Background

Methods of weakening and attenuating pathogens’ abilities to infect and propagate in a host, thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories, applicable to both generic and specific virulence categories.

Results

A lightweight method for data integration is used, which links information regarding a protein via a path-based query graph. A method of weighting is then applied to query graphs that can serve as input to various statistical classification methods for discrimination, and the combined usage of both data integration and learning methods are tested against the problem of both generalized and specific virulence function prediction.

Conclusions

This approach improves coverage of functional data over a protein. Moreover, while depending largely on noisy and potentially non-curated data from public sources, we find it outperforms other techniques to identification of general virulence factors and baseline remote homology detection methods for specific virulence categories.

The future of neonatal informatics will be driven by the availability of increasingly vast amounts of clinical and genetic data. The field of translational bioinformatics is concerned with linking and learning from these data and applying new findings to clinical care to transform the data into proactive, predictive, preventive, and participatory health. As a result of advances in translational informatics, the care of neonates will become more data driven, evidence based, and personalized.

The University of Washington Institute of Translational Health Sciences is engaged in a project, LC Data QUEST, building data sharing capacity in primary care practices serving rural and tribal populations in the Washington, Wyoming, Alaska, Montana, Idaho region to build research infrastructure. We report on the iterative process of developing the technical architecture for semantically aligning electronic health data in primary care settings across our pilot sites and tools that will facilitate linkages between the research and practice communities. Our architecture emphasizes sustainable technical solutions for addressing data extraction, alignment, quality, and metadata management. The architecture provides immediate benefits to participating partners via a clinical decision support tool and data querying functionality to support local quality improvement efforts. The FInDiT tool catalogues type, quantity, and quality of the data that are available across the LC Data QUEST data sharing architecture. These tools facilitate the bi-directional process of translational research.

Though there have been many advances in providing access to linked and integrated biomedical data across repositories, developing methods which allow users to specify ambiguous and exploratory queries over disparate sources remains a challenge to extracting well-curated or diversely-supported biological information. In the following work, we discuss the concepts of data coverage and evidence in the context of integrated sources. We address diverse information retrieval via a simple framework for representing coverage and evidence that operates in parallel with an arbitrary schema, and a language upon which queries on the schema and framework may be executed. We show that this approach is capable of answering questions that require ranged levels of evidence or triangulation, and demonstrate that appropriately-formed queries can significantly improve the level of precision when retrieving well-supported biomedical data.

Objective

The Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers.

Methods

The project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource.

Results

By standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility.

Discussion

The authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned.

Conclusion

The common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites.

Genome wide association studies (GWAS) are an important approach to understanding the genetic mechanisms behind human diseases. Single nucleotide polymorphisms (SNPs) are the predominant markers used in genome wide association studies, and the ability to predict which SNPs are likely to be functional is important for both a priori and a posteriori analyses of GWA studies. This article describes the design, implementation and evaluation of a family of systems for the purpose of identifying SNPs that may cause a change in phenotypic outcomes. The methods described in this article characterize the feasibility of combinations of logical and probabilistic inference with federated data integration for both point and regional SNP annotation and analysis. Evaluations of the methods demonstrate the overall strong predictive value of logical, and logical with probabilistic, inference applied to the domain of SNP annotation.

Nearly a decade since the completion of the first draft of the human genome, the biomedical community is positioned to usher in a new era of scientific inquiry that links fundamental biological insights with clinical knowledge. Accordingly, holistic approaches are needed to develop and assess hypotheses that incorporate genotypic, phenotypic, and environmental knowledge. This perspective presents translational bioinformatics as a discipline that builds on the successes of bioinformatics and health informatics for the study of complex diseases. The early successes of translational bioinformatics are indicative of the potential to achieve the promise of the Human Genome Project for gaining deeper insights to the genetic underpinnings of disease and progress toward the development of a new generation of therapies.

Background

The United States (US) Health Information Technology for Economic and Clinical Health Act of 2009 has spurred adoption of electronic health records. The corresponding meaningful use criteria proposed by the Centers for Medicare and Medicaid Services mandates use of computerized provider order entry (CPOE) systems. Yet, adoption in the US and other Western countries is low and descriptions of successful implementations are primarily from the inpatient setting; less frequently the ambulatory setting. We describe prescriber and staff perceptions of implementation of a CPOE system for medications (electronic- or e-prescribing system) in the ambulatory setting.

