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At least 577 records · Page 32Linked to original sources

PubMed2005

An inquiry into protein structure and genetic disease: introducing undergraduates to bioinformatics in a large introductory course.

This inquiry-based lab is designed around genetic diseases with a focus on protein structure and function. To allow students to work on their own investigatory projects, 10 projects on 10 different proteins were developed. Students are grouped in sections of 20 and work in pairs on each of the projects. To begin their investigation, students are given a cDNA sequence that translates into a human protein with a single mutation. Each case results in a genetic disease that has been studied and recorded in the Online Mendelian Inheritance in Man (OMIM) database. Students use bioinformatics tools to investigate their proteins and form a hypothesis for the effect of the mutation on protein function. They are also asked to predict the impact of the mutation on human physiology and present their findings in the form of an oral report. Over five laboratory sessions, students use tools on the National Center for Biotechnology Information (NCBI) Web site (BLAST, LocusLink, OMIM, GenBank, and PubMed) as well as ExPasy, Protein Data Bank, ClustalW, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the structure-viewing program DeepView. Assessment results showed that students gained an understanding of the Web-based databases and tools and enjoyed the investigatory nature of the lab.

Algorithms↗

PubMed2004

Bioinformatics strategies for translating genome-wide expression analyses into clinically useful cancer markers.

The DNA microarray has revolutionized cancer research. Now, scientists can obtain a genome-wide perspective of cancer gene expression. One potential application of this technology is the discovery of novel cancer biomarkers for more accurate diagnosis and prognosis, and potentially for the earlier detection of disease or the monitoring of treatment effectiveness. Because microarray experiments generate a tremendous amount of data and because the number of laboratories generating microarray data is rapidly growing, new bioinformatics strategies that promote the maximum utilization of such data are necessary. Here, we describe a method to validate multiple microarray data sets, a Web-based cancer microarray database for biomarker discovery, and methods for integrating gene ontology annotations with microarray data to improve candidate biomarker selection.

Biomarkers, Tumor↗

PubMed2004

Application of bioinformatics in cancer epigenetics.

With the completion of the human genome sequence and the advent of high-throughput genomics-based technologies, it is now possible to study the entire human genome and epigenome. The challenge in the next decade of biomedical research is to functionally annotate the genome, epigenome, transcriptome, and proteome. High-throughput genome technology has already produced massive amounts of data including genome sequences, single nucleotide polymorphisms, and microarray gene expression. Our ability to manage and analyze data needs to match the speed of data acquisition. We will summarize our studies of allele-specific gene expression using genomic and computational approaches and identification of sequence motifs that are signature of imprinted genes. We will also discuss about how bioinformatics can facilitate epigenetic researches.

Computational Biology↗

PubMed2004

Bioinformatics tools for single nucleotide polymorphism discovery and analysis.

Single nucleotide polymorphisms (SNPs) are a valuable resource for investigating the genetic basis of disease. These variants can serve as markers for fine-scale genetic mapping experiments and genome-wide association studies. Certain of these nucleotide polymorphisms may predispose individuals to illnesses such as diabetes, hypertension, or cancer, or affect disease progression. Bioinformatics techniques can play an important role in SNP discovery and analysis. We use computational methods to identify SNPs and to predict whether they are likely to be neutral or deleterious. We also use informatics to annotate genes that contain SNPs. To make this information available to the research community, we provide a variety of Internet-accessible tools for data access and display. These tools allow researchers to retrieve data about SNPs based on gene of interest, genetic or physical map location, or expression pattern.

Chromosome Mapping↗

PubMed2003

Bioinformatics and medical informatics: collaborations on the road to genomic medicine?

In this report, the authors compare and contrast medical informatics (MI) and bioinformatics (BI) and provide a viewpoint on their complementarities and potential for collaboration in various subfields. The authors compare MI and BI along several dimensions, including: (1) historical development of the disciplines, (2) their scientific foundations, (3) data quality and analysis, (4) integration of knowledge and databases, (5) informatics tools to support practice, (6) informatics methods to support research (signal processing, imaging and vision, and computational modeling, (7) professional and patient continuing education, and (8) education and training. It is pointed out that, while the two disciplines differ in their histories, scientific foundations, and methodologic approaches to research in various areas, they nevertheless share methods and tools, which provides a basis for exchange of experience in their different applications. MI expertise in developing health care applications and the strength of BI in biological "discovery science" complement each other well. The new field of biomedical informatics (BMI) holds great promise for developing informatics methods that will be crucial in the development of genomic medicine. The future of BMI will be influenced strongly by whether significant advances in clinical practice and biomedical research come about from separate efforts in MI and BI, or from emerging, hybrid informatics subdisciplines at their interface.

