Renal Pre-Competitive Consortium (RPC2): discovering therapeutic targets together.

Despite significant effort, patients with kidney disease have not seen their outcomes improved significantly over the past two decades. This has motivated clinicians and researchers to consider alternative methods to identifying risk factors, disease progression markers, and effective therapies. Genome-scale data sets from patients with renal disease can be used to establish a platform to improve understanding of the molecular basis of disease; however, such studies require expertise and resources. To overcome these challenges, we formed an academic-industry consortium to share molecular target identification efforts and expertise across academia and the pharmaceutical industry. The Renal Pre-Competitive Consortium (RPC2) aims to accelerate novel drug development for kidney diseases through a systems biology approach. Here, we describe the rationale, philosophy, establishment, and initial results of this strategy.

Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines.

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors conducting exploratory analysis using the same interactive data visualizations as practicing scientists. We examined 22 upper level undergraduates in a genomics course as they engaged in a case-based inquiry with an interactive heat map. We qualitatively and quantitatively analyzed students' visual analytic behaviors, reasoning and outcomes to identify student performance patterns, commonly shared efficiencies and task completion. We analyzed students' successes and difficulties in applying knowledge and skills relevant to the visual analytics case and related gaps in knowledge and skill to associated tool designs. Findings show that undergraduate engagement in visual analytics is feasible and could be further strengthened through tool usability improvements. We identify these improvements. We speculate, as well, on instructional considerations that our findings suggested may also enhance visual analytics in case-based modules.

Research Infrastructure for Collaborative Team Science: Challenges in Technology-Supported Workflows in and Across Laboratories, Institutions, and Geographies.

Collaborative research has many challenges. One under-researched challenge is how to align collaborators' research practices and evolving analytical reasoning with technologies and configurations of technologies that best support them. The goal of such alignment is to enhance collaborative problem solving capabilities in research. Toward this end, we draw on our own research and a synthesis of the literature to characterize the workflow of collaborating scientists in systems-level renal disease research. We describe the various phases of a hypothetical workflow among diverse collaborators within and across laboratories, extending from their primary analysis through secondary analysis. For each phase, we highlight required technology supports, and. At time, complementary organizational supports. This survey of supports matching collaborators' analysis practices and needs in research projects to technological support is preliminary, aimed ultimately at developing a research capability framework that can help scientists and technologists mutually understand workflows and technologies that can help enable and enhance them.

Scientists' sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support.

A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists' analytical workflows and their implications for tool design.

Studying PubMed usages in the field for complex problem solving: Implications for tool design.

Many recent studies on MEDLINE-based information seeking have shed light on scientists' behaviors and associated tool innovations that may improve efficiency and effectiveness. Few if any studies, however, examine scientists' problem-solving uses of PubMed in actual contexts of work and corresponding needs for better tool support. Addressing this gap, we conducted a field study of novice scientists (14 upper level undergraduate majors in molecular biology) as they engaged in a problem solving activity with PubMed in a laboratory setting. Findings reveal many common stages and patterns of information seeking across users as well as variations, especially variations in cognitive search styles. Based on findings, we suggest tool improvements that both confirm and qualify many results found in other recent studies. Our findings highlight the need to use results from context-rich studies to inform decisions in tool design about when to offer improved features to users.

The NIH National Center for Integrative Biomedical Informatics (NCIBI).

The National Center for Integrative and Biomedical Informatics (NCIBI) is one of the eight NCBCs. NCIBI supports information access and data analysis for biomedical researchers, enabling them to build computational and knowledge models of biological systems to address the Driving Biological Problems (DBPs). The NCIBI DBPs have included prostate cancer progression, organ-specific complications of type 1 and 2 diabetes, bipolar disorder, and metabolic analysis of obesity syndrome. Collaborating with these and other partners, NCIBI has developed a series of software tools for exploratory analysis, concept visualization, and literature searches, as well as core database and web services resources. Many of our training and outreach initiatives have been in collaboration with the Research Centers at Minority Institutions (RCMI), integrating NCIBI and RCMI faculty and students, culminating each year in an annual workshop. Our future directions include focusing on the TranSMART data sharing and analysis initiative.

