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PubMed2003

Designing XML schemas for bioinformatics.

Data interchange bioinformatics databases will, in the future, most likely take place using extensible markup language (XML). The document structure will be described by an XML Schema rather than a document type definition (DTD). To ensure flexibility, the XML Schema must incorporate aspects of Object-Oriented Modeling. This impinges on the choice of the data model, which, in turn, is based on the organization of bioinformatics data by biologists. Thus, there is a need for the general bioinformatics community to be aware of the design issues relating to XML Schema. This paper, which is aimed at a general bioinformatics audience, uses examples to describe the differences between a DTD and an XML Schema and indicates how Unified Modeling Language diagrams may be used to incorporate Object-Oriented Modeling in the design of schema.

Biotechnology↗

PubMed2004

Structural bioinformatics and its impact to biomedical science.

During the last two decades, the number of sequence-known proteins has increased rapidly. In contrast, the corresponding increment for structure-known proteins is much slower. The unbalanced situation has critically limited our ability to understand the molecular mechanism of proteins and conduct structure-based drug design by timely using the updated information of newly found sequences. Therefore, it is highly desired to develop an automated method for fast deriving the 3D (3-dimensional) structure of a protein from its sequence. Under such a circumstance, the structural bioinformatics was emerging naturally as the time required. In this review, three main strategies developed in structural bioinformatics, i.e., pure energetic approach, heuristic approach, and homology modeling approach, as well as their underlying principles, are briefly introduced. Meanwhile, a series of demonstrations are presented to show how the structural bioinformatics has been applied to timely derive the 3D structures of some functionally important proteins, helping to understand their action mechanisms and stimulating the course of drug discovery. Also, the limitation of these approaches and the future challenges of structural bioinformatics are briefly addressed.

Amino Acid Sequence↗

PubMed2004

Structural bioinformatic approaches to the discovery of new antimycobacterial drugs.

Integrated bioinformatic approaches to drug discovery exploit computational techniques to examine the flow of information from genome to structure to function. Informatics is being be used to accelerate and rationalize the process of antimycobacterial drug discovery and design, with the immediate goals to identify viable drug targets and produce a set of critically evaluated protein target models and corresponding set of probable lead compounds. Bioinformatic approaches are being successfully applied in the selection and prioritization of putative mycobacterial drug target genes; computational modelling and x-ray structure validation of protein targets with drug lead compounds; simulated docking and virtual screening of potential lead compounds; and lead validation and optimization using structure-activity and structure-function relationships. By identifying active sites, characterizing patterns of conserved residues and, where relevant, predicting catalytic residues, bioinformatics provides information to aid the design of selective and efficacious pharmacophores. In this review, we describe selected recent progress in antimycobacterial drug design, illustrating the strengths and limitations of current structural bioinformatic approaches as tools in the fight against tuberculosis.

Anti-Bacterial Agents↗

PubMed2005

Pokemon expression in malignant glioma: an application of bioinformatics methods.

OBJECT: In this report the authors review the role of bioinformatics in the design of a research project in which the molecular genetics of malignant glioma were studied. A project to characterize Pokemon expression in malignant glioma was developed, refined, and implemented using bioinformatics methods. METHODS: Using the resources available from the National Center for Biotechnology Information, the messenger RNA (mRNA) sequence for Pokemon was determined. With this information and online primer design tools, novel primers were designed that would specifically amplify Pokemon mRNA by using reverse transcription-polymerase chain reaction assays. CONCLUSIONS: The promise of bioinformatics is in the rapid and widespread dissemination and analysis of genomic information. This information is then used in research investigating the genetic basis of disease. In this paper the authors review the bioinformatics methods used in their study of Pokemon expression in malignant glioma.

Base Sequence↗

PubMed2000

An evaluation of ontology exchange languages for bioinformatics.