Methods

Using a cross-sectional study design, we conducted eight focus groups at three primary care sites in an independent medical group. Each site represented a unique stage of e-prescribing implementation - pre/transition/post. We used a theoretically based, semi-structured questionnaire to elicit physician (n = 17) and staff (n = 53) perceptions of implementation of the e-prescribing system. We conducted a thematic analysis of focus group discussions using formal qualitative analytic techniques (i.e. deductive framework and grounded theory). Two coders independently coded to theoretical saturation and resolved discrepancies through discussions.

Results

Ten themes emerged that describe perceptions of e-prescribing implementation: 1) improved availability of clinical information resulted in prescribing efficiencies and more coordinated care; 2) improved documentation resulted in safer care; 3) efficiencies were gained by using fewer paper charts; 4) organizational support facilitated adoption; 5) transition required time; resulted in workload shift to staff; 6) hardware configurations and network stability were important in facilitating workflow; 7) e-prescribing was time-neutral or time-saving; 8) changes in patient interactions enhanced patient care but required education; 9) pharmacy communications were enhanced but required education; 10) positive attitudes facilitated adoption.

Conclusions

Prescribers and staff worked through the transition to successfully adopt e-prescribing, and noted the benefits. Overall impressions were favorable. No one wished to return to paper-based prescribing.

In pursuing personalized medicine, pharmacogenomic (PGx) knowledge may help guide prescribing drugs based on a person’s genotype. Here we evaluate the feasibility of incorporating PGx knowledge, combined with clinical data, to support clinical decision-making by: 1) analyzing clinically relevant knowledge contained in PGx knowledge resources; 2) evaluating the feasibility of a rule-based framework to support formal representation of clinically relevant knowledge contained in PGx knowledge resources; and, 3) evaluating the ability of an electronic medical record/electronic health record (EMR/EHR) to provide computable forms of clinical data needed for PGx clinical decision support. Findings suggest that the PharmGKB is a good source for PGx knowledge to supplement information contained in FDA approved drug labels. Furthermore, we found that with supporting knowledge (e.g. IF age <18 THEN patient is a child), sufficient clinical data exists in University of Washington’s EMR systems to support 50% of PGx knowledge contained in drug labels that could be expressed as rules.

Genome wide association studies can potentially identify the genetic causes behind the majority of human diseases. With the advent of more advanced genotyping techniques, there is now an explosion of data gathered on single nucleotide polymorphisms (SNPs). The need exists for an integrated system that can provide up-to-date functional annotation information on SNPs. We have developed the SNP Integration Tool (SNPit) system to address this need. Built upon a federated data integration system, SNPit provides current information on a comprehensive list of SNP data sources. Additional logical inference analysis was included through an inference engine plug in. The SNPit web servlet is available online for use. SNPit allows users to go to one source for up-to-date information on the functional annotation of SNPs. A tool that can help to integrate and analyze the potential functional significance of SNPs is important for understanding the results from genome wide association studies.

Health data sharing with and among practices is a method for engaging rural and underserved populations, often with strong histories of marginalization, in health research. The Institute of Translational Health Sciences, funded by a National Institutes of Health Clinical and Translational Science Award, is engaged in the LC Data QUEST project to build practice and community based research networks with the ability to share semantically aligned electronic health data. We visited ten practices and communities to assess the feasibility of and barriers to developing data sharing networks. We found that these sites had very different approaches and expectations for data sharing. In order to support practices and communities and foster the acceptance of data sharing in these settings, informaticists must take these diverse views into account. Based on these findings, we discuss system design implications and the need for flexibility in the development of community-based data sharing networks.

Due to its complex nature, modern biomedical research has become increasingly interdisciplinary and collaborative in nature. Although a necessity, interdisciplinary biomedical collaboration is difficult. There is, however, a growing body of literature on the study and fostering of collaboration in fields such as computer supported cooperative work (CSCW) and information science (IS). These studies of collaboration provide insight into how to potentially alleviate the difficulties of interdisciplinary collaborative research. We, therefore, undertook a cross cutting study of science and engineering collaboratories to identify emergent themes. We review many relevant collaboratory concepts: (a) general collaboratory concepts across many domains: communication, common workspace and coordination, and data sharing and management, (b) specific collaboratory concepts of particular biomedical relevance: data integration and analysis, security structure, metadata and data provenance, and interoperability and data standards, (c) environmental factors that support collaboratories: administrative and management structure, technical support, and available funding as critical environmental factors, and (d) future considerations for biomedical collaboration: appropriate training and long-term planning. In our opinion, the collaboratory concepts we discuss can guide planning and design of future collaborative infrastructure by biomedical informatics researchers to alleviate some of the difficulties of interdisciplinary biomedical collaboration.

