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Warren A Kibbe

Publications and source records attributed to Warren A Kibbe.

12 recordsLinked to original sources
PubMed2006

Xanthusbase: adapting wikipedia principles to a model organism database.

xanthusBase (http://www.xanthusbase.org) is the official model organism database (MOD) for the social bacterium Myxococcus xanthus. In many respects, M.xanthus represents the pioneer model organism (MO) for studying the genetic, biochemical, and mechanistic basis of prokaryotic multicellularity, a topic that has garnered considerable attention due to the significance of biofilms in both basic and applied microbiology research. To facilitate its utility, the design of xanthusBase incorporates open-source software, leveraging the cumulative experience made available through the Generic Model Organism Database (GMOD) project, MediaWiki (http://www.mediawiki.org), and dictyBase (http://www.dictybase.org), to create a MOD that is both highly useful and easily navigable. In addition, we have incorporated a unique Wikipedia-style curation model which exploits the internet's inherent interactivity, thus enabling M.xanthus and other myxobacterial researchers to contribute directly toward the ongoing genome annotation.

Databases, Nucleic Acid↗
PubMed2006

Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching.

MOTIVATION: A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. RESULTS: Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. AVAILABILITY: The algorithm is implemented in R and will be included as an open source module in the Bioconductor project.

Algorithms↗
PubMed2006

dictyBase, the model organism database for Dictyostelium discoideum.

dictyBase (http://dictybase.org) is the model organism database (MOD) for the social amoeba Dictyostelium discoideum. The unique biology and phylogenetic position of Dictyostelium offer a great opportunity to gain knowledge of processes not characterized in other organisms. The recent completion of the 34 MB genome sequence, together with the sizable scientific literature using Dictyostelium as a research organism, provided the necessary tools to create a well-annotated genome. dictyBase has leveraged software developed by the Saccharomyces Genome Database and the Generic Model Organism Database project. This has reduced the time required to develop a full-featured MOD and greatly facilitated our ability to focus on annotation and providing new functionality. We hope that manual curation of the Dictyostelium genome will facilitate the annotation of other genomes.

Animals↗
PubMed2006

Improved prediction of treatment response using microarrays and existing biological knowledge.

A desired application for microarrays in the clinic is to predict treatment response from an often diverse patient population. We present a method for analyzing microarray data that is predicated on biological pathway and function knowledge as opposed to a purely data-driven initial analysis. From an analysis perspective, this methodology takes advantage of information that is available across genes on a single array, as well as differences in those patterns across measurements. By using biological knowledge in the initial analysis, the accuracy and robustness of microarray profile classification is enhanced, especially when low numbers of samples are available. For clinical studies, particularly Phase I or I/II studies, this technique is exceptionally advantageous.

Asparaginase↗
PubMed2005

Annotating nonspecific SAGE tags with microarray data.

SAGE (serial analysis of gene expression) detects transcripts by extracting short tags from the transcripts. Because of the limited length, many SAGE tags are shared by transcripts from different genes. Relying on sequence information in the general gene expression database has limited power to solve this problem due to the highly heterogeneous nature of the deposited sequences. Considering that the complexity of gene expression at a single tissue level should be much simpler than that in the general expression database, we reasoned that by restricting gene expression to tissue level, the accuracy of gene annotation for the nonspecific SAGE tags should be significantly improved. To test the idea, we developed a tissue-specific SAGE annotation database based on microarray data (). This database contains microarray expression information represented as UniGene clusters for 73 normal human tissues and 18 cancer tissues and cell lines. The nonspecific SAGE tag is first matched to the database by the same tissue type used by both SAGE and microarray analysis; then the multiple UniGene clusters assigned to the nonspecific SAGE tag are searched in the database under the matched tissue type. The UniGene cluster presented solely or at higher expression levels in the database is annotated to represent the specific gene for the nonspecific SAGE tags. The accuracy of gene annotation by this database was largely confirmed by experimental data. Our study shows that microarray data provide a useful source for annotating the nonspecific SAGE tags.

Cell Line↗
PubMed2005

The case for strategic international alliances to harness nutritional genomics for public and personal health.