Ontologies are specifications of the concepts in a given field, and of the relationships among those concepts. The development of ontologies for molecular-biology information and the sharing of those ontologies within the bioinformatics community are central problems in bioinformatics. If the bioinformatics community is to share ontologies effectively, ontologies must be exchanged in a form that uses standardized syntax and semantics. This paper reports on an effort among the authors to evaluate alternative ontology-exchange languages, and to recommend one or more languages for use within the larger bioinformatics community. The study selected a set of candidate languages, and defined a set of capabilities that the ideal ontology-exchange language should satisfy. The study scored the languages according to the degree to which they satisfied each capability. In addition, the authors performed several ontology-exchange experiments with the two languages that received the highest scores: OML and Ontolingua. The result of those experiments, and the main conclusion of this study, was that the frame-based semantic model of Ontolingua is preferable to the conceptual graph model of OML, but that the XML-based syntax of OML is preferable to the Lisp-based syntax of Ontolingua.

Computational Biology↗

PubMed2003

[Bioinformatic analysis of glioma development relative genes].

BACKGROUND & OBJECTIVE: Exploiting the transcriptional regulation mechanism by microarray and bioinformatics is an important method at genomic research field, and it is better than previous methods, by which we can easily analyze the gene expression regulative networks at genomic level. This study was designed to analyze the glioma gene expression profiles by clustering and bioinformatic retrieving to seek some tumor development relative genes that can be cloned for the purpose of function research in the future. METHODS: Firstly, we analyzed the glioma gene expression profiles of 16,363 genes by clustering, and chose 368 genes which expression differences were greater than 2 folds and the variances of 2 dots were smaller than 0.33. We selected 2 groups of genes that were expressed similarly. One group (11 cases) was upregulated with the glioma development and another (6 cases) was downregulated with the glioma development. Secondly, we determined the genomic information about those 17 genes and exploited their association with the development of gliomas. RESULTS: Two groups of genes were expressed similarly during the glioma development, one group was upregulated with the development of gliomas, and another was downregulated. Bioinformatic analysis showed that 3 of those genes (X55987, M85085, and AB011097) might be important tumor relative genes. CONCLUSION: By bioinformatics and microarray technologies, we found 3 genes, X55987(EDN, eosinophil-derived neurotoxin), AB011097 (ARTS-1, TNF receptor shedding regulator),and M85085(CStF, cleavage stimulation factor), which might be potential key tumor development relative genes that can be developed for therapy targets in the future.

Adult↗

PubMed2003

Reflections on an arranged marriage between bioinformatics and health informatics.

OBJECTIVE: To compare the discussions of two workshops held during 2001 by two Canadian organisations, HEALNet, a Network of Centres of Excellence for research in health information applications, and Genome Canada, a national research funding agency for genomics and proteomics, in collaboration with the Institute of Genetics of the Canadian Institutes of Health Research, to examine strategic research development in Health Informatics and Bioinformatics respectively. METHODS: Invited workshops with structured debate. Concept analysis of preparative material and debates. RESULTS: A predominantly common set of concepts was discerned from both workshops. Analysis of published definitions showed an inability to distinguish a definition that would suggest that health informatics and bioinformatics are separate disciplines. In both workshops there was evidence of deep concerns of identity, the lack of clear structures to support research funding as well as uncertainty in distinguishing between service and research. CONCLUSIONS: Many deep issues currently inhibit the recognition and funding of research in health and bioinformatics in Canada and elsewhere. Some of these issues are common to both health and bioinformatics. The overlap in prevailing definitions, research concerns and methodological content in the respective domains suggest that common research needs should be better identified and reinforced for the benefit of both.

Biomedical Research↗

PubMedDate not supplied

[What is the subject of science "bioinformatics"?].

The paper is concerned with some problems of terminology, in particular the term "bioinformatics". In the last few years, the term "bioinformatics" has been intensively used among molecular biologists to indicate a subject that is only a constituent of genomics and is considered to involve a computer-assisted analysis of all data on nucleotide sequences of DNA. However, a wide circle of scientists, including biologists, physicists, mathematicians, and specialists in the field of cybernetics, informatics, and other disciplines have accepted and accept, as a rule, the "bioinformatics" as a synonym of science cybernetics and as a successor of this science. In this case, the subject of science "bioinformatics" should embrace not only genomics but practically all sections of the biological science. It should involve a study of information processes (storage, transfer, and processing of information, etc.) participating in the regulation and control at all levels of living systems, from macromolecules to the brain of higher animals and human.

Computational Biology↗

PubMed2005

Automated tissue analysis--a bioinformatics perspective.

