MOTIVATION: Whole metagenome shotgun sequencing is a powerful approach for assaying the functional potential of microbial communities. We currently lack tools that efficiently and accurately align DNA reads against protein references, the technique necessary for constructing a functional profile. Here, we present PALADIN-a novel modification of the Burrows-Wheeler Aligner that provides accurate alignment, robust reporting capabilities and orders-of-magnitude improved efficiency by directly mapping in protein space. RESULTS: We compared the accuracy and efficiency of PALADIN against existing tools that employ nucleotide or protein alignment algorithms. Using simulated reads, PALADIN consistently outperformed the popular DNA read mappers BWA and NovoAlign in detected proteins, percentage of reads mapped and ontological similarity. We also compared PALADIN against four existing protein alignment tools: BLASTX, RAPSearch2, DIAMOND and Lambda, using empirically obtained reads. PALADIN yielded results seven times faster than the best performing alternative, DIAMOND and nearly 8000 times faster than BLASTX. PALADIN's accuracy was comparable to all tested solutions. AVAILABILITY AND IMPLEMENTATION: PALADIN was implemented in C, and its source code and documentation are available at https://github.com/twestbrookunh/paladin. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Numerical simulations running on very large High Performance Computer clusters still suffer from the I/O bottleneck. The cost of communication can overwhelm the cost of computation, and scales inversely with the number of processors used in the cluster. In previous work we have developed a multiresolution data model to help improve performance for visualizations of very large multi dimensional scientific data sets. In our approach, the data is represented as a multi level hierarchy. Reconstructive error analysis is used to identify regions in the data where the data loss is greatest. We have incorporated this data model into the OpenGGCM solar wind simulation environment. In this paper, we demonstrate that this approach can reduce the I/O and improve the overall performance of a large numerical simulation environment.

Genomic study is fairly novel. Typical research processes are not established yet. Many new discoveries are happening in this area all the time. Are current methods of visualizations effective? What works well? What could be improved? These are some of the questions we are interested in evaluating for two graphical tools used to compare nucleotide sequences. Scatter plots and parallel coordinate-like visuals have been used in genomics for identifying similarities in genetic code. Our preliminary evaluation focuses on determining the aspects of the two visualizations that are successful and those that need enhancements.

Proximity search is an important type of database query which is essential to many practical applications involving various types of metric data, including multivariate data with distance function. Point spatial data is a popular subset of metric data in which each data record corresponds to a point in a multidimensional space, and the proximity is represented as a distance function, such as the Euclidean distance, defined on the multidimensional space. Numerous hierarchical data structures, under the name of point spatial data structures, have been developed for implementing efficient spatial proximity searches. Much less work has been done on developing general hierarchical metric data structures for general metric data, such as non-spatial multivariate data. This paper presents an innovative approach for deriving a new class of hierarchical metric data structures from existing point spatial data structures. Instead of performing direct decomposition on metric data as is done for previous hierarchical data structures such as metric trees and vp-trees, we define a class of simple proximity-preserving mappings from metric data to multidimensional spaces, which we call multipolar mappings. By applying multipolar mappings to metric data, hierarchical decompositions can be done in multidimensional space, and various point spatial data structures, such as quadtree, octree, or k-d tree, can be utilized for storing and accessing metric data based on proximity.

The authors present an efficient visualization approach to support multivariate data exploration through a simple but effective low dimensional data overview based on metric scaling. A multivariate dataset is first transformed into a set of dissimilarities between all pairs of data records. A graph configuration algorithm based on principal components is then wed to determine the display coordinates of the data records in the low dimensional data overview. This overview provides a graphical summary of the multivariate data with reduced data dimensions, reduced data size, and additional data semantics. It can be used to enhance multidimensional data brushing, or to arrange the layout of other conventional multivariate visualization techniques. Real life data is used to demonstrate the approach.

