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1.  Comparative effectiveness of next generation genomic sequencing for disease diagnosis: Design of a randomized controlled trial in patients with colorectal cancer/polyposis syndromes✩ 
Whole exome and whole genome sequencing are applications of next generation sequencing transforming clinical care, but there is little evidence whether these tests improve patient outcomes or if they are cost effective compared to current standard of care. These gaps in knowledge can be addressed by comparative effectiveness and patient-centered outcomes research. We designed a randomized controlled trial that incorporates these research methods to evaluate whole exome sequencing compared to usual care in patients being evaluated for hereditary colorectal cancer and polyposis syndromes. Approximately 220 patients will be randomized and followed for 12 months after return of genomic findings. Patients will receive findings associated with colorectal cancer in a first return of result visit, and findings not associated with colorectal cancer (incidental findings) during a second return of result visit. The primary outcome is efficacy to detect mutations associated with these syndromes; secondary outcomes include psychosocial impact, cost-effectiveness and comparative costs. The secondary outcomes will be obtained via surveys before and after each return visit. The expected challenges in conducting this randomized controlled trial include the relatively low prevalence of genetic disease, difficult interpretation of some genetic variants, and uncertainty about which incidental findings should be returned to patients. The approaches utilized in this study may help guide other investigators in clinical genomics to identify useful outcome measures and strategies to address comparative effectiveness questions about the clinical implementation of genomic sequencing in clinical care.
doi:10.1016/j.cct.2014.06.016
PMCID: PMC4175052  PMID: 24997220
Comparative effectiveness research; Genomics; Next generation sequencing; Randomized clinical trial; Outcomes research; Whole exome sequencing
2.  Characterizing Data Discovery and End-User Computing Needs in Clinical Translational Science 
In this paper, the authors present the results of a qualitative case-study seeking to characterize data discovery needs and barriers of principal investigators and research support staff in clinical translational science. Several implications for designing and implementing translational research systems have emerged through the authors’ analysis. The results also illustrate the benefits of forming early partnerships with scientists to better understand their workflow processes and end-user computing practices in accessing data for research. The authors use this user-centered, iterative development approach to guide the implementation and extension of i2b2, a system they have adapted to support cross-institutional aggregate anonymized clinical data querying. With ongoing evaluation, the goal is to maximize the utility and extension of this system and develop an interface that appropriately fits the swiftly evolving needs of clinical translational scientists.
doi:10.4018/joeuc.2011100102
PMCID: PMC3983692  PMID: 24729759
Biomedical Research; Clinical Data Discovery; Clinical Translational Science; End-User Scientific Computing; Federated Querying; Patient Information Systems; User Needs
3.  A survey of informatics approaches to whole-exome and whole-genome clinical reporting in the electronic health record 
Purpose
Genome-scale clinical sequencing is being adopted more broadly in medical practice. The National Institutes of Health developed the Clinical Sequencing Exploratory Research (CSER) program to guide implementation and dissemination of best practices for the integration of sequencing into clinical care. This study describes and compares the state of the art of incorporating whole-exome and whole-genome sequencing results into the electronic health record, including approaches to decision support across the six current CSER sites.
Methods
The CSER Medical Record Working Group collaboratively developed and completed an in-depth survey to assess the communication of genome-scale data into the electronic health record. We summarized commonalities and divergent approaches.
Results
Despite common sequencing platform (Illumina) adoptions, there is a great diversity of approaches to annotation tools and workflow, as well as to report generation. At all sites, reports are human-readable structured documents available as passive decision support in the electronic health record. Active decision support is in early implementation at two sites.
Conclusion
The parallel efforts across CSER sites in the creation of systems for report generation and integration of reports into the electronic health record, as well as the lack of standardized approaches to interfacing with variant databases to create active clinical decision support, create opportunities for cross-site and vendor collaborations.
doi:10.1038/gim.2013.120
PMCID: PMC3951437  PMID: 24071794
clinical decision support; clinical sequencing; decision support rules; electronic health record; electronic medical record; next-generation sequencing
5.  AMIA Board white paper: definition of biomedical informatics and specification of core competencies for graduate education in the discipline 
The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is ‘the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.’ Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics sub-disciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.
doi:10.1136/amiajnl-2012-001053
PMCID: PMC3534470  PMID: 22683918
President and CEO; preparedness; wireless; preferences; population health; primary care; collaborative technologies; knowledge representations; knowledge acquisition and knowledge management; controlled terminologies and vocabularies; ontologies; AMIA
6.  Personalized medicine: challenges and opportunities for translational bioinformatics 
Personalized medicine  2013;10(5):453-462.
