updates have been made, including new groups joining the CLO consortium, new cell line cells, upper level alignment with the Cell Ontology (CL) and the Ontology for Biomedical Investigation, and logical extensions.Construction and content: Collaboration among the CLO, CL, and OBI has established consensus definitions of cell line-specific terms such as 'cell line', 'cell line cell', 'cell line culturing', and 'mortal' vs. 'immortal cell line cell'. A cell line is a genetically stable cultured cell population that contains individual cell line cells. The hierarchical structure of the CLO is built based on the hierarchy of the in vivo cell types defined in CL and tissue types (from which cell line cells are derived) defined in the UBERON cross-species anatomy ontology. The new hierarchical structure makes it easier to browse, query, and perform automated classification. We have recently added classes representing more than 2,000 cell line cells from the RIKEN BRC Cell Bank to CLO. Overall, the CLO now contains similar to 38,000 classes of specific cell line cells derived from over 200 in vivo cell types from various organisms.Utility and discussion: The CLO has been applied to different biomedical research studies. Example case studies include annotation and analysis of EBI ArrayExpress data, bioassays, and host-vaccine/pathogen interaction. CLO's utility goes beyond a catalogue of cell line types. The alignment of the CLO with related ontologies combined with the use of ontological reasoners will support sophisticated inferencing to advance translational informatics development.

Since the first DNA vaccine studies were done in the 1990s, thousands more studies have followed. Here we report the development and analysis of DNAVaxDB (http://www.violinet.org/dnavaxdb), the first publically available web-based DNA vaccine database that curates, stores, and analyzes experimentally verified DNA vaccines, DNA vaccine plasmid vectors, and protective antigens used in DNA vaccines. All data in DNAVaxDB are annotated from reliable resources, particularly peer-reviewed articles. Among over 140 DNA vaccine plasmids, some plasmids were more frequently used in one type of pathogen than others; for example, pCMVi-UB for G- bacterial DNA vaccines, and pCAGGS for viral DNA vaccines. Presently, over 400 DNA vaccines containing over 370 protective antigens from over 90 infectious and non-infectious diseases have been curated in DNAVaxDB. While extracellular and bacterial cell surface proteins and adhesin proteins were frequently used for DNA vaccine development, the majority of protective antigens used in Chlamydophila DNA vaccines are localized to the inner portion of the cell. The DNA vaccine priming, other vaccine boosting vaccination regimen has been widely used to induce protection against infection of different pathogens such as HIV. Parasitic and cancer DNA vaccines were also systematically analyzed. User-friendly web query and visualization interfaces are available in DNAVaxDB for interactive data search. To support data exchange, the information of DNA vaccines, plasmids, and protective antigens is stored in the Vaccine Ontology (VO). DNAVaxDB is targeted to become a timely and vital source of DNA vaccines and related data and facilitate advanced DNA vaccine research and development.

Summary: CRCView is a user-friendly point-and-click web server for analyzing and visualizing microarray gene expression data using a Dirichlet process mixture model-based clustering algorithm. CRCView is designed to clustering genes based on their expression profiles. It allows flexible input data format, rich graphical illustration as well as integrated GO term based annotation/interpretation of clustering results. Availability: http://helab.bioinformatics.med.umich.edu/crcview/ Contact: yongqunh@umich.edu

Background The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. Results The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists. Conclusions The GenoMesh algorithm and web program provide the first genome-wide, MeSH-based literature mining system that effectively predicts implicit gene-gene interaction relationships and networks in a genome-wide scope.

In vaccine design, databases and in silico tools play different but complementary roles. Databases collect experimentally verified vaccines and vaccine components, and in silico tools provide computational methods to predict and design new vaccines and vaccine components. Vaccine-related databases include databases of vaccines and vaccine components. In the USA, the Food and Drug Administration (FDA) maintains a database of licensed human vaccines, and the US Department of Agriculture keeps a database of licensed animal vaccines. Databases of vaccine clinical trials and vaccines in research also exist. The important vaccine components include vaccine antigens, vaccine adjuvants, vaccine vectors, and -vaccine preservatives. The vaccine antigens can be whole proteins or immune epitopes. Various in silico vaccine design tools are also available. The Vaccine Investigation and Online Information Network (VIOLIN; http://www.violinet.org ) is a comprehensive vaccine database and analysis system. The VIOLIN database includes various types of vaccines and vaccine components. VIOLIN also includes Vaxign, a Web-based in silico vaccine design program based on the reverse vaccinology strategy. Vaccine information and resources can be integrated with Vaccine Ontology (VO). This chapter introduces databases and in silico tools that facilitate vaccine design, especially those in the VIOLIN system.