Biomedical Research↗

PubMed2003

Beyond the literature: bioinformatics training for medical librarians.

As genetics and molecular biology progressively impact on society and the practice of medicine, medical librarians must prepare themselves to deal with this new arena of information. The Eskind Biomedical Library at Vanderbilt University Medical Center has developed a bioinformatics training program for its librarians involving subject knowledge, literature evaluation, and database searching techniques. This program can serve as a model for other libraries.

Computational Biology↗

PubMed2006

Bioinformatics and multiepitope DNA immunization to design rational snake antivenom.

BACKGROUND: Snake venom is a potentially lethal and complex mixture of hundreds of functionally diverse proteins that are difficult to purify and hence difficult to characterize. These difficulties have inhibited the development of toxin-targeted therapy, and conventional antivenom is still generated from the sera of horses or sheep immunized with whole venom. Although life-saving, antivenoms contain an immunoglobulin pool of unknown antigen specificity and known redundancy, which necessitates the delivery of large volumes of heterologous immunoglobulin to the envenomed victim, thus increasing the risk of anaphylactoid and serum sickness adverse effects. Here we exploit recent molecular sequence analysis and DNA immunization tools to design more rational toxin-targeted antivenom. METHODS AND FINDINGS: We developed a novel bioinformatic strategy that identified sequences encoding immunogenic and structurally significant epitopes from an expressed sequence tag database of a venom gland cDNA library of Echis ocellatus, the most medically important viper in Africa. Focusing upon snake venom metalloproteinases (SVMPs) that are responsible for the severe and frequently lethal hemorrhage in envenomed victims, we identified seven epitopes that we predicted would be represented in all isomers of this multimeric toxin and that we engineered into a single synthetic multiepitope DNA immunogen (epitope string). We compared the specificity and toxin-neutralizing efficacy of antiserum raised against the string to antisera raised against a single SVMP toxin (or domains) or antiserum raised by conventional (whole venom) immunization protocols. The SVMP string antiserum, as predicted in silico, contained antibody specificities to numerous SVMPs in E. ocellatus venom and venoms of several other African vipers. More significantly, the antiserum cross-specifically neutralized hemorrhage induced by E. ocellatus and Cerastes cerastes cerastes venoms. CONCLUSIONS: These data provide valuable sequence and structure/function information of viper venom hemorrhagins but, more importantly, a new opportunity to design toxin-specific antivenoms-the first major conceptual change in antivenom design after more than a century of production. Furthermore, this approach may be adapted to immunotherapy design in other cases where targets are numerous, diverse, and poorly characterized such as those generated by hypermutation or antigenic variation.

Amino Acid Sequence↗

PubMed2024

Effect of light on ascorbic acid biosynthesis and bioinformatics analysis of related genes in Chinese chives.

Ascorbic acid (AsA) is an essential nutritional component and powerful antioxidant in vegetables, and in plants, AsA levels are regulated by light. AsA levels in the leaves of Chinese chive (Allium tuberosum Rottler ex Spr), a popular vegetable, are poorly understood. Thus, this study was performed to assess the influence of light on AsA biosynthesis in chive and select related genes (AtuGGP1 and AtuGME1); in addition, bioinformatic analyses and gene expression level assays were performed. The biological information obtained for AtuGGP1 and AtuGME1 was analysed with several tools, including NCBI, DNAMAN, and MEGA11. After different light treatments were performed, the Chive AsA content and AtuGGP1 and AtuGME1 expression levels were determined. These results suggest that 1) compared with natural light, continuous darkness inhibited AsA synthesis in chives. 2) The amino acid sequences of AtuGGP1 and AtuGME1 are very similar to those of other plants. 3) The trends observed for the expression levels of AtuGGP1 and AtuGME1 were consistent with the AsA content observed in chives. Hence, we speculated that light controls AsA biosynthesis in chives by regulating AtuGGP1 and AtuGME1 expression. This study provided impactful and informative evidence regarding the functions of GGP and GME in chives.

Ascorbic Acid↗

PubMed2005

Bioinformatic approaches to assigning protein function from novel sequence data.

The current pace of functional genomic initiatives and genome sequencing projects has provided researchers with a bewildering array of sequence and biological data to analyze. The disease system-driven approach to identifying key genes frequently identifies nucleotide and protein sequences for which the gene and protein function are not known in sufficient detail to allow informed follow-up. Using a range of bioinformatic tools and sequence-based clues, most of unassigned sequences can now be annotated. This chapter takes as an example an unannotated expressed sequence tag, describing how to identify its related gene, and how to annotate the encoded protein using sequence, profile, and structure-based annotation methodologies.

Computational Biology↗

PubMed2006

Clinical applications of bioinformatics, genomics, and pharmacogenomics.