A cognitive task analysis of a visual analytic workflow: Exploring molecular interaction networks in systems biology.

BACKGROUND: Bioinformatics visualization tools are often not robust enough to support biomedical specialists’ complex exploratory analyses. Tools need to accommodate the workflows that scientists actually perform for specific translational research questions. To understand and model one of these workflows, we conducted a case-based, cognitive task analysis of a biomedical specialist’s exploratory workflow for the question: What functional interactions among gene products of high throughput expression data suggest previously unknown mechanisms of a disease? RESULTS: From our cognitive task analysis four complementary representations of the targeted workflow were developed. They include: usage scenarios, flow diagrams, a cognitive task taxonomy, and a mapping between cognitive tasks and user-centered visualization requirements. The representations capture the flows of cognitive tasks that led a biomedical specialist to inferences critical to hypothesizing. We created representations at levels of detail that could strategically guide visualization development, and we confirmed this by making a trial prototype based on user requirements for a small portion of the workflow. CONCLUSIONS: Our results imply that visualizations should make available to scientific users â€Å“bundles of features” consonant with the compositional cognitive tasks purposefully enacted at specific points in the workflow. We also highlight certain aspects of visualizations that: (a) need more built-in flexibility; (b) are critical for negotiating meaning; and (c) are necessary for essential metacognitive support.

The Biomedical Resource Ontology (BRO) to enable resource discovery in clinical and translational research.

The biomedical research community relies on a diverse set of resources, both within their own institutions and at other research centers. In addition, an increasing number of shared electronic resources have been developed. Without effective means to locate and query these resources, it is challenging, if not impossible, for investigators to be aware of the myriad resources available, or to effectively perform resource discovery when the need arises. In this paper, we describe the development and use of the Biomedical Resource Ontology (BRO) to enable semantic annotation and discovery of biomedical resources. We also describe the Resource Discovery System (RDS) which is a federated, inter-institutional pilot project that uses the BRO to facilitate resource discovery on the Internet. Through the RDS framework and its associated Biositemaps infrastructure, the BRO facilitates semantic search and discovery of biomedical resources, breaking down barriers and streamlining scientific research that will improve human health.

Cross-domain neurobiology data integration and exploration.

BACKGROUND: Understanding the biomedical implications of data from high throughput experiments requires solutions for effective cross-scale and cross-domain data exploration. However, existing solutions do not provide sufficient support for linking molecular level data to neuroanatomical structures, which is critical for understanding high level neurobiological functions. RESULTS: Our work integrates molecular level data with high level biological functions and we present results using anatomical structure as a scaffold. Our solution also allows the sharing of intermediate data exploration results with other web applications, greatly increasing the power of cross-domain data exploration and mining. CONCLUSIONS: The Flex-based PubAnatomy web application we developed enables highly interactive visual exploration of literature and experimental data for understanding the relationships between molecular level changes, pathways, brain circuits and pathophysiological processes. The prototype of PubAnatomy is freely accessible at: [http://brainarray.mbni.med.umich.edu/Brainarray/prototype/PubAnatomy].

Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks.

SUMMARY: Metscape is a plug-in for Cytoscape, used to visualize and interpret metabolomic data in the context of human metabolic networks. We have developed a metabolite database by extracting and integrating information from several public sources. By querying this database, Metscape allows users to trace the connections between metabolites and genes, visualize compound networks and display compound structures as well as information for reactions, enzymes, genes and pathways. Applying the pathway filter, users can create subnetworks that consist of compounds and reactions from a given pathway. Metscape allows users to upload experimental data, and visualize and explore compound networks over time, or experimental conditions. Color and size of the nodes are used to visualize these dynamic changes. Metscape can display the entire metabolic network or any of the pathway-specific networks that exist in the database. AVAILABILITY: Metscape can be installed from within Cytoscape 2.6.x under 'Network and Attribute I/O' category. For more information, please visit http://metscape.ncibi.org/tryplugin.html.