Live attenuated vaccines are usually generated by mutation of genes encoding virulence factors. “Virmugen” is coined here to represent a gene that encodes for a virulent factor of a pathogen and has been proven feasible in animal models to make a live attenuated vaccine by knocking out this gene. Not all virulence factors are virmugens. VirmugenDB is a web-based virmugen database ( http://www.violinet.org/virmugendb ). Currently, VirmugenDB includes 225 virmugens that have been verified to be valuable for vaccine development against 57 bacterial, viral, and protozoan pathogens. Bioinformatics analysis has revealed significant patterns in virmugens. For example, 10 Gram-negative and 1 Gram-positive bacterial aroA genes are virmugens. A sequence analysis has revealed at least 50% of identities in the protein sequences of the 10 Gram-negative bacterial aroA virmugens. As a pathogen case study, Brucella virmugens were analyzed. Out of 15 verified Brucella virmugens, 6 are related to carbohydrate or nucleotide transport and metabolism, and 2 involving cell membrane biogenesis. In addition, 54 virmugens from 24 viruses and 12 virmugens from 4 parasites are also stored in VirmugenDB. Virmugens tend to involve metabolism of nutrients (e.g., amino acids, carbohydrates, and nucleotides) and cell membrane formation. Host genes whose expressions were regulated by virmugen mutation vaccines or wild type virulent pathogens have also been annotated and systematically compared. The bioinformatics annotation and analysis of virmugens helps to elucidate enriched virmugen profiles and the mechanisms of protective immunity, and further supports rational vaccine design.

Vaccine adjuvants are compounds that enhance host immune responses to co-administered antigens in vaccines. Vaxjo is a web-based central database and analysis system that curates, stores, and analyzes vaccine adjuvants and their usages in vaccine development. Basic information of a vaccine adjuvant stored in Vaxjo includes adjuvant name, components, structure, appearance, storage, preparation, function, safety, and vaccines that use this adjuvant. Reliable references are curated and cited. Bioinformatics scripts are developed and used to link vaccine adjuvants to different adjuvanted vaccines stored in the general VIOLIN vaccine database. Presently, 103 vaccine adjuvants have been curated in Vaxjo. Among these adjuvants, 98 have been used in 384 vaccines stored in VIOLIN against over 81 pathogens, cancers, or allergies. All these vaccine adjuvants are categorized and analyzed based on adjuvant types, pathogens used, and vaccine types. As a use case study of vaccine adjuvants in infectious disease vaccines, the adjuvants used inBrucellavaccines are specifically analyzed. A user-friendly web query and visualization interface is developed for interactive vaccine adjuvant search. To support data exchange, the information of vaccine adjuvants is stored in the Vaccine Ontology (VO) in the Web Ontology Language (OWL) format.

Background Interferon-gamma (IFN-γ) is vital in vaccine-induced immune defense against bacterial and viral infections and tumor. Our recent study demonstrated the power of a literature-based discovery method in extraction and comparison of the IFN-γ and vaccine-mediated gene interaction networks. The Vaccine Ontology (VO) contains a hierarchy of vaccine names. It is hypothesized that the application of VO will enhance the prediction of IFN-γ and vaccine-mediated gene interaction network. Results In this study, 186 specific vaccine names listed in the Vaccine Ontology (VO) and their semantic relations were used for possible improved retrieval of the IFN-γ and vaccine associated gene interactions. The application of VO allows discovery of 38 more genes and 60 more interactions. Comparison of different layers of IFN-γ networks and the example BCG vaccine-induced subnetwork led to generation of new hypotheses. By analyzing all discovered genes using centrality metrics, 32 genes were ranked high in the VO-based IFN-γ vaccine network using four centrality scores. Furthermore, 28 specific vaccines were found to be associated with these top 32 genes. These specific vaccine-gene associations were further used to generate a network of vaccine-vaccine associations. The BCG and LVS vaccines are found to be the most central vaccines in the vaccine-vaccine association network. Conclusion Our results demonstrate that the combined usages of biomedical ontologies and centrality-based literature mining are able to significantly facilitate discovery of gene interaction networks and gene-concept associations. Availability VO is available at: http://www.violinet.org/vaccineontology; and the SVM edit kernel for gene interaction extraction is available at: http://www.violinet.org/ifngvonet/int_ext_svm.zip

Interferon-gamma (IFN-γ) regulates various immune responses that are often critical for vaccine-induced protection. In order to annotate the IFN-γ-related gene interaction network from a large amount of IFN-γresearch reported in the literature, a literature-based discovery approach was applied with a combination of natural language processing (NLP) and network centrality analysis. The interaction network of human IFN-γ(Gene symbol: IFNG) and its vaccine-specific subnetwork were automatically extracted using abstracts from all articles in PubMed. Four network centrality metrics were further calculated to rank the genes in the constructed networks. The resulting generic IFNG network contains 1060 genes and 26313 interactions among these genes. The vaccine-specific subnetwork contains 102 genes and 154 interactions. Fifty six genes such as TNF, NFKB1, IL2, IL6, and MAPK8 were ranked among the top 25 by at least one of the centrality methods in one or both networks. Gene enrichment analysis indicated that these genes were classified in various immune mechanisms such as response to extracellular stimulus, lymphocyte activation, and regulation of apoptosis. Literature evidence was manually curated for the IFN-γrelatedness of 56 genes and vaccine development relatedness for 52 genes. This study also generated many new hypotheses worth further experimental studies.