OBJECTIVES: Recent progress in automated tissue analysis (tissomics) provides reproducible phenotypical characterization of histological specimens. We introduce informatics tools to cluster and correlate quantitative tissue profiles with gene expression data. The great potential of synergies between tissue analysis and bioinformatics and its perspectives in medical research and computational diagnostics are discussed. METHODS: Key enablers in microscopic imaging and machine vision are reviewed to perform a high-throughput tissue analysis. Methodologies are described and results are demonstrated that support a combined analysis of tissue with gene expression profiles whereby the consideration of individual responses is key. RESULTS: Comprehensive histomorphometric profiles, extracted using machine vision, provide information regarding the components and heterogeneity of a tissue in a reproducible format amenable to data mining and analysis. Tissue quantitative information can be placed in synergetic context with bioinformatics data, such as gene expression profiles, for a more comprehensive stratification of individual responses. From a bioinformatics point of view tissue data are co-variants that support the identification of candidate genes relevant in tissue injury or disease. CONCLUSIONS: Progress in automated analytics enables the generation of quantitative data about tissue previously limited to visual histopathology. Such reproducible data sets can be statistically correlated and clustered throughout the continuum of bioinformatics. The combined approach supports a system-wide view of biology and has a potential to accelerate developments for a personalized computational diagnosis.

Automation↗

PubMed2006

Broad issues to consider for library involvement in bioinformatics.

BACKGROUND: The information landscape in biological and medical research has grown far beyond literature to include a wide variety of databases generated by research fields such as molecular biology and genomics. The traditional role of libraries to collect, organize, and provide access to information can expand naturally to encompass these new data domains. METHODS: This paper discusses the current and potential role of libraries in bioinformatics using empirical evidence and experience from eleven years of work in user services at the National Center for Biotechnology Information. FINDINGS: Medical and science libraries over the last decade have begun to establish educational and support programs to address the challenges users face in the effective and efficient use of a plethora of molecular biology databases and retrieval and analysis tools. As more libraries begin to establish a role in this area, the issues they face include assessment of user needs and skills, identification of existing services, development of plans for new services, recruitment and training of specialized staff, and establishment of collaborations with bioinformatics centers at their institutions. CONCLUSIONS: Increasing library involvement in bioinformatics can help address information needs of a broad range of students, researchers, and clinicians and ultimately help realize the power of bioinformatics resources in making new biological discoveries.

Computational Biology↗

PubMed1998

Bioinformatics in support of molecular medicine.

Bioinformatics studies two important information flows in modern biology. The first is the flow of genetic information from the DNA of an individual organism up to the characteristics of a population of such organisms (with an eventual passage of information back to the genetic pool, as encoded within DNA). The second is the flow of experimental information from observed biological phenomena to models that explain them, and then to new experiments in order to test these models. The discipline of bioinformatics has its roots in a number of activities, including the organization of DNA sequence and protein three-dimensional structural data collections in the 1960's and 1970's. It has become a booming academic and industrial enterprise with the introduction of biological experiments that rapidly produce massive amounts of data (such as the multiple genome sequencing projects, the large scale analysis of gene expression, and the large scale analysis of protein-protein interactions). Basic biological science has always had an impact on clinical medicine (and clinical medical information systems), and is creating a new generation of epidemiologic, diagnostic, prognostic, and treatment modalities. Bioinformatics efforts that appear to be wholly geared towards basic science are likely to become relevant to clinical informatics in the coming decade. For example, DNA sequence information and sequence annotations will appear in the medical chart with increasing frequency. The algorithms developed for research in bioinformatics will soon become part of clinical information systems.

Computational Biology↗

PubMed2026

Integrated experimental and bioinformatics analysis reveals ECM-integrin and redox signaling associated with PMMA/NiO nanocomposites for craniofacial applications.