Describes several visualization techniques based on the notion of multi-resolution brushing to browse large 3D volume datasets. Our software is implemented using public-domain libraries, and is designed to run on average-equipped desktop computers such as a Linux machine with 32 MBytes of memory. Empirically, our system allows scientists to obtain information from a large dataset with over 8.3 million numbers in interactive time. We show that very large scientific volume datasets can be accessed and utilized without expensive hardware and software.

Brushing is a data visualization technique that identifies and highlights data subsets. We introduce a form of brushing in which the brushed data is usually displayed at a different resolution than the non brushed data. The paper presents the rationale behind the multiresolution support of multivariate data visualization and describes the construction of multiresolution brushing using wavelet approximations. The idea is implemented in an enhanced version of XmdvTool. Real scientific data is used for demonstration and practical applications are suggested.

Parallel program visualization and debugging require new techniques for gathering and displaying execution trace and profile data. Interaction with the program during execution is also required to facilitate parallel debugging. We discuss the difficulties associated with runtime user/program interaction and how the data-parallel programming paradigm facilitates much more liberal runtime interaction than typical MIMD-based models. We present a model for data-parallel program visualization that addresses both data collection/interaction and visualization issues. We follow our model presentation with the design and implementation of a subset of our visualization model. We discuss our preliminary findings and propose future research directions.

Wavelet transforms include data decompositions and reconstructions. This paper is concerned with the authenticity issues of the data decomposition, particularly for data visualization. A total of six datasets are used to clarify the approximation characteristics of compactly supported orthogonal wavelets. We present an error tracking mechanism, which uses the available wavelet resources to measure the quality of the wavelet approximations.

An experiment in exploratory data visualization using a massively parallel processor is described. In exploratory data visualization, it is typically not known what is being looked for: instead, the data are explored with a variety of visualization techniques that can illuminate its nature by demonstrating patterns in it. With this approach, the authors were able to find new features in some of their oldest datasets and to create more vivid presentations of familiar features in these datasets. Their experience has also led to a better understanding of the nature of the exploratory visualization and has resulted in some formal representations of the interaction process in this environment.>

The analysis and interpretation of very high dimensional data require the development and use of data presentation techniques that harness human perceptual powers. The University of Lowell's Exploratory Visualization project (Exvis) aims at designing, implementing, and evaluating perceptually-based tools for data presentation using both visual and auditory domains. This paper describes several auditory data presentation techniques, including the generation of stereophonic sound with apparent depth and sound that appears to emanate from a two-dimensional area. Both approaches can produce sound with auditory texture.

This paper presents a reference model for the development of systems for the visualization of multidimensional data. The purpose of the reference model is to build a conceptual basis for thinking about multi-dimensional visualization and for use in developing visualization environments. We describe the reference model in terms of the fundamental concepts of PHIGS (Programmer’s Hierarchical Interactive Graphics System), but extend those concepts to the representation of objects of arbitrary dimensionality.

This paper describes a design approach that combines knowledge engineering techniques with an interactive graphics program. A prototype for specifying computer systems interactively is used to demonstrate the design approach. The program incorporates a rule-based knowledge engineering module which allows for both data base consistency and the ability to guide the user through an interactive dialogue.

No abstract available.

A library of routines has been developed for the design of arbitrarily curved three-dimensional surfaces. The routines provide for the description and editing of surfaces. Surfaces are defined by families of parametric curves which are blended, using linear interpolation, approximating splines or interpolating splines. Each component curve is in turn defined by a set of ordered control points which are blended using any of these techniques. This approach affords greater flexibility than more classical methods such as bi-variate patches. The editing facilities include the basic spatial transforms as well as a full complement of attribute setting functions.

Abstract As applications systems grow in complexity, the optimization of their resource usage becomes more important. This holds true not only as an attempt to increase their own effectiveness, but also to reduce the impact of that system on resources shared by other users in the environment. The analysis and optimization of user systems can be simplified considerably by identifying commonly required evaluation facilities and implementing these facilities in a system‐independent manner. This paper examines existing techniques and describes a system, the SSD evaluation system, designed and implemented by the authors on an IBM System/370 for the purpose of monitoring the performance of applications systems.