Melissa Haendel - US grants

The objective of the proposed research is to determine in zebrafish the molecular mechanisms that underlie the specification of the first neurons, those of the early-escape reflex. Primary motor and sensory neurons have similar genetic programs that are executed in response to different dorsal- ventral signaling. I propose to elucidate how these programs are signaled and how they determine cell specification. I will test the role that the c-ret receptor plays in local signaling by examining the expression pattern during development. I will determine if the differentiation of primary neurons requires expression of c-ret, and if c-ret is sufficient to drive differentiation of primary motor and sensory neurons. I will also ascertain the expression pattern of the putative c-ret ligand, GDNF, to see whether it is required for maintenance of primary sensory and motor neurons. Programmed cell death of some sensory neurons is coincident with decreasing levels of thyroid hormone. I will test if the specification and maintenance of other primary neurons is also dependent on alterations in long-range thyroid hormone signaling. Finally, I will examine the expression pattern of thyroid hormone receptors and their dimerization partners, the retinoid X receptors. Perturbation of the signaling with receptor antagonists should reveal the requirement for hormone action in the specification or maintenance of zebrafish primary neurons.

DESCRIPTION (provided by applicant): Our understanding of human disease is vitally informed by the use of model systems to investigate disease mechanisms and therapies. Because identifying disease models is so critical, a significant effort has been made to connect researchers to potential models of disease by bringing together information about diseases and model organisms via gene orthology and pathway links in LAMHDI, the initiative to Link Animal Models to Human Disease portal (www.lamhdi.org). However, LAMHDI is currently incomplete in that in vitro biological models are not yet included and one cannot identify model systems based on phenotype similarity to a human disease. Furthermore, there is no mechanism by which to prioritize the myriad of relationships between disease and their models, making both identification of candidate models and discovery of new relationships difficult. The goal of this work is to facilitate the identification of models for disease research, make better use of existin model organisms and in vitro resources and data about them, and provide the ability to uncover new relationships between disease, phenotypes and genes that will further our understanding of disease. To this end we propose to: 1) Enable computation of candidate disease models based on semantic similarity of phenotypes using imported and aligned phenotype data from humans and model organisms. We will include expression data to refine search of phenotypes based on presence of expression within a particular anatomical location and/or genotype. 2) Expand semantic linkage between diseases and in vitro model systems within LAMHDI, including resources such as biospecimens and cell lines from the eagle-i project and external sources. This will permit investigators to identify candidate in vitro model systems based on phenotype or genetic basis. 3) Create a discovery tool to refine searches and to uncover novel relationships between diseases, model organisms, and in vitro resources using genetic, pathway, and phenotype relationships. To bring together the disparate data required, we leverage semantic web technologies and sophisticated information modeling combined with computational algorithms. Usability studies will inform the iterative development of the new knowledge-guided discovery LAMDHI interface. PUBLIC HEALTH RELEVANCE (provided by applicant): This work proposed here will facilitate the identification of model systems for disease research. This will be accomplished by bringing together data from many different organisms, in vitro resources, and diseases in one central web portal. We believe that this system will not only make better use of our existing organisms and resources and data about them, but it will provide the ability to uncover new information about disease that will further our understanding of disease mechanisms and potential therapies.

DESCRIPTION: The overall goal of this project is to develop open educational resources for use in courses, programs, and related activities in Big Data to Knowledge (BD2K). The focus of the materials will be at the advanced introductory level, including such people as beginning informatics graduate students, established investigators and senior trainees seeking to learn more about data science to expand their research programs, advanced undergraduates exploring future career paths into data science, and established professionals who would benefit from knowledge of the application of BD2K concepts. The content developed will use the same format as the highly successful Office of the National Coordinator for Health IT (ONC) curriculum materials. The development of the materials was led by Oregon Health & Science University (OHSU) and four other universities, with support and dissemination led by OHSU. The PI of this proposal served as one of five PIs leading the development of content as well as lead for the ONC National Training and Dissemination Center (NTDC) that provided support and Web-based dissemination of the materials. The value of using the ONC Health IT Curriculum approach includes an open format that provides both out of the box content as well as the source materials for that content. This will also provide us the ability to map content from the new BD2K materials back to other content in the ONC curriculum, such as structure and function of the health care system, basic computer and information science, standards and interoperability, electronic health records, and project management and leadership. It will also enable us to build on the foundation of the ONC materials to emphasize the value of Big Data and analytical approaches in clinical and translational research. Finally, it will allow us to draw on the experience and leadership of OHSU and others who contributed to developing, evaluating, and disseminating the curricular content. In the project, we will carry out a needs assessment of a wide variety of potential BD2K users to refine our initial views on what should be part of a curriculum, develop and disseminate the curricular materials using the ONC curriculum format, evaluate the materials with all major user groups, build a community of users of the materials who can participate in evaluation and provide other feedback. We will repeat the cycle of needs assessment, development and dissemination, and evaluation for two 18-month periods.

DESCRIPTION: We propose to create and disseminate an OHSU Informatics Analytics BD2K Skill Course to enable researchers and students at any career stage to gain crucial analytics and data skills and competencies as part of the Big Data to Knowledge (BD2K) initiative. Although the potential of big data to advance research is enormous, our needs assessment has identified that a wide range of researchers and graduate students lack fundamental skills in identifying the appropriate data sets to answer their hