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PubMed2006

BioWarehouse: a bioinformatics database warehouse toolkit.

BACKGROUND: This article addresses the problem of interoperation of heterogeneous bioinformatics databases. RESULTS: We introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research. CONCLUSION: BioWarehouse embodies significant progress on the database integration problem for bioinformatics.

Computational Biology↗
PubMed2006

XML schemas for common bioinformatic data types and their application in workflow systems.

BACKGROUND: Today, there is a growing need in bioinformatics to combine available software tools into chains, thus building complex applications from existing single-task tools. To create such workflows, the tools involved have to be able to work with each other's data--therefore, a common set of well-defined data formats is needed. Unfortunately, current bioinformatic tools use a great variety of heterogeneous formats. RESULTS: Acknowledging the need for common formats, the Helmholtz Open BioInformatics Technology network (HOBIT) identified several basic data types used in bioinformatics and developed appropriate format descriptions, formally defined by XML schemas, and incorporated them in a Java library (BioDOM). These schemas currently cover sequence, sequence alignment, RNA secondary structure and RNA secondary structure alignment formats in a form that is independent of any specific program, thus enabling seamless interoperation of different tools. All XML formats are available at http://bioschemas.sourceforge.net, the BioDOM library can be obtained at http://biodom.sourceforge.net. CONCLUSION: The HOBIT XML schemas and the BioDOM library simplify adding XML support to newly created and existing bioinformatic tools, enabling these tools to interoperate seamlessly in workflow scenarios.

Algorithms↗
PubMed2003

Evolving strategies for the incorporation of bioinformatics within the undergraduate cell biology curriculum.

Recent advances in genomics and structural biology have resulted in an unprecedented increase in biological data available from Internet-accessible databases. In order to help students effectively use this vast repository of information, undergraduate biology students at Drake University were introduced to bioinformatics software and databases in three courses, beginning with an introductory course in cell biology. The exercises and projects that were used to help students develop literacy in bioinformatics are described. In a recently offered course in bioinformatics, students developed their own simple sequence analysis tool using the Perl programming language. These experiences are described from the point of view of the instructor as well as the students. A preliminary assessment has been made of the degree to which students had developed a working knowledge of bioinformatics concepts and methods. Finally, some conclusions have been drawn from these courses that may be helpful to instructors wishing to introduce bioinformatics within the undergraduate biology curriculum.

Biology↗
PubMed2005

Applying instructional design theories to bioinformatics education in microarray analysis and primer design workshops.

The need to support bioinformatics training has been widely recognized by scientists, industry, and government institutions. However, the discussion of instructional methods for teaching bioinformatics is only beginning. Here we report on a systematic attempt to design two bioinformatics workshops for graduate biology students on the basis of Gagne's Conditions of Learning instructional design theory. This theory, although first published in the early 1970s, is still fundamental in instructional design and instructional technology. First, top-level as well as prerequisite learning objectives for a microarray analysis workshop and a primer design workshop were defined. Then a hierarchy of objectives for each workshop was created. Hands-on tutorials were designed to meet these objectives. Finally, events of learning proposed by Gagne's theory were incorporated into the hands-on tutorials. The resultant manuals were tested on a small number of trainees, revised, and applied in 1-day bioinformatics workshops. Based on this experience and on observations made during the workshops, we conclude that Gagne's Conditions of Learning instructional design theory provides a useful framework for developing bioinformatics training, but may not be optimal as a method for teaching it.

Computational Biology↗
PubMed2003

Bioinformatics in neurosurgery.

WITH THE COMPLETION of the Human Genome Project, the amount of molecular biological sequence data available in public databases has reached staggering proportions. Data continue to accumulate at an exponential rate in the postgenomic era. Compilation, storage, searching, sharing, studying, and transmitting of all these data present formidable challenges. To keep pace with this extant database, the science of bioinformatics (sometimes called computational biology) has evolved. Bioinformatics is the combination of biology and computers and usually involves the storage or analysis of molecular biological sequence data at either the deoxyribonucleic acid, ribonucleic acid, or protein (amino acid) level. Most bioinformatics tools are freely available on the Internet for use by investigators around the globe. The collective wisdom from bioinformatics databases worldwide will continue to spawn advances in the neurological sciences for generations to come. Neurosurgeons must be aware of the power and potential applications of bioinformatics for the analysis of neurosurgical diseases.

Animals↗
PubMed2001

What is bioinformatics? A proposed definition and overview of the field.

BACKGROUND: The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. OBJECTIVES: Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems. METHODS: Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. RESULTS AND CONCLUSIONS: Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (e.g. expression data). Additional information includes the text of scientific papers and "relationship data" from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.

Computational Biology↗
PubMed2002

[What is bioinformatics?].

Yet bioinformatics has neither clear definition nor proper Japanese equivalent for this word. Bioinformatics is mainly composed of two fields or disciplines; one is computer and information technology as a tool for analyzing massive data such as genome and proteome, and the other one is research for the integrated biology based on the knowledge of genome. Classical and narrow-sense bioinformatics corresponds to the former. The latter called new bioinformatics aims to understand living thing as a whole, where both genome information and mathematical model play complimentary roles. From now the postgenome area on, this new bioinformatics may give novel perspective about living thing, biology and human.

Computational Biology↗
PubMed2003

Informatics united: exemplary studies combining medical informatics, neuroinformatics and bioinformatics.

OBJECTIVES: Medical informatics, neuroinformatics and bioinformatics provide a wide spectrum of research. Here, we show the great potential of synergies between these research areas on the basis of four exemplary studies where techniques are transferred from one of the disciplines to the other. METHODS: Reviewing and analyzing exemplary and specific projects at the intersection of medical informatics, neuroinformatics, and bioinformatics from our experience in an interdisciplinary research group. RESULTS: Synergy emerges when techniques and solutions from medical informatics, bioinformatics, or neuroinformatics are successfully applied in one of the other disciplines. Synergy was found in 1. the modeling of neurophysiological systems for medical therapy development, 2. the use of image processing techniques from medical computer vision for the analysis of the dynamics of cell nuclei, and 3. the application of neuroinformatics tools for data mining in bioinformatics and as classifiers in clinical oncology. CONCLUSIONS: Each of the three different disciplines have delivered technologies that are readily applicable in the other disciplines. The mutual transfer of knowledge and techniques proved to increase efficiency and accuracy in a manifold of applications. In particular, we expect that clinical decision support systems based on techniques derived from neuro- and bioinformatics have the potential to improve medical diagnostics and will finally lead to a personalized delivery of healthcare.

Computational Biology↗
PubMed2004

Towards clinical bioinformatics: advancing genomic medicine with informatics methods and tools.

OBJECTIVES: To summarize the challenges facing clinical applications in the light of growing research results in genomic medicine and bioinformatics. METHODS: Analysis of the contents of the Yearbook of Medical Informatics 2004 of the International Medical Informatics Association (IMIA). RESULTS: The Yearbook of Medical Informatics 2004 includes 32 articles selected from 22 peer-reviewed scientific journals. A special section on clinical bioinformatics highlights recent developments in this field. Several guest editors review the promises and limitations of available methods and resources from biomedical informatics that are relevant to clinical medicine. Integrated data and knowledge resources are generally regarded to be central and key issues for clinical bioinformatics. Further review papers deal with public health implications of bioinformatics, knowledge management and trends in health care education. The Yearbook includes for the first time a section on the history of medical informatics, where the significant impact of the Reisensburg protocol 1973 on international health and medical informatics education is examined. CONCLUSIONS: Close collaboration between bioinformatics and medical informatics researchers can contribute to new insights in genomic medicine and contribute towards the more efficient and effective use of genomic data to advance clinical care.

Computational Biology↗
PubMed2006

Vignettes: Diverse library staff offering diverse bioinformatics services.

OBJECTIVES: The paper gives examples of the bioinformatics services provided in a variety of different libraries by librarians with a broad range of educational background and training. METHODS: Two investigators sent an email inquiry to attendees of the "National Center for Biotechnology Information's (NCBI) Introduction to Molecular Biology Information Resources" or "NCBI Advanced Workshop for Bioinformatics Information Specialists (NAWBIS)" courses. The thirty-five-item questionnaire addressed areas such as educational background, library setting, types and numbers of users served, and bioinformatics training and support services provided. Answers were compiled into program vignettes. DISCUSSION: The bioinformatics support services addressed in the paper are based in libraries with academic and clinical settings. Services have been established through different means: in collaboration with biology faculty as part of formal courses, through teaching workshops in the library, through one-on-one consultations, and by other methods. Librarians with backgrounds from art history to doctoral degrees in genetics have worked to establish these programs. CONCLUSION: Successful bioinformatics support programs can be established in libraries in a variety of different settings and by staff with a variety of different backgrounds and approaches.

Computational Biology↗
PubMed2006

Carving a niche: establishing bioinformatics collaborations.

