The beginning of the twenty-first century has been characterized by an explosion of biological information. The avalanche of data grows daily and arises as a consequence of advances in the fields of molecular biology and genomics and proteomics. The challenge for nowadays biologist lies in the de-codification of this huge and complex data, in order to achieve a better understanding of how our genes shape who we are, how our genome evolved, and how we function. Without the annotation and data mining, the information provided by for example high throughput genomic sequencing projects is not very useful. Bioinformatics is the application of computer science and technology to the management and analysis of biological data, in an effort to address biological questions. The work presented in this thesis has focused on the use of Grid and High Performance Computing for solving computationally expensive bioinformatics tasks, where, due to the very large amount of available data and the complexity of the tasks, new solutions are required for efficient data analysis and interpretation. Three major research topics are addressed; First, the use of grids for distributing the execution of sequence based proteomic analysis, its application in optimal epitope selection and in a proteome-wide effort to map the linear epitopes in the human proteome. Second, the application of grid technology in genetic association studies, which enabled the analysis of thousand of simulated genotypes, and finally the development and application of a economic based model for grid-job scheduling and resource administration. The applications of the grid based technology developed in the present investigation, results in successfully tagging and linking chromosomes regions in Alzheimer disease, proteome-wide mapping of the linear epitopes, and the development of a Market-Based Resource Allocation in Grid for Scientific Applications. / QC 20100622

abstract: The greatest barrier to understanding how life interacts with its environment is the complexity in which biology operates. In this work, I present experimental designs, analysis methods, and visualization techniques to overcome the challenges of deciphering complex biological datasets. First, I examine an iron limitation transcriptome of Synechocystis sp. PCC 6803 using a new methodology. Until now, iron limitation in experiments of Synechocystis sp. PCC 6803 gene expression has been achieved through media chelation. Notably, chelation also reduces the bioavailability of other metals, whereas naturally occurring low iron settings likely result from a lack of iron influx and not as a result of chelation. The overall metabolic trends of previous studies are well-characterized but within those trends is significant variability in single gene expression responses. I compare previous transcriptomics analyses with our protocol that limits the addition of bioavailable iron to growth media to identify consistent gene expression signals resulting from iron limitation. Second, I describe a novel method of improving the reliability of centroid-linkage clustering results. The size and complexity of modern sequencing datasets often prohibit constructing distance matrices, which prevents the use of many common clustering algorithms. Centroid-linkage circumvents the need for a distance matrix, but has the adverse effect of producing input-order dependent results. In this chapter, I describe a method of cluster edge counting across iterated centroid-linkage results and reconstructing aggregate clusters from a ranked edge list without a distance matrix and input-order dependence. Finally, I introduce dendritic heat maps, a new figure type that visualizes heat map responses through expanding and contracting sequence clustering specificities. Heat maps are useful for comparing data across a range of possible states. However, data binning is sensitive to clustering cutoffs which are often arbitrarily introduced by researchers and can substantially change the heat map response of any single data point. With an understanding of how the architectural elements of dendrograms and heat maps affect data visualization, I have integrated their salient features to create a figure type aimed at viewing multiple levels of clustering cutoffs, allowing researchers to better understand the effects of environment on metabolism or phylogenetic lineages. / Dissertation/Thesis / Chapter 2 Excel file of transcriptome responses / Chapter 2 Perl scripts / Chapter 3 Cluster Aggregation Perl script / Chapter 4 Example of the top-down clustering method used to construct dendritic heat maps / Chapter 4Perl scripts and dendritic heat map images / Chapter 4 Perl scripts and dendritic heat map images / Doctoral Dissertation Geological Sciences 2017