Signaling and regulatory pathways that guide gene expression have only been partially defined for most organisms. However, given the increasing number of microarray measurements, it may be possible to reconstruct such pathways and uncover missing connections directly from experimental data. Using a compendium of microarray gene expression data obtained from Escherichia coli, we constructed a series of Bayesian network models for the reactive oxygen species (ROS) pathway as defined by EcoCyc. A consensus Bayesian network model was generated using those networks sharing the top recovered score. This microarray-based network only partially agreed with the known ROS pathway curated from the literature and databases. A top network was then expanded to predict genes that could enhance the Bayesian network model using an algorithm we termed 'BN+1'. This expansion procedure predicted many stress-related genes (e.g., dusB and uspE), and their possible interactions with other ROS pathway genes. A term enrichment method discovered that biofilm-associated microarray data usually contained high expression levels of both uspE and gadX. The predicted involvement of gene uspE in the ROS pathway and interactions between uspE and gadX were confirmed experimentally using E. coli reporter strains. Genes gadX and uspE showed a feedback relationship in regulating each other's expression. Both genes were verified to regulate biofilm formation through gene knockout experiments. These data suggest that the BN+1 expansion method can faithfully uncover hidden or unknown genes for a selected pathway with significant biological roles. The presently reported BN+1 expansion method is a generalized approach applicable to the characterization and expansion of other biological pathways and living systems.

PubMed articles are annotated using the Medical Subject Headings (MeSH) to increase search efficiency. However, MeSH contains limited information on many biomedical domains (e.g., vaccine). Biomedical ontologies may be used to improve PubMed searching capability. This study demonstrates that Vaccine Ontology (VO) can be used to significantly improve PubMed searching efficacy in the vaccine domain. The recall and precision of the ontology-based literature mining approach are analyzed and discussed.

The Centers for Disease Control and Prevention Category A infectious agents include Bacillus anthracis (anthrax), Clostridium botulinum toxin (botulism), Yersinia pestis (plague), variola major virus (smallpox), Francisella tularensis (tularemia), and the filoviruses and arenaviruses that induce viral hemorrhagic fevers. These agents are regarded as having the greatest potential for adverse impact on public health and therefore are a focus of renewed attention in infectious disease research. Frequently rodent models are used to study the pathobiology of these agents. Although much is known regarding naturally occurring infections in humans, less is documented on the sources of exposures and potential risks of infection to researchers and animal care personnel after the administration of these hazardous substances to laboratory animals. Failure to appropriately manage the animals can result both in the creation of workplace hazards if human exposures occur and in disruption of the research if unintended animal exposures occur. Here we review representative Category A agents, with a focus on comparing the biologic effects in naturally infected humans and rodent models and on considerations specific to the management of infected rodent subjects. The information reviewed for each agent has been curated manually and stored in a unique Internet-based database system called HazARD (Hazards in Animal Research Database, http://helab.bioinformatics.med.umich.edu/hazard/) that is designed to assist researchers, administrators, safety officials, Institutional Biosafety Committees, and veterinary personnel seeking information on the management of risks associated with animal studies involving hazardous substances.

Background Brucella species are Gram-negative, facultative intracellular bacteria that cause brucellosis in humans and animals. Sequences of four Brucella genomes have been published, and various Brucella gene and genome data and analysis resources exist. A web gateway to integrate these resources will greatly facilitate Brucella research. Brucella genome data in current databases is largely derived from computational analysis without experimental validation typically found in peer-reviewed publications. It is partially due to the lack of a literature mining and curation system able to efficiently incorporate the large amount of literature data into genome annotation. It is further hypothesized that literature-based Brucella gene annotation would increase understanding of complicated Brucella pathogenesis mechanisms. Results The Brucella Bioinformatics Portal (BBP) is developed to integrate existing Brucella genome data and analysis tools with literature mining and curation. The BBP InterBru database and Brucella Genome Browser allow users to search and analyze genes of 4 currently available Brucella genomes and link to more than 20 existing databases and analysis programs. Brucella literature publications in PubMed are extracted and can be searched by a TextPresso-powered natural language processing method, a MeSH browser, a keywords search, and an automatic literature update service. To efficiently annotate Brucella genes using the large amount of literature publications, a literature mining and curation system coined Limix is developed to integrate computational literature mining methods with a PubSearch-powered manual curation and management system. The Limix system is used to quickly find and confirm 107 Brucella gene mutations including 75 genes shown to be essential for Brucella virulence. The 75 genes are further clustered using COG. In addition, 62 Brucella genetic interactions are extracted from literature publications. These results make possible more comprehensive investigation of Brucella pathogenesis. Other BBP features include publication email alert service, Brucella researchers' contact database, and discussion forum. Conclusion BBP is a gateway for Brucella researchers to search, analyze, and curate Brucella genome data originated from public databases and literature. Brucella gene mutations and genetic interactions are annotated using Limix leading to better understanding of Brucella pathogenesis.