Joseph L. Gabbard

PATRIC, the bacterial bioinformatics database and analysis resource

The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein–protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issue.

The immune epitope database (IEDB) 3.0

The IEDB, www.iedb.org, contains information on immune epitopes—the molecular targets of adaptive immune responses—curated from the published literature and submitted by National Institutes of Health funded epitope discovery efforts. From 2004 to 2012 the IEDB curation of journal articles published since 1960 has caught up to the present day, with >95% of relevant published literature manually curated amounting to more than 15 000 journal articles and more than 704 000 experiments to date. The revised curation target since 2012 has been to make recent research findings quickly available in the IEDB and thereby ensure that it continues to be an up-to-date resource. Having gathered a comprehensive dataset in the IEDB, a complete redesign of the query and reporting interface has been performed in the IEDB 3.0 release to improve how end users can access this information in an intuitive and biologically accurate manner. We here present this most recent release of the IEDB and describe the user testing procedures as well as the use of external ontologies that have enabled it.

A survey of usability evaluation in virtual environments: classification and comparison of methods

Virtual environments (VEs) are a relatively new type of humancomputer interface in which users perceive and act in a three-dimensional world. The designers of such systems cannot rely solely on design guidelines for traditional two-dimensional interfaces, so usability evaluation is crucial for VEs. This paper presents an overview of VE usability evaluation to organize and critically analyze diverse work from this field. First, we discuss some of the issues that differentiate VE usability evaluation from evaluation of traditional user interfaces such as GUIs. We also present a review of some VE evaluation methods currently in use, and discuss a simple classification space for VE usability evaluation methods. This classification space provides a structured means for comparing evaluation methods according to three key characteristics: involvement of representative users, context of evaluation, and types of results produced. Finally, to illustrate these concepts, we compare two existing evaluation approaches: testbed evaluation (Bowman, Johnson, & Hodges, 1999) and sequential evaluation (Gabbard, Hix, & Swan, 1999).

User-centered design and evaluation of virtual environments

We present a structured, iterative methodology for user-centered design and evaluation of VE user interaction. We recommend performing (1) user task analysis followed by (2) expert guidelines-based evaluation, (3) formative user-centered evaluation, and finally (4) comparative evaluation. In this article we first give the motivation and background for our methodology, then we describe each technique in some detail. We applied these techniques to a real-world battlefield visualization VE. Finally, we evaluate why this approach provides a cost-effective strategy for assessing and iteratively improving user interaction in VEs.

PATRIC: the Comprehensive Bacterial Bioinformatics Resource with a Focus on Human Pathogenic Species

ABSTRACT Funded by the National Institute of Allergy and Infectious Diseases, the Pathosystems Resource Integration Center (PATRIC) is a genomics-centric relational database and bioinformatics resource designed to assist scientists in infectious-disease research. Specifically, PATRIC provides scientists with (i) a comprehensive bacterial genomics database, (ii) a plethora of associated data relevant to genomic analysis, and (iii) an extensive suite of computational tools and platforms for bioinformatics analysis. While the primary aim of PATRIC is to advance the knowledge underlying the biology of human pathogens, all publicly available genome-scale data for bacteria are compiled and continually updated, thereby enabling comparative analyses to reveal the basis for differences between infectious free-living and commensal species. Herein we summarize the major features available at PATRIC, dividing the resources into two major categories: (i) organisms, genomes, and comparative genomics and (ii) recurrent integration of community-derived associated data. Additionally, we present two experimental designs typical of bacterial genomics research and report on the execution of both projects using only PATRIC data and tools. These applications encompass a broad range of the data and analysis tools available, illustrating practical uses of PATRIC for the biologist. Finally, a summary of PATRICs outreach activities, collaborative endeavors, and future research directions is provided.

User-centered design and evaluation of a real-time battlefield visualization virtual environment

The ever-increasing power of computers and hardware rendering systems has, to date, primarily motivated the creation of visually rich and perceptually realistic virtual environment (VE) applications. Comparatively very little effort has been expended on the user interaction components of VEs. As a result, VE user interfaces are often poorly designed and are rarely evaluated with users. Although usability engineering is a newly emerging facet of VE development, user-centered design and usability evaluation in VEs as a practice still lags far behind what is needed. This paper presents a structured, iterative approach for the user-centered design and evaluation of VE user interaction. This approach consists of the iterative use of expert heuristic evaluation, followed by formative usability evaluation, followed by summative evaluation. We describe our application of this approach to a real-world VE for battlefield visualization, describe the resulting series of design iterations, and present evidence that this approach provides a cost-effective strategy for assessing and iteratively improving user interaction design in VEs. This paper is among the first to report applying an iterative, structured, user-centered design and evaluation approach to VE user interaction design.

Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center

The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by ‘virtual integration’ to any of PATRICs public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.

The effects of text drawing styles, background textures, and natural lighting on text legibility in outdoor augmented reality

A challenge in presenting augmenting information in outdoor augmented reality (AR) settings lies in the broad range of uncontrollable environmental conditions that may be present, specifically large-scale fluctuations in natural lighting and wide variations in likely backgrounds or objects in the scene. In this paper, we motivate the need for research on the effects of text drawing styles, outdoor background textures, and natural lighting on user performance in outdoor AR. We present a pilot study and a follow-on user-based study that examined the effects on user performance of outdoor background textures, changing outdoor illuminance values, and text drawing styles in a text identification task using an optical, see-through AR system. We report significant effects for all these variables, and discuss user interface design guidelines and ideas for future work.

Active Text Drawing Styles for Outdoor Augmented Reality: A User-Based Study and Design Implications

A challenge in presenting augmenting information in outdoor augmented reality (AR) settings lies in the broad range of uncontrollable environmental conditions that may be present, specifically large-scale fluctuations in natural lighting and wide variations in likely backgrounds or objects in the scene. In this paper, we present a active AR testbed that samples the users field of view, and collects outdoor illuminance values at the participants position. The main contribution presented herein is a user-based study (conducted using the testbed) that examined the effects on user performance of four outdoor background textures, four text colors, three text drawing styles, and two text drawing style algorithms for a text identification task using an optical, see-through AR system. We report significant effects for all these variables, and discuss design guidelines and ideas for future work

A Perceptual Matching Technique for Depth Judgments in Optical, See-Through Augmented Reality

A fundamental problem in optical, see-through augmented reality (AR) is characterizing how it affects the perception of spatial layout and depth. This problem is important because AR system developers need to both place graphics in arbitrary spatial relationships with real-world objects, and to know that users will perceive them in the same relationships. Furthermore, AR makes possible enhanced perceptual techniques that have no real-world equivalent, such as x-ray vision, where AR users are supposed to perceive graphics as being located behind opaque surfaces. This paper reviews and discusses techniques for measuring egocentric depth judgments in both virtual and augmented environments. It then describes a perceptual matching task and experimental design for measuring egocentric AR depth judgments at medium- and far-field distances of 5 to 45 meters. The experiment studied the effect of field of view, the x-ray vision condition, multiple distances, and practice on the task. The paper relates some of the findings to the well-known problem of depth underestimation in virtual environments, and further reports evidence for a switch in bias, from underestimating to overestimating the distance of AR-presented graphics, at 23 meters. It also gives a quantification of how much more difficult the x-ray vision condition makes the task, and then concludes with ideas for improving the experimental methodology.