OBJECTIVES: The paper describes collaborations and partnerships developed between library bioinformatics programs and other bioinformatics-related units at four academic institutions. METHODS: A call for information on bioinformatics partnerships was made via email to librarians who have participated in the National Center for Biotechnology Information's Advanced Workshop for Bioinformatics Information Specialists. Librarians from Harvard University, the University of Florida, the University of Minnesota, and Vanderbilt University responded and expressed willingness to contribute information on their institutions, programs, services, and collaborating partners. Similarities and differences in programs and collaborations were identified. RESULTS: The four librarians have developed partnerships with other units on their campuses that can be categorized into the following areas: knowledge management, instruction, and electronic resource support. All primarily support freely accessible electronic resources, while other campus units deal with fee-based ones. These demarcations are apparent in resource provision as well as in subsequent support and instruction. CONCLUSIONS AND RECOMMENDATIONS: Through environmental scanning and networking with colleagues, librarians who provide bioinformatics support can develop fruitful collaborations. Visibility is key to building collaborations, as is broad-based thinking in terms of potential partners.

Computational Biology↗
PubMed2026

Unveiling the Molecular Secrets of Seaweeds: A Comprehensive Review of Bioinformatics Applications in Algal Research.

Recent advances in high-throughput sequencing, bioinformatics, and multi-omics technologies have transformed seaweed research by overcoming long-standing challenges associated with complex genomes, diverse life cycles, and limited genomic resources. This review provides a comprehensive overview of bioinformatics approaches used to investigate seaweed genomics, transcriptomics, proteomics, metabolomics, microbiomes, and functional genomics, with emphasis on the computational tools and databases that support these analyses. Applications of bioinformatics in phylogenetics, drug discovery, microbiome characterization, and the development of biofuels, nutraceuticals, pharmaceuticals, and sustainable agriculture are also discussed. Particular attention is given to emerging strategies involving multi-omics integration, genome editing, artificial intelligence, machine learning, and synthetic biology that are reshaping seaweed research. The review further examines current challenges, including incomplete genomic resources, data standardization, and the need for experimental validation of computational predictions. Collectively, these advances highlight the growing role of bioinformatics in enabling systems-level understanding of seaweed biology and accelerating their translation into sustainable biotechnological and marine bioeconomy applications.

macroalgal genomics↗
PubMed2001

Medical informatics and bioinformatics: European efforts to facilitate synergy.

Over the past decade there have been several attempts to rethink the basic strategies and scope of medical informatics. Meanwhile, bioinformatics has only recently experienced a similar debate about its scientific character. Both disciplines envision the development of novel diagnostic, therapeutic, and management tools, and products for patient care. A combination of the expertise of medical informatics in developing clinical applications and the focused principles that have guided bioinformatics could create a synergy between the two areas of application. Such interaction could have a great influence on future health research and the ultimate goal, namely continuity and individualization of health care. This article summarizes current activities related to facilitating synergy between medical informatics and bioinformatics, emphasizing activities in Europe while relating them to efforts in other parts of the world. The report provides examples of the analysis that European investigators are carrying out, aiming to propose new ideas for collaborations between medical informatics and bioinformatics researchers in a variety of areas.

Computational Biology↗
PubMed1996

Company strategies for using bioinformatics.

Bioinformatics enables biotechnology companies to access and analyse their growing databases of experimental results, and to exploit public data from genome programmes and other sources. Traditionally occupying the domain of a 'guru' supplying answers to infrequent research questions, corporate bioinformatics is breaking down under the flood of data. New, more robust, professional and expandable systems will give scientists effective access to new tools. This review outlines how companies have evolved beyond the 'guru', and have organized their bioinformatics by acquiring or developing bioinformatics resources. It also describes why the biologist must be central to this process, and why this is a problem for computer professionals to solve, not for 'gurus'.

Biotechnology↗
PubMed2004

Characterization of genomic organization of the adenosine A2A receptor gene by molecular and bioinformatics analyses.

The adenosine A(2A) receptor (A(2A)R) is abundantly expressed in brain and emerging as an important therapeutic target for Parkinson's disease and potentially other neuropsychiatric disorders. To understand the molecular mechanisms of A(2A)R gene expression, we have characterized the genomic organization of the mouse and human A(2A)R genes by molecular and bioinformatic analyses. Three new exons (m1A, m1B and m1C) encoding the 5' untranslated regions (5'-UTRs) of mouse A(2A)R mRNA were identified by rapid amplification of 5' cDNA end (5' RACE), RT-PCR analysis and genome sequence analyses. Similar bioinformatics analysis also suggested six variants of the non-coding "exon 1" (h1A, h1B, h1C, h1D, h1E and h1F) in the human A(2A)R gene, which were confirmed by RT-PCR analysis, while three of the human exon 1 variants (h1D, h1E and h1F) were likewise verified by 5' oligonucleotide capping analysis suggesting multiple transcription start sites. Importantly, RT-PCR and quantitative PCR analysis demonstrated that the A(2A)R transcripts with different exon 1 variants displayed tissue-specific expression patterns. For instance, the mouse exon m1A mRNA was detected only in brain (specifically striatum) and the human exon h1D mRNA in lymphoreticular system. Furthermore, the determination of the three new transcription start sites of human A(2A)R gene by 5' oligonucleotide capping and bioinformatics analyses led to the identification of three corresponding promoter regions which contain several important cis elements, providing additional target for further molecular dissection of A(2A)R gene expression. Finally, our analysis indicates that A(2A)R mRNA and a novel transcript partially overlapping with the 3' exon h3, but in opposite orientation to the A(2A)R gene, could conceivably form duplexes to mutually regulate transcript expression. Thus, combined molecular and bioinformatics analyses revealed a new A(2A)R genomic structure, with conserved coding exons 2 and 3 and divergent, tissue-specific exon 1 variants encoding for 5'-UTR. This raises the possibility of generating multiple tissue-specific A(2A)R mRNA species by alternative promoters with varying regulatory susceptibility.

Animals↗
PubMed2005

KDE Bioscience: platform for bioinformatics analysis workflows.

Bioinformatics is a dynamic research area in which a large number of algorithms and programs have been developed rapidly and independently without much consideration so far of the need for standardization. The lack of such common standards combined with unfriendly interfaces make it difficult for biologists to learn how to use these tools and to translate the data formats from one to another. Consequently, the construction of an integrative bioinformatics platform to facilitate biologists' research is an urgent and challenging task. KDE Bioscience is a java-based software platform that collects a variety of bioinformatics tools and provides a workflow mechanism to integrate them. Nucleotide and protein sequences from local flat files, web sites, and relational databases can be entered, annotated, and aligned. Several home-made or 3rd-party viewers are built-in to provide visualization of annotations or alignments. KDE Bioscience can also be deployed in client-server mode where simultaneous execution of the same workflow is supported for multiple users. Moreover, workflows can be published as web pages that can be executed from a web browser. The power of KDE Bioscience comes from the integrated algorithms and data sources. With its generic workflow mechanism other novel calculations and simulations can be integrated to augment the current sequence analysis functions. Because of this flexible and extensible architecture, KDE Bioscience makes an ideal integrated informatics environment for future bioinformatics or systems biology research.

Biological Science Disciplines↗
PubMed2003

New trends in bioinformatics: from genome sequence to personalized medicine.

Molecular medicine requires the integration and analysis of genomic, molecular, cellular, as well as clinical data and it thus offers a remarkable set of challenges to bioinformatics. Bioinformatics nowadays has an essential role both, in deciphering genomic, transcriptomic, and proteomic data generated by high-throughput experimental technologies, and in organizing information gathered from traditional biology and medicine. The evolution of bioinformatics, which started with sequence analysis and has led to high-throughput whole genome or transcriptome annotation today, is now going to be directed towards recently emerging areas of integrative and translational genomics, and ultimately personalized medicine.Therefore considerable efforts are required to provide the necessary infrastructure for high-performance computing, sophisticated algorithms, advanced data management capabilities, and-most importantly-well trained and educated personnel to design, maintain and use these environments. This review outlines the most promising trends in bioinformatics, which may play a major role in the pursuit of future biological discoveries and medical applications.

Computational Biology↗
PubMed1999

The Emerging Importance of Genetics in Epidemiologic Research III. Bioinformatics and statistical genetic methods.

PURPOSE: To outline potential benefits of integrating recent developments in bioinformatics and statistical genetics with traditional epidemiologic studies to localize genes influencing complex phenotypes and examine genetic effects on disease susceptibility. METHODS: An overview of bioinformatic and statistical approaches for localizing disease-susceptibility genes as well as challenges associated with identifying functional DNA variants and context-dependent genetic effects concludes this three-part series on the importance of genetics in epidemiologic research. RESULTS: Rapidly evolving bioinformatic and statistical methods are providing invaluable information on newly-discovered genes and molecular variation influencing human diseases that is readily available to epidemiologic researchers. CONCLUSIONS: Integrating bioinformatics and molecular biotechnology with epidemiologic methods of assessing disease risk is rapidly expanding our ability to identify genetic influences on complex human diseases. These technological advances are likely to have a profound impact on current knowledge of complex disease etiology, and may reveal novel approaches to disease treatment and prevention.

Data Interpretation, Statistical↗