Nutrigenomics is the study of how constituents of the diet interact with genes, and their products, to alter phenotype and, conversely, how genes and their products metabolise these constituents into nutrients, antinutrients, and bioactive compounds. Results from molecular and genetic epidemiological studies indicate that dietary unbalance can alter gene-nutrient interactions in ways that increase the risk of developing chronic disease. The interplay of human genetic variation and environmental factors will make identifying causative genes and nutrients a formidable, but not intractable, challenge. We provide specific recommendations for how to best meet this challenge and discuss the need for new methodologies and the use of comprehensive analyses of nutrient-genotype interactions involving large and diverse populations. The objective of the present paper is to stimulate discourse and collaboration among nutrigenomic researchers and stakeholders, a process that will lead to an increase in global health and wellness by reducing health disparities in developed and developing countries.

Animals↗
PubMed2005

What is mzXML good for?

mzXML (extensible markup language) is one of the pioneering data formats for mass spectrometry-based proteomics data collection. It is an open data format that has benefited and evolved as a result of the input of many groups, and it continues to evolve. Due to its dynamic history, its structure, purpose and applicability have all changed with time, meaning that groups that have looked at the standard at different points during its evolution have differing impressions of the usefulness of mzXML. In discussing mzXML, it is important to understand what mzXML is not. First, mzXML does not capture the raw data. Second, mzXML is not sufficient for regulatory submission. Third, mzXML is not optimized for computation and, finally, mzXML does not capture the experiment design. In general, it is the authors' opinion that XML is not a panacea for bioinformatics or a substitute for good data representation, and groups that want to use mzXML (or other XML-based representations) directly for data storage or computation will encounter performance and scalability problems. With these limitations in mind, the authors conclude that mzXML is, nonetheless, an indispensable data exchange format for proteomics.

Computational Biology↗
PubMed2004

Large-scale mutagenesis of the mouse to understand the genetic bases of nervous system structure and function.

N-ethyl-N-nitrosourea (ENU) mutagenesis is presented as a powerful approach to developing models for human disease. The efforts of three NIH Mutagenesis Centers established for the detection of neuroscience-related phenotypes are described. Each center has developed an extensive panel of phenotype screens that assess nervous system structure and function. In particular, these screens focus on complex behavioral traits from drug and alcohol responses to circadian rhythms to epilepsy. Each of these centers has developed a bioinformatics infrastructure to track the extensive number of transactions that are inherent in these large-scale projects. Over 100 new mouse mutant lines have been defined through the efforts of these three mutagenesis centers and are presented to the research community via the centralized Web presence of the Neuromice.org consortium (http://www.neuromice.org). This community resource provides visitors with the ability to search for specific mutant phenotypes, to view the genetic and phenotypic details of mutant mouse lines, and to order these mice for use in their own research program.

Alkylating Agents↗
PubMed2004

Identification of genes contributing to the obese yellow Avy phenotype: caloric restriction, genotype, diet x genotype interactions.

The incidence and severity of obesity and type 2 diabetes are increasing in Western societies. The progression of obesity to type 2 diabetes is gradual with overlapping symptoms of insulin resistance, hyperinsulinemia, hyperglycemia, dyslipidemias, ion imbalance, and inflammation; this complex syndrome has been called diabesity. We describe here comparisons of gene expression in livers of A/a (agouti) vs. A(vy)/A (obese yellow) segregants (i.e., littermates) from BALB/cStCrlfC3H/Nctr x VYWffC3Hf/Nctr-A(vy)/a matings in response to 70% and 100% of ad libitum caloric intakes of a reproducible diet. Twenty-eight (28) genes regulated by diet, genotype, or diet x genotype interactions mapped to diabesity quantitative trait loci. A subset of the identified genes is linked to abnormal physiological signs observed in obesity and diabetes.

Animals↗
PubMed2004

dictyBase: a new Dictyostelium discoideum genome database.

Dictyostelium discoideum is a powerful and genetically tractable model system used for the study of numerous cellular molecular mechanisms including chemotaxis, phagocytosis and signal transduction. The past 2 years have seen a significant expansion in the scope and accessibility of online resources for Dictyostelium. Recent advances have focused on the development of a new comprehensive online resource called dictyBase (http://dictybase.org). This database not only provides access to genomic data including functional annotation of genes, gene products and chromosomal mapping, but also to extensive biological information such as mutant phenotypes and corresponding reference material. In conjunction with additional sites (http://genome. imb-jena.de/dictyostelium/, http://dictyensembl. bioch.bcm.tmc.edu and http://www.sanger.ac.uk/Projects/D_discoideum/) from the genome sequencing and assembly centers, these improvements have expanded the scope of the Dictyostelium databases making them accessible and useful to any researcher interested in comparative and functional genomics in metazoan organisms.

Animals↗