Knowledge gained in studies of genetic disorders is reported in a growing body of biomedical literature containing reports of genetic variation in individuals that map to medical conditions and/or response to therapy. These scientific discoveries need to be translated into practical applications to optimize patient care. Translating research into practice can be facilitated by supplying clinicians with research evidence. We assessed the role of existing tools in extracting answers to translational research questions in the area of genomic medicine. We: evaluate the coverage of translational research terms in the Unified Medical Language Systems (UMLS) Metathesaurus; determine where answers are most often found in full-text articles; and determine common answer patterns. Findings suggest that we will be able to leverage the UMLS in development of natural language processing algorithms for automated extraction of answers to translational research questions from biomedical text in the area of genomic medicine.

Many common and chronic diseases are influenced at some level by genetic variation. Research done in population genetics, specifically in the area of single nucleotide polymorphisms (SNPs) is critical to understanding human genetic variation. A key element in assessing role of a given SNP is determining if the variation is likely to result in change in function. The SNP Integration Tool (SNPit) is a comprehensive tool that integrates diverse, existing predictors of SNP functionality, providing the user with information for improved association study analysis. To evaluate the SNPit system, we developed an alternative gold standard to measure accuracy using sensitivity and specificity. The results of our evaluation demonstrated that our alternative gold standard produced encouraging results.

Abstract

Proteins of unknown function are a barrier to our understanding of molecular biology. Assigning function to these “uncharacterized” proteins is imperative, but challenging. The usual approach is similarity searches using annotation databases, which are useful for predicting function. However, since the performance of these databases on uncharacterized proteins is basically unknown, the accuracy of their predictions is suspect, making annotation difficult. To address this challenge, we developed a benchmark annotation dataset of 30 proteins in Shewanella oneidensis. The proteins in the dataset were originally uncharacterized after the initial annotation of the S. oneidensis proteome in 2002. In the intervening 5 years, the accumulation of new experimental evidence has enabled specific functions to be predicted. We utilized this benchmark dataset to evaluate several commonly utilized annotation databases. According to our criteria, six annotation databases accurately predicted functions for at least 60% of proteins in our dataset. Two of these six even had a “conditional accuracy” of 90%. Conditional accuracy is another evaluation metric we developed which excludes results from databases where no function was predicted. Also, 27 of the 30 proteins' functions were correctly predicted by at least one database. These represent one of the first performance evaluations of annotation databases on uncharacterized proteins. Our evaluation indicates that these databases readily incorporate new information and are accurate in predicting functions for uncharacterized proteins, provided that experimental function evidence exists.

Knowledge gained in studies of genetic disorders is reported in a growing body of biomedical literature containing reports of genetic variation in individuals that map to medical conditions and/or response to therapy. These scientific discoveries need to be translated into practical applications to optimize patient care. Translating research into practice can be facilitated by supplying clinicians with research evidence. We assessed the role of existing tools in extracting answers to translational research questions in the area of genomic medicine. We: evaluate the coverage of translational research terms in the Unified Medical Language Systems (UMLS) Metathesaurus; determine where answers are most often found in full-text articles; and determine common answer patterns. Findings suggest that we will be able to leverage the UMLS in development of natural language processing algorithms for automated extraction of answers to translational research questions from biomedical text in the area of genomic medicine.

Knowledge about people and organizational issues pertinent to implementation and maintenance of clinical systems has grown steadily over the past fifteen years. Less is known about implementation of systems used for clinical and biomedical research. In conjunction with current National Institutes of Health Roadmap efforts that promote translational research, these issues should now be identified and addressed. During the 2007 American College of Medical Informatics Symposium, members discussed behavioral aspects of translational informatics. This article summarizes that discussion, which covered organizational issues, implications of how knowledge about clinical systems implementation can inform research systems implementation, and those issues unique to each kind of system.

Objectives

A. Identify the current state of data management needs of academic biomedical researchers. B. Explore their anticipated data management and analysis needs. C. Identify barriers to addressing those needs.

Design

A multimodal needs analysis was conducted using a combination of an online survey and in-depth one-on-one semi-structured interviews. Subjects were recruited via an e-mail list representing a wide range of academic biomedical researchers in the Pacific Northwest.

Measurements

The results from 286 survey respondents were used to provide triangulation of the qualitative analysis of data gathered from 15 semi-structured in-depth interviews.

Results

Three major themes were identified: 1) there continues to be widespread use of basic general-purpose applications for core data management; 2) there is broad perceived need for additional support in managing and analyzing large datasets; and 3) the barriers to acquiring currently available tools are most commonly related to financial burdens on small labs and unmet expectations of institutional support.

Conclusion

Themes identified in this study suggest that at least some common data management needs will best be served by improving access to basic level tools such that researchers can solve their own problems. Additionally, institutions and informaticians should focus on three components: 1) facilitate and encourage the use of modern data exchange models and standards, enabling researchers to leverage a common layer of interoperability and analysis; 2) improve the ability of researchers to maintain provenance of data and models as they evolve over time though tools and the leveraging of standards; and 3) develop and support information management service cores that could assist in these previous components while providing researchers with unique data analysis and information design support within a spectrum of informatics capabilities.

Setting: The University of Washington Health Sciences Libraries and Information Center BioCommons serves the bioinformatics needs of researchers at the university and in the vibrant for-profit and not-for-profit biomedical research sector in the Washington area and region.

Program Components: The BioCommons comprises services addressing internal University of Washington, not-for-profit, for-profit, and regional and global clientele. The BioCommons is maintained and administered by the BioResearcher Liaison Team. The BioCommons architecture provides a highly flexible structure for adapting to rapidly changing resources and needs.

Evaluation Mechanisms: BioCommons uses Web-based pre- and post-course evaluations and periodic user surveys to assess service effectiveness. Recent surveys indicate substantial usage of BioCommons services and a high level of effectiveness and user satisfaction.

Next Steps/Future Directions: BioCommons is developing novel collaborative Web resources to distribute bioinformatics tools and is experimenting with Web-based competency training in bioinformation resource use.

Background

Providing for long-term and consistent public access to scientific data is a growing concern in biomedical research. One aspect of this problem can be demonstrated by evaluating the persistence of supplementary data associated with published biomedical papers.

Methods

We manually evaluated 655 supplementary data links extracted from PubMed abstracts published 1998–2005 (Method 1) as well as a further focused subset of 162 full-text manuscripts published within three representative high-impact biomedical journals between September and December 2004 (Method 2).

Results

For Method 1 we found that since 2001, only 71 – 92% of supplementary data were still accessible via the links provided, with 93% of these inaccessible links occurring where supplementary data was not stored with the publishing journal. Of the manuscripts evaluated in Method 2, we found that only 83% of these links were available approximately a year after publication, with 55% of these inaccessible links were at locations outside the journal of publication.

Conclusion

We conclude that if supplemental data is required to support the publication, journals policies must take-on the responsibility to accept and store such data or require that it be maintained with a credible independent institution or under the terms of a strategic data storage plan specified by the authors. We further recommend that publishers provide automated systems to ensure that supplementary links remain persistent, and that granting bodies such as the NIH develop policies and funding mechanisms to maintain long-term persistent access to these data.

Personal Digital Assistants (PDAs) offer many potential advantages to clinicians. A number of systems have begun to appear for all types of PDAs that allow for the recording and tracking of patient information. PDAs allow information to be both entered and accessed at the point of care. They also allow information entered away from a central repository to be added or "synced" with data through the use of a wireless or wired connection. Few systems, however, have been designed to work in the client/server environment. Even fewer have been designed as point of care additions to already existing enterprise systems. This paper describes the issues encountered in deploying such a system for use in the University of Washington Neonatal Intensive Care Unit (NICU). The lessons learned could be applied to other institutions that will seek to add handheld technology to information systems in the future.

GeneClinics is an online genetic information resource consisting of descriptions of specific inherited disorders (“disease profiles”) as well as information on the role of genetic testing in the diagnosis, management, and genetic counseling of patients with these inherited conditions. GeneClinics is intended to promote the use of genetic services in medical care and personal decision making by providing health care practitioners and patients with information on genetic testing for specific inherited disorders. GeneClinics is implemented as an object-oriented database containing a combination of data and semistructured text that is rendered as HTML for publishing a given “disease profile” on the Web. Content is acquired from authors via templates, converted to an XML document reflecting the underlying database schema (with tagging of embedded data), and then loaded into the database and subjected to peer review. The initial implementation of a production system and the first phase of population of the GeneClinics database content are complete. Further expansion of the content to cover more disease, significant scaling up of rate of content creation, and evaluation redesign are under way. The ultimate goal is to have an entry in GeneClinics for each entry in the GeneTests directory of medical genetics laboratories—that is, for each disease for which clinical genetic testing is available.