Personalized medicine can be defined broadly as a model of healthcare that is predictive, personalized, preventive and participatory. Two US President’s Council of Advisors on Science and Technology reports illustrate challenges in personalized medicine (in a 2008 report) and in use of health information technology (in a 2010 report). Translational bioinformatics is a field that can help address these challenges and is defined by the American Medical Informatics Association as “the development of storage, analytic and interpretive methods to optimize the transformation of increasing voluminous biomedical data into proactive, predictive, preventative and participatory health.” This article discusses barriers to implementing genomics applications and current progress toward overcoming barriers, describes lessons learned from early experiences of institutions engaged in personalized medicine and provides example areas for translational bioinformatics research inquiry.
PMCID: PMC3770190  PMID: 24039624
biobanks; clinical decision support; computational analyses; education; electronic health records; implementation challenges; individual research results; personalized medicine; translational bioinformatics; translational research
7.  Deriving rules and assertions from pharmacogenomics knowledge resources in support of patient drug metabolism efficacy predictions 
Objective
Pharmacogenomics evaluations of variability in drug metabolic processes may be useful for making individual drug response predictions. We present an approach to deriving ‘phenotype scores’ based on existing pharmacogenomics knowledge and a patient's genomics data. Pharmacogenomics plays an important role in the bioactivation of tamoxifen, a prodrug administered to patients for breast cancer treatment. Tamoxifen is therefore considered a model for many drugs requiring bioactivation. We investigate whether this knowledge-based approach can be applied to produce a phenotype score that is predictive of the endoxifen/N-desmethyltamoxifen (NDM) plasma concentration ratio in patients taking tamoxifen.
Materials and methods
We implement a knowledge-based model for calculating phenotype scores from patient-specific genotype data. These data include allelic variants of genes encoding enzymes involved in the bioactivation of tamoxifen. We performed quantile linear regression to evaluate whether six phenotype scoring algorithms are predictive of patient endoxifen/NDM plasma concentration ratio, and validate our scoring methods.
Results
Our model illustrates a knowledge-based approach to predict drug metabolism efficacy given patient genomics data. Results showed that for one phenotype scoring algorithm, scores were weakly correlated with patient endoxifen/NDM plasma concentration ratios. This algorithm performed better than simple metrics for variation in individual and multiple genes.
Discussion
We discuss advantages of the model, challenges to its implementation in a personalized medicine context, and provide example future directions.
Conclusions
We demonstrate the utility of our model in a tamoxifen case study context. We also provide evidence that more complicated polygenic models are needed to represent heterogeneity in clinical outcomes.
doi:10.1136/amiajnl-2011-000405
PMCID: PMC3422817  PMID: 22539082
Pharmacogenetics; pharmacogenomics; pharmacokinetics; knowledge bases; computer reasoning; genotype; genetic variation; predictive genetic testing; cytochrome P-450 enzyme system; tamoxifen; computer reasoning; knowledge bases; genomics; electronic health records; clinical decision support; developing/using computerized provider order entry; classical experimental and quasi-experimental study methods (lab and field); developing/using clinical decision support (other than diagnostic) and guideline systems; other specific ehr applications (results review; medication administration; disease progression; system implementation and management issues; surveys and needs analysis; qualitative/ethnographic field study
8.  A survey of informatics platforms that enable distributed comparative effectiveness research using multi-institutional heterogeneous clinical data 
Medical care  2012;50(Suppl):S49-S59.
Comparative Effectiveness Research (CER) has the potential to transform the current healthcare delivery system by identifying the most effective medical and surgical treatments, diagnostic tests, disease prevention methods and ways to deliver care for specific clinical conditions. To be successful, such research requires the identification, capture, aggregation, integration, and analysis of disparate data sources held by different institutions with diverse representations of the relevant clinical events. In an effort to address these diverse demands, there have been multiple new designs and implementations of informatics platforms that provide access to electronic clinical data and the governance infrastructure required for inter-institutional CER. The goal of this manuscript is to help investigators understand why these informatics platforms are required and to compare and contrast six, large-scale, recently funded, CER-focused informatics platform development efforts. We utilized an 8-dimension, socio-technical model of health information technology use to help guide our work. We identified six generic steps that are necessary in any distributed, multi-institutional CER project: data identification, extraction, modeling, aggregation, analysis, and dissemination. We expect that over the next several years these projects will provide answers to many important, and heretofore unanswerable, clinical research questions.
doi:10.1097/MLR.0b013e318259c02b
PMCID: PMC3415281  PMID: 22692259
Methods; Comparative Effectiveness Research; Organization and Administration; Medical Informatics; Methods
9.  Developing a Prototype System for Integrating Pharmacogenomics Findings into Clinical Practice 
Journal of personalized medicine  2012;2(4):241-256.