Elucidation of the entire human genomic sequence is one of the greatest achievements of science. Understanding the functional role of 30,000 human genes and more than 2 million polymorphisms was possible through a multidisciplinary approach using micro-arrays and bioinformatics. Polymorphisms, variations in DNA sequences, occur in 1% of the population, and a vast majority of them are single nucleotide polymorphisms. Genotype analysis has identified genes important in thrombosis, cardiac defects, and risk of cardiac disease. Many of the genes show a significant correlation with polymorphisms and the incidence of coronary artery disease and heart failure. In this chapter, the application of current state-of-the-art genomic analysis to a variety of these disorders is reviewed.

Computational Biology↗

PubMed2006

Postgenomic bioinformatic analysis of yeast artificial chromosome sequence.

The free availability of multiple genomic sequences represents one of the greatest advances in biology of the new millennium, and promises to revolutionize our ability to determine and treat the causes of human disease. This chapter highlights a number of basic, freely available, and user-friendly bioinformatic techniques that can be used to predict the functional genetic contents of specific yeast artificial chromosome (YAC) clones. The content of this chapter is written for the level of graduate students, who may be relatively inexperienced with the use of computers for analyzing DNA sequences. The basic instructions that allow the identification of the genomic sequence of interest and to download this sequence onto a personal computer from an online database are presented. Simple instructions are also given on how to perform basic sequence manipulations, how to use online tools to design polymerase chain reaction primers, and how to map restriction sites. Also described are more complicated programs that rapidly and efficiently perform genome alignments that, in addition to predicting the location of protein coding sequences, allow the prediction of functional genomic sequences, such as cis regulatory elements and scaffold/matrix attachment sites. The availability of genomic sequences and the rapidly expanding numbers of predictive programs that allow the predictive analysis of these sequences promises to greatly facilitate the use of YAC clones in the search for the causes of disease.

Chromosomes, Artificial, Yeast↗

PubMed2001

The BioTools Suite. A comprehensive suite of platform-independent bioinformatics tools.

The BioTools Suite is a set of three comprehensive, platform-independent software packages (PepTool, GeneTool, and ChromaTool) developed for sequence assembly and analysis. In addition to supporting a large number of standard bioinformatics functions, these programs also incorporate a number of useful innovations including uniform graphical-user interface (GUI) design, direct internet connectivity, a novel approach to feature annotation, and a variety of enhanced algorithms for large scale proteome and genome analysis. This article describes the key features, recent changes, and general operation of all three programs.

Computational Biology↗

PubMed2003

Bioinformatics methods to predict protein structure and function. A practical approach.

Protein structure prediction by using bioinformatics can involve sequence similarity searches, multiple sequence alignments, identification and characterization of domains, secondary structure prediction, solvent accessibility prediction, automatic protein fold recognition, constructing three-dimensional models to atomic detail, and model validation. Not all protein structure prediction projects involve the use of all these techniques. A central part of a typical protein structure prediction is the identification of a suitable structural target from which to extrapolate three-dimensional information for a query sequence. The way in which this is done defines three types of projects. The first involves the use of standard and well-understood techniques. If a structural template remains elusive, a second approach using nontrivial methods is required. If a target fold cannot be reliably identified because inconsistent results have been obtained from nontrivial data analyses, the project falls into the third type of project and will be virtually impossible to complete with any degree of reliability. In this article, a set of protocols to predict protein structure from sequence is presented and distinctions among the three types of project are given. These methods, if used appropriately, can provide valuable indicators of protein structure and function.

Algorithms↗

PubMed2005

Databases, models, and algorithms for functional genomics: a bioinformatics perspective.

A variety of patterns have been observed on the DNA and protein sequences that serve as control points for gene expression and cellular functions. Owing to the vital role of such patterns discovered on biological sequences, they are generally cataloged and maintained within internationally shared databases. Furthermore,the variability in a family of observed patterns is often represented using computational models in order to facilitate their search within an uncharacterized biological sequence. As the biological data is comprised of a mosaic of sequence-levels motifs, it is significant to unravel the synergies of macromolecular coordination utilized in cell-specific differential synthesis of proteins. This article provides an overview of the various pattern representation methodologies and the surveys the pattern databases available for use to the molecular biologists. Our aim is to describe the principles behind the computational modeling and analysis techniques utilized in bioinformatics research, with the objective of providing insight necessary to better understand and effectively utilize the available databases and analysis tools. We also provide a detailed review of DNA sequence level patterns responsible for structural conformations within the Scaffold or Matrix Attachment Regions (S/MARs).

Animals↗

PubMed2001

Cluster analysis and promoter modelling as bioinformatics tools for the identification of target genes from expression array data.

