66
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Emerging Vaccine Informatics

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning.

          Related collections

          Most cited references288

          • Record: found
          • Abstract: found
          • Article: not found

          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Basic Local Alignment Search Tool

            S Altschul (1990)
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              In silico prediction of protein-protein interactions in human macrophages

              Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
                Bookmark

                Author and article information

                Journal
                J Biomed Biotechnol
                JBB
                Journal of Biomedicine and Biotechnology
                Hindawi Publishing Corporation
                1110-7243
                1110-7251
                2010
                15 June 2011
                : 2010
                : 218590
                Affiliations
                1Department of Microbiology and Immunology, Unit for Laboratory Animal Medicine, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
                2Novartis Vaccines and Diagnostics, 53100 Siena, Italy
                3EpiVax, Inc., Providence, RI 02903, USA
                4Institute for Immunology and Informatics, University of Rhode Island, Providence, RI 02903, USA
                5HIV Vaccine and Special Studies Team, Centers for Disease Control and Prevention (CDC/DHAP/EB), Atlanta, GA 30333, USA
                Author notes

                Academic Editor: Rodomiro Ortiz

                Article
                10.1155/2010/218590
                3134832
                21772787
                42bdf4d8-34be-447e-a1ac-b38bc788e40d
                Copyright © 2010 Yongqun He et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 8 December 2010
                : 31 December 2010
                Categories
                Review Article

                Molecular medicine
                Molecular medicine

                Comments

                Comment on this article