User requirements for exploring a resource inventory for clinical research.

The CTSA Inventory of Resources Explorer facilitates searching and finding relevant biomedical resources in this rich, federated inventory. We used efficient and non-traditional formal usability methods to define requirements and to design the Explorer, which may be extended to similar web-based tools.

ConceptGen: a gene set enrichment and gene set relation mapping tool.

MOTIVATION: The elucidation of biological concepts enriched with differentially expressed genes has become an integral part of the analysis and interpretation of genomic data. Of additional importance is the ability to explore networks of relationships among previously defined biological concepts from diverse information sources, and to explore results visually from multiple perspectives. Accomplishing these tasks requires a unified framework for agglomeration of data from various genomic resources, novel visualizations, and user functionality. RESULTS: We have developed ConceptGen, a web-based gene set enrichment and gene set relation mapping tool that is streamlined and simple to use. ConceptGen offers over 20,000 concepts comprising 14 different types of biological knowledge, including data not currently available in any other gene set enrichment or gene set relation mapping tool. We demonstrate the functionalities of ConceptGen using gene expression data modeling TGF-beta-induced epithelial-mesenchymal transition and metabolomics data comparing metastatic versus localized prostate cancers.

Open Biomedical Ontology-based Medline exploration.

BACKGROUND: Effective Medline database exploration is critical for the understanding of high throughput experimental results and the development of novel hypotheses about the mechanisms underlying the targeted biological processes. While existing solutions enhance Medline exploration through different approaches such as document clustering, network presentations of underlying conceptual relationships and the mapping of search results to MeSH and Gene Ontology trees, we believe the use of multiple ontologies from the Open Biomedical Ontology can greatly help researchers to explore literature from different perspectives as well as to quickly locate the most relevant Medline records for further investigation. RESULTS: We developed an ontology-based interactive Medline exploration solution called PubOnto to enable the interactive exploration and filtering of search results through the use of multiple ontologies from the OBO foundry. The PubOnto program is a rich internet application based on the FLEX platform. It contains a number of interactive tools, visualization capabilities, an open service architecture, and a customizable user interface. It is freely accessible at: http://brainarray.mbni.med.umich.edu/brainarray/prototype/pubonto.

Supporting cognition in systems biology analysis: findings on users' processes and design implications.

BACKGROUND: Current usability studies of bioinformatics tools suggest that tools for exploratory analysis support some tasks related to finding relationships of interest but not the deep causal insights necessary for formulating plausible and credible hypotheses. To better understand design requirements for gaining these causal insights in systems biology analyses a longitudinal field study of 15 biomedical researchers was conducted. Researchers interacted with the same protein-protein interaction tools to discover possible disease mechanisms for further experimentation. RESULTS: Findings reveal patterns in scientists' exploratory and explanatory analysis and reveal that tools positively supported a number of well-structured query and analysis tasks. But for several of scientists' more complex, higher order ways of knowing and reasoning the tools did not offer adequate support. Results show that for a better fit with scientists' cognition for exploratory analysis systems biology tools need to better match scientists' processes for validating, for making a transition from classification to model-based reasoning, and for engaging in causal mental modelling. CONCLUSION: As the next great frontier in bioinformatics usability, tool designs for exploratory systems biology analysis need to move beyond the successes already achieved in supporting formulaic query and analysis tasks and now reduce current mismatches with several of scientists' higher order analytical practices. The implications of results for tool designs are discussed.

Integrating and annotating the interactome using the MiMI plugin for cytoscape.