Expression arrays yield enormous amounts of data linking genes, via their cDNA sequences, to gene expression patterns. This now allows the characterisation of gene expression in normal and diseased tissues, as well as the response of tissues to the application of therapeutic reagents. Expression array data can be analysed with respect to the underlying protein sequences, which facilitates the precise determination of when and where certain groups of genes are expressed. More recent developments of clustering algorithms take additional parameters of the experimental set-up into account, focusing more directly on co-regulated set of genes. However, the information concerning transcriptional regulatory networks responsible for the observed expression patterns is not contained within the cDNA sequences used to generate the arrays. Regulation of expression is determined to a large extent by the promoter sequences of the individual genes (and/or enhancers). The complete sequence of the human genome now provides the molecular basis for the identification of many regulatory regions. Promoter sequences for specific cDNAs can be obtained reliably from genomic sequences by exon mapping. In the many cases in which cDNAs are 5'-incomplete, high quality promoter prediction tools can be used to locate promoters directly in the genomic sequence. Once sufficient numbers of promoter sequences have been obtained, a comparative promoter analysis of the co-regulated genes and groups of genes can be applied in order to generate models describing the higher order levels of transcription factor binding site organisation within these promoter regions. Such modules represent the molecular mechanisms through which regulatory networks influence gene expression, and candidates can be determined solely by bioinformatics. This approach also provides a powerful alternative for elucidating the functional features of genes with no detectable sequence similarity, by linking them to other genes on the basis of their common promoter structures.

Algorithms↗

PubMed2004

Functional informatics: convergence and integration of automation and bioinformatics.

The biopharmaceutical industry is currently being presented with opportunities to improve research and business efficiency via automation and the integration of various systems. In the examples discussed, industrial high-throughput screening systems are integrated with functional tools and bioinformatics to facilitate target and biomarker identification and validation. These integrative functional approaches generate value-added opportunities by leveraging available automation and information technologies into new applications that are broadly applicable to different types of projects, and by improving the overall research and development and business efficiency via the integration of various systems.

Automation↗

PubMed2002

Bioinformatics and approaches to identifying polygenic susceptibility traits.

The role of genetic factors in periodontal disease is now well recognized, although details for the genetic mechanisms of the disease and implications for therapy can be as obscure as they are for other human traits. This paper addresses the role that the analysis of genome-wide data might play in helping to understand the molecular determinants of periodontal risk. Very few human diseases are not polygenic, in that an individual's susceptibility depends on his or her constitution at many genetic loci, each of which may have a small effect. Not only do these loci interact, but also their actions and interactions depend on nongenetic factors. Much of the statistical machinery to handle this complexity was developed in the plant and animal breeding context, where crosses between inbred lines selected for trait differences could be conducted. Human polygenic studies began with studies on large pedigrees, but have expanded to include case-control analyses of random samples of individuals who differ in disease status, and studies of marker transmissions within nuclear families. In the area of characterizing the genetic architecture of complex traits, the relatively new field of bioinformatics is distinguished from the more mature fields of statistical genetics or genetic epidemiology by its focus on genome-wide data. The very dense sets of genetic markers now available, particularly those at single nucleotide positions (SNPs), have meant that it is possible to seek linkages or associations between chromosomal position and disease from the whole genome in a single study. Apart from the obvious problems of scale, there are real issues involved with multiple testing and recognizing interactions. Current thinking tends to focus on relatively conserved "haplotype blocks" instead of single genetic markers, although there is no consensus on the utility of this emphasis.

Dental Informatics↗

PubMed2003

Bioinformatics research on inter-racial difference in drug metabolism II. Analysis on relationship between enzyme activities of CYP2D6 and CYP2C19 and their relevant genotypes.

The enzyme activities of CYP2D6 and CYP2C19 show a genetic polymorphism, and the frequency of poor metabolizers (PMs) on these enzymes depends on races. We have analyzed frequencies of mutant alleles and PMs based on the published data in previous study (Shimizu, T. et al.: Bioinformatics research on inter-racial difference in drug metabolism, I. Analysis on frequencies of mutant alleles and poor metabolizers on CYP2D6 and CYP2C19.). The study shows that there were racial differences in the frequencies of each mutant allele and PMs. In the present study, the correlation between genotypes and drug-metabolizing enzyme activities was investigated. The result showed that enzyme activities varied according to the genotypes of subjects even in the same race. On the other hand, if subjects had the same genotypes, almost no racial differences were observed in drug-metabolizing enzyme activities. From these results, it was supposed that the racial differences in activities of these enzymes could be explained by the differences in distribution of genotypes. It would be possible to explain the racial differences in drug-metabolizing enzyme activities based on the differences on individual pharmacogenetic background information, not merely by comparison of frameworks such as races and nations.

Journal Article↗