Findings from pharmacogenomics (PGx) studies have the potential to be applied to individualize drug therapy to improve efficacy and reduce adverse drug events. Researchers have identified factors influencing uptake of genomics in medicine, but little is known about the specific technical barriers to incorporating PGx into existing clinical frameworks. We present the design and development of a prototype PGx clinical decision support (CDS) system that builds on existing clinical infrastructure and incorporates semi-active and active CDS. Informing this work, we updated previous evaluations of PGx knowledge characteristics, and of how the CDS capabilities of three local clinical systems align with data and functional requirements for PGx CDS. We summarize characteristics of PGx knowledge and technical needs for implementing PGx CDS within existing clinical frameworks. PGx decision support rules derived from FDA drug labels primarily involve drug metabolizing genes, vary in maturity, and the majority support the post-analytic phase of genetic testing. Computerized provider order entry capabilities are key functional requirements for PGx CDS and were best supported by one of the three systems we evaluated. We identified two technical needs when building on this system, the need for (1) new or existing standards for data exchange to connect clinical data to PGx knowledge, and (2) a method for implementing semi-active CDS. Our analyses enhance our understanding of principles for designing and implementing CDS for drug therapy individualization and our current understanding of PGx characteristics in a clinical context. Characteristics of PGx knowledge and capabilities of current clinical systems can help govern decisions about CDS implementation, and can help guide decisions made by groups that develop and maintain knowledge resources such that delivery of content for clinical care is supported.
doi:10.3390/jpm2040241
PMCID: PMC3670105  PMID: 23741623
electronic health records; clinical decision support systems; pharmacogenomics; personalized medicine; computerized provider order entry; knowledge representation
10.  Biotrust: A Comprehensive System for Acquiring and Distributing Biospecimens 
The Biotrust resource provides a web-accessible method to coordinate discovery and request of annotated biospecimens for research. The system is built on an open-source web-application framework, and has a modular approach to defining education on process, study registration and feasibility review, patient identification and cohort forwarding, consent tracking, and biospecimen processing/distribution. The architecture has been designed as a “pass through” system that provides annotated deidentified biospecimens for investigator use in a restricted time window of 4–7 days, and does not maintain biobanking facilities. As a core institutional resource, the system integrates seven vertical service arms, each of which can be accessed independently to support flexible and independent use in translational research. The system will be described in terms of requirements for use, metrics of evaluation, and lessons learned in integrating this into clinical and operational environments.
PMCID: PMC3814462  PMID: 24303285
11.  Learning virulent proteins from integrated query networks 
BMC Bioinformatics  2012;13:321.
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.
doi:10.1186/1471-2105-13-321
PMCID: PMC3560104  PMID: 23198735
12.  Developing a Prototype System for Integrating Pharmacogenomics Findings into Clinical Practice  
Journal of Personalized Medicine  2012;2(4):241-256.
Findings from pharmacogenomics (PGx) studies have the potential to be applied to individualize drug therapy to improve efficacy and reduce adverse drug events. Researchers have identified factors influencing uptake of genomics in medicine, but little is known about the specific technical barriers to incorporating PGx into existing clinical frameworks. We present the design and development of a prototype PGx clinical decision support (CDS) system that builds on existing clinical infrastructure and incorporates semi-active and active CDS. Informing this work, we updated previous evaluations of PGx knowledge characteristics, and of how the CDS capabilities of three local clinical systems align with data and functional requirements for PGx CDS. We summarize characteristics of PGx knowledge and technical needs for implementing PGx CDS within existing clinical frameworks. PGx decision support rules derived from FDA drug labels primarily involve drug metabolizing genes, vary in maturity, and the majority support the post-analytic phase of genetic testing. Computerized provider order entry capabilities are key functional requirements for PGx CDS and were best supported by one of the three systems we evaluated. We identified two technical needs when building on this system, the need for (1) new or existing standards for data exchange to connect clinical data to PGx knowledge, and (2) a method for implementing semi-active CDS. Our analyses enhance our understanding of principles for designing and implementing CDS for drug therapy individualization and our current understanding of PGx characteristics in a clinical context. Characteristics of PGx knowledge and capabilities of current clinical systems can help govern decisions about CDS implementation, and can help guide decisions made by groups that develop and maintain knowledge resources such that delivery of content for clinical care is supported.
doi:10.3390/jpm2040241
PMCID: PMC3670105  PMID: 23741623
electronic health records; clinical decision support systems; pharmacogenomics; personalized medicine; computerized provider order entry; knowledge representation
13.  Neonatal Informatics: Transforming Neonatal Care Through Translational Bioinformatics 
NeoReviews  2012;13(5):e281-e284.
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.
doi:10.1542/neo.13-5-e281
PMCID: PMC3424284  PMID: 22924023
14.  LC Data QUEST: A Technical Architecture for Community Federated Clinical Data Sharing 
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.
PMCID: PMC3392065  PMID: 22779052
15.  Supporting Retrieval of Diverse Biomedical Data Using Evidence-aware Queries 
Journal of biomedical informatics  2010;43(6):873-882.
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.
doi:10.1016/j.jbi.2010.07.005
PMCID: PMC3059407  PMID: 20643225
Data integration; Database management; Information storage and retrieval; Bioinformatics; Ontology; Query language; Data diversity and provenance
16.  Implementation of a deidentified federated data network for population-based cohort discovery 
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.
doi:10.1136/amiajnl-2011-000133
PMCID: PMC3392860  PMID: 21873473
Information dissemination; information management; clinical trials as topic; cohort discovery; federated network; ethical study methods; knowledge representations; information storage and retrieval (text and images); knowledge bases; surveys and needs analysis; shrine; i2b2; transmart; data warehousing; informatics; evaluation; machine learning; predictive modeling; statistical learning; privacy technology; modeling physiologic and disease processes; linking the genotype and phenotype; identifying genome and protein structure and function; visualization of data and knowledge; information dissemination; information management; clinical trials as topic; cohort discovery; federated network
17.  Evaluation of Probabilistic and Logical Inference for a SNP Annotation System 
Journal of biomedical informatics  2009;43(3):407-418.
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.
doi:10.1016/j.jbi.2009.12.002
PMCID: PMC2878960  PMID: 20015478
Single nucleotide polymorphisms (SNPs); Federated data integration; SNP annotation system; logical inference; probabilistic inference; SNP evaluation
18.  Translational bioinformatics: linking knowledge across biological and clinical realms 
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.
doi:10.1136/amiajnl-2011-000245
PMCID: PMC3128415  PMID: 21561873
Translational bioinformatics; systems medicine; systems biology; bioinformatics; biomedical informatics; knowledge representation; information retrieval; phylogenetics; modeling physiologic and disease processes; linking the genotype and phenotype; identifying genome and protein structure and function; visualization of data and knowledge; simulation of complex systems (at all levels: molecules to work groups to organizations); knowledge representations; uncertain reasoning and decision theory; languages; computational methods; statistical analysis of large datasets; advanced algorithms; discovery; text and data mining methods; natural-language processing; automated learning; ontologies
19.  Prescriber and staff perceptions of an electronic prescribing system in primary care: a qualitative assessment 
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.
doi:10.1186/1472-6947-10-72
PMCID: PMC2996338  PMID: 21087524
20.  Selected proceedings of the 2010 Summit on Translational Bioinformatics 
BMC Bioinformatics  2010;11(Suppl 9):S1.
doi:10.1186/1471-2105-11-S9-S1
PMCID: PMC2967739  PMID: 21044356
21.  Feasibility of incorporating genomic knowledge into electronic medical records for pharmacogenomic clinical decision support 
BMC Bioinformatics  2010;11(Suppl 9):S10.
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.
doi:10.1186/1471-2105-11-S9-S10
PMCID: PMC2967740  PMID: 21044357
22.  SNPit: a federated data integration system for the purpose of functional SNP annotation 
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.
doi:10.1016/j.cmpb.2009.02.010
PMCID: PMC2680224  PMID: 19327864
Single nucleotide polymorphisms (SNPs); Public health genetics; Biomedical informatics; Data integration; SNP annotation system; SNP integration system
23.  Facilitating Health Data Sharing Across Diverse Practices and Communities 
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.
PMCID: PMC3041543  PMID: 21347138
24.  Incorporating collaboratory concepts into informatics in support of translational interdisciplinary biomedical research 
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.
doi:10.1016/j.ijmedinf.2008.06.011
PMCID: PMC2606933  PMID: 18706852
Collaboration; Biomedical informatics; Computer supported collaborative work; Collaboratories; Social and technical issues; Bioinformatics
25.  The potential for automated question answering in the context of genomic medicine: an assessment of existing resources and properties of answers 
BMC Bioinformatics  2009;10(Suppl 9):S8.
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.
doi:10.1186/1471-2105-10-S9-S8
PMCID: PMC2745695  PMID: 19761578

Results 1-25 (39)