UNLABELLED: The MiMI molecular interaction repository integrates data from multiple sources, resolves interactions to standard gene names and symbols, links to annotation data from GO, MeSH and PubMed and normalizes the descriptions of interaction type. Here, we describe a Cytoscape plugin that retrieves interaction and annotation data from MiMI and links out to multiple data sources and tools. Community annotation of the interactome is supported. AVAILABILITY: MiMI plugin v3.0.1 can be installed from within Cytoscape 2.6 using the Cytoscape plugin manager in 'Network and Attribute I/0' category. The plugin is also preloaded when Cytoscape is launched using Java WebStart at http://mimi.ncibi.org by querying a gene and clicking 'View in MiMI Plugin for Cytoscape' link.

Michigan molecular interactions r2: from interacting proteins to pathways.

Molecular interaction data exists in a number of repositories, each with its own data format, molecule identifier and information coverage. Michigan molecular interactions (MiMI) assists scientists searching through this profusion of molecular interaction data. The original release of MiMI gathered data from well-known protein interaction databases, and deep merged this information while keeping track of provenance. Based on the feedback received from users, MiMI has been completely redesigned. This article describes the resulting MiMI Release 2 (MiMIr2). New functionality includes extension from proteins to genes and to pathways; identification of highlighted sentences in source publications; seamless two-way linkage with Cytoscape; query facilities based on MeSH/GO terms and other concepts; approximate graph matching to find relevant pathways; support for querying in bulk; and a user focus-group driven interface design. MiMI is part of the NIH's; National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at: http://mimi.ncibi.org.

Integrating automated workflows, human intelligence and collaboration.

Many methods and tools have evolved for microarray analysis such as single probe evaluation, promoter module modeling and pathway analysis. Little is known, however, about optimizing this flow of analysis for the flexible reasoning biomedical researchers need for hypothesizing about disease mechanisms. In developing and implementing a workflow, we found that workflows are not complete or valuable unless automation is well-integrated with human intelligence. We present our workflow for the translational problem of classifying new sub-types of renal diseases. Using our workflow as an example, we explain opportunities and limitations in achieving this necessary integration and propose approaches to guide such integration for the next great frontier-facilitating exploratory analysis of candidate genes.

A diagram editor for efficient biomedical knowledge capture and integration.

Understanding the molecular mechanisms underlying complex disorders requires the integration of data and knowledge from different sources including free text literature and various biomedical databases. To facilitate this process, we created the Biomedical Concept Diagram Editor (BCDE) to help researchers distill knowledge from data and literature and aid the process of hypothesis development. A key feature of BCDE is the ability to capture information with a simple drag-and-drop. This is a vast improvement over manual methods of knowledge and data recording and greatly increases the efficiency of the biomedical researcher. BCDE also provides a unique concept matching function to enforce consistent terminology, which enables conceptual relationships deposited by different researchers in the BCDE database to be mined and integrated for intelligible and useful results. We hope BCDE will promote the sharing and integration of knowledge from different researchers for effective hypothesis development.

An active visual search interface for Medline.

Searching the Medline database is almost a daily necessity for many biomedical researchers. However, available Medline search solutions are mainly designed for the quick retrieval of a small set of most relevant documents. Because of this search model, they are not suitable for the large-scale exploration of literature and the underlying biomedical conceptual relationships, which are common tasks in the age of high throughput experimental data analysis and cross-discipline research. We try to develop a new Medline exploration approach by incorporating interactive visualization together with powerful grouping, summary, sorting and active external content retrieval functions. Our solution, PubViz, is based on the FLEX platform designed for interactive web applications and its prototype is publicly available at: http://brainarray.mbni.med.umich.edu/Brainarray/DataMining/PubViz.

Designing CIS to improve decisions in depression disease management: a discourse analysis of front line practice.

Clinical care management promises to help diminish the major health problem of depression. To realize this promise, front line clinicians must know which care management interventions are best for which patients and act accordingly. Unfortunately, the detailed intervention data required for such differentiated assessments are missing in most clinical information systems (CIS). To determine frontline clinicians' needs for these data and to identify the data that CIS should keep, we conducted an 18 month ethnographic study and discourse analysis of telehealth depression care management. Results show care managers need data-based evidence to choose best options, and discourse analysis suggests some personalized interventions that CIS should and can feasibly capture for evidence.