Being able to predict the sequence-structure relationship in proteins will extend the scope of many bioinformatics tools relying on structure information. Here we use Hidden Markov models (HMM) to recognize and pinpoint the location in target sequences of local structural motifs (local descriptors of protein structure, LDPS) These substructures are composed of three or more segments of amino acid backbone structures that are in proximity with each other in space but not necessarily along the amino acid sequence. We were able to align descriptors to their proper locations in 41.1% of the cases when using models solely built from amino acid information. Using models that also incorporated secondary structure information, we were able to assign 57.8% of the local descriptors to their proper location. Further enhancements in performance was yielded when threading a profile through the Hidden Markov models together with the secondary structure, with this material we were able assign 58,5% of the descriptors to their proper locations. Hidden Markov models were shown to be able to locate LDPS in target sequences, the performance accuracy increases when secondary structure and the profile for the target sequence were used in the models.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / You, Qian. Ph.D., Purdue University, December, 2010. Iterative Visual Analytics and its Applications in Bioinformatics. Major Professors: Shiaofen Fang and Luo Si. Visual Analytics is a new and developing field that addresses the challenges of knowledge discoveries from the massive amount of available data. It facilitates humans‘ reasoning capabilities with interactive visual interfaces for exploratory data analysis tasks, where automatic data mining methods fall short due to the lack of the pre-defined objective functions. Analyzing the large volume of data sets for biological discoveries raises similar challenges. The domain knowledge of biologists and bioinformaticians is critical in the hypothesis-driven discovery tasks. Yet developing visual analytics frameworks for bioinformatic applications is still in its infancy. In this dissertation, we propose a general visual analytics framework – Iterative Visual Analytics (IVA) – to address some of the challenges in the current research. The framework consists of three progressive steps to explore data sets with the increased complexity: Terrain Surface Multi-dimensional Data Visualization, a new multi-dimensional technique that highlights the global patterns from the profile of a large scale network. It can lead users‘ attention to characteristic regions for discovering otherwise hidden knowledge; Correlative Multi-level Terrain Surface Visualization, a new visual platform that provides the overview and boosts the major signals of the numeric correlations among nodes in interconnected networks of different contexts. It enables users to gain critical insights and perform data analytical tasks in the context of multiple correlated networks; and the Iterative Visual Refinement Model, an innovative process that treats users‘ perceptions as the objective functions, and guides the users to form the optimal hypothesis by improving the desired visual patterns. It is a formalized model for interactive explorations to converge to optimal solutions. We also showcase our approach with bio-molecular data sets and demonstrate its effectiveness in several biomarker discovery applications.

No description available.

No description available.

No description available.

No description available.

<p> Since the decoding of the first RNA virus in 1976, the field of viral genomics has exploded, first through the use of Sanger sequencing technologies and later with the use next-generation sequencing approaches. With the development of these sequencing technologies, viral genomics has entered an era of big data. New challenges for analyzing these data are now apparent. Here, we describe novel methods to extend the current capabilities of viral comparative genomics. Through the use of antigenic distancing techniques, we have examined the relationship between the antigenic phenotype and the genetic content of influenza virus to establish a more systematic approach to viral surveillance and vaccine selection. Distancing of Antigenicity by Sequence-based Hierarchical Clustering (DASH) was developed and used to perform a retrospective analysis of 22 influenza seasons. Our methods produced vaccine candidates identical to or with a high concordance of antigenic similarity with those selected by the WHO. In a second effort, we have developed VirComp and OrionPlot: two independent yet related tools. These tools first generate gene-based genome constellations, or genotypes, of viral genomes, and second create visualizations of the resultant genome constellations. VirComp utilizes sequence-clustering techniques to infer genome constellations and prepares genome constellation data matrices for visualization with OrionPlot. OrionPlot is a java application for tailoring genome constellation figures for publication. OrionPlot allows for color selection of gene cluster assignments, customized box sizes to enable the visualization of gene comparisons based on sequence length, and label coloring. We have provided five analyses designed as vignettes to illustrate the utility of our tools for performing viral comparative genomic analyses. Study three focused on the analysis of respiratory syncytial virus (RSV) genomes circulating during the 2012- 2013 RSV season. We discovered a correlation between a recent tandem duplication within the G gene of RSV-A and a decrease in severity of infection. Our data suggests that this duplication is associated with a higher infection rate in female infants than is generally observed. Through these studies, we have extended the state of the art of genotype analysis, phenotype/genotype studies and established correlations between clinical metadata and RSV sequence data.</p>