BACKGROUND: Poly(methyl methacrylate) (PMMA) is widely used in dental and craniofacial applications; however, its clinical performance is limited by poor surface wettability, moderate mechanical strength, and restricted biological activity. Integrating nanomaterial engineering with computational biology offers an opportunity to better understand biomaterial-cell interactions and support the rational design of functional biomaterials. METHODS: Nickel oxide (NiO) nanoparticles were synthesized via chemical precipitation and incorporated into PMMA to fabricate nanocomposites. Physicochemical characterization included contact angle measurements, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and Vickers hardness testing. Biocompatibility was evaluated using zebrafish embryo developmental assays. To explore biological processes potentially associated with biomaterial-cell interactions, bioinformatics analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and STRING protein-protein interaction (PPI) network analyses were performed. RESULTS: Incorporation of NiO nanoparticles improved the surface and mechanical properties of PMMA, reducing the contact angle from 105.35° to 90.46° and increasing Vickers hardness compared with unmodified PMMA. Structural and morphological analyses confirmed successful synthesis and homogeneous nanoparticle incorporation. Zebrafish embryo studies demonstrated minimal developmental toxicity, supporting the biocompatibility of the nanocomposite. Bioinformatics analyses identified significant enrichment of pathways related to extracellular matrix organization, cell adhesion, focal adhesion, PI3K-Akt signaling, and oxidative stress regulation. Protein-protein interaction analysis revealed highly interconnected networks associated with ECM-integrin signaling and redox homeostasis, highlighting biological processes potentially associated with biomaterial-cell communication. CONCLUSIONS: PMMA/NiO nanocomposites exhibited improved physicochemical performance and favorable biocompatibility characteristics. The integration of experimental characterization with bioinformatics and network-based analyses provides a systems-level perspective on biomaterial-associated cellular processes and identifies ECM-integrin signaling and oxidative stress-related pathways as candidate biological processes for future experimental validation. These findings support the continued development of PMMA/NiO nanocomposites for oral and craniofacial biomedical applications.

Nanocomposites↗

PubMed2026

Bioinformatics in crop research: using genomic data for crop improvement.

Sustainable crop development aims to maintain or increase yields while reducing environmental impact and managing the challenges imposed by climate change. As the global population grows and arable land becomes scarcer, the integration of molecular breeding with bioinformatics has emerged as an effective strategy for long-term crop improvement. Bioinformatics enables researchers to analyze and interpret the vast quantities of genetic data generated by high-throughput sequencing, making it possible to identify molecular markers, candidate genes, and regulatory networks linked to specific agronomic traits, which breeders then translate into focused, ecologically sustainable breeding programs. This approach has enabled major progress across several fronts: the identification of genes conferring resistance to biotic stressors (pests, pathogens) and abiotic stressors (drought, salinity, heat); the development of nutrient-efficient, low-input crop varieties; the improvement of agronomic performance and nutritional quality through identification of yield- and quality-related genes; and the conservation and deployment of genetic diversity to safeguard long-term breeding sustainability. By combining genomic data with precision breeding techniques, researchers are developing crops that are better adapted to a growing population and a changing climate, positioning the integration of molecular breeding and bioinformatics as a central pillar of future global food security.

bioinformatics↗

PubMed2002

Biomic study of human myeloid leukemia cells differentiation to macrophages using DNA array, proteomic, and bioinformatic analytical methods.

A biomic approach by integrating three independent methods, DNA microarray, proteomics and bioinformatics, is used to study the differentiation of human myeloid leukemia cell line HL-60 into macrophages when induced by 12-O-tetradecanoyl-phorbol-13-acetate (TPA). Analysis of gene expression changes at the RNA level using cDNA against an array of 6033 human genes showed that 5950 (98.6%) of the genes were expressed in the HL-60 cells. A total of 624 genes (10.5%) were found to be regulated during HL-60 cell differentiation. Most of these genes have not been previously associated with HL-60 cells and include genes encoded for secreted proteins as well as genes involved in cell adhesion, signaling transduction, and metabolism. Protein analysis using two-dimensional gel electrophoresis showed a total of 682 distinct protein spots; 136 spots (19.9%) exhibited quantitative changes between HL-60 control and macrophages. These differentially expressed proteins were identified by mass spectrometry. We developed a bioinformatics program, the Bulk Gene Search System (BGSS, http://www.sinica.edu.tw:8900/perl/genequery.pl) to search for the functions of genes and proteins identified by cDNA microarrays and proteomics. The identified regulated proteins and genes were classified into seven groups according to subcellular locations and functions. This powerful holistic biomic approach using cDNA microarray, proteomics coupled to bioinformatics can provide in-depth information on the impact and importance of the regulated genes and proteins for HL-60 differentiation.

Cell Differentiation↗

PubMed2001

Bioinformatics for rice resources.

The distinguishing feature of the 'new biology' is that it is information intensive. Not only does it demand access to and assimilation of vast data sets accumulated by engineered laboratory processes, but it also demands a previously unimaginable level of data integration across data types and sources. There are various information resources available for rice. In addition, there are various information resources that are not focused on rice but that contain rice data. The challenge for rice researchers and breeders is to access this wealth of data meaningfully. This challenge will grow significantly as international efforts aimed at sequencing the entire rice genome come into full swing. Only through concerted efforts in bioinformatics will the power of these public data be brought to bear on the needs of rice researchers and breeders worldwide. These efforts will need to focus on two large but distinct areas: (1) development of an effective bioinformatics infrastructure (hardware systems, software systems, and software engineers and support staff) and (2) computational biology research in visualization and analysis of very large, complex data sets, such as those that will be developed using high-throughput expression technologies, large-scale insertional mutagenesis, and biochemical profiling of various types. In the midst of the large flow of high-throughput data that the international rice genome sequencing efforts will produce, it is also imperative that integration of those data with unique germplasm data held in trust by the CGIAR be a part of the informatics infrastructure. This paper will focus on the state of rice information resources, the needs of the rice community, and some proposed bioinformatics activities to support these needs.

Algorithms↗

PubMed2006

Gene expression of human T lymphocytes cell cycle: experimental and bioinformatic analysis.

Human lymphocytes gene expression is monitored before and after PHA stimulation over 72 h, using DNA microarray technology. Results are then compared with our previous bioinformatics predictions, which identified six leader genes of highest importance in human T lymphocytes cell cycle. Experimental data are strikingly compatible with bioinformatic predictions of the specific role and interaction of PCNA, CDC2, and CCNA2 at all phases of the cell cycle and of CHEK1 in regulating DNA repair and preservation. It does not escape our notice that the conception and use of ad hoc arrays, based on a bioinformatics prediction which identifies the most important genes involved in a particular biological process, can really be an added value in cell biology and cancer research alternative to massive frequently misleading molecular genomics.

CDC2 Protein Kinase↗

PubMed2006

Introduction to bioinformatics.

This article introduces the field of bioinformatics and describes bioinformatic approaches and their application to the study of protein allergens. The predominant bioinformatics tools and resources are listed and discussed.

Allergens↗

PubMed2006

Practical and predictive bioinformatics methods for the identification of potentially cross-reactive protein matches.

A bioinformatics comparison of proteins introduced into food crops through genetic engineering provides a mechanism to identify those proteins that may present an increased risk of allergic reactions for individuals with existing allergies. The goal is to identify proteins that are known to be allergens or are so similar to an allergen that they may induce allergic cross-reactions. Three comparative approaches have traditionally been used, or considered for safety evaluations. One identifies any short (6-8) amino acid segment of the protein that exactly matches a known allergen sequence. The second is an overall primary sequence comparison using Basic Local Alignment Search Tool (BLAST) or FASTA to find matches of greater than 35% identity over 80 amino acids. The third is based on 3-D prediction programs to identify 3-D similarities that might predict potential cross-reactivity. The utility of each of these approaches was debated in the bioinformatics workshop. The consensus agreement from the expert workshop participants was that the short-segment match (e. g., 6-8 amino acids) provides an unacceptably high rate of false positive matches and an uncertain rate of true positive matches, and was not particularly useful for an allergenicity evaluation performed in the context of comprehensive safety evaluation. There was no consensus regarding the most appropriate bioinformatics method, an acceptable scoring criteria for triggering closer examination subsequent to a positive match, or an acceptable scoring mechanism for ranking the utility of the various 3-D approaches that were discussed during the workshop. However, the general consensus was that the most practical approach at this time is to evaluate primary sequence identities to known allergens using either FASTA or BLAST. While there was good agreement that identities of greater than 35% over 80 or more amino acids (recommended by Codex in 2003) is quite conservative, the conclusion was that additional data or studies would be needed to justify changing this criterion as there is some evidence that some individuals sensitized to proteins in evolutionarily conserved protein families may experience cross-reactions to proteins sharing approximately 40% identity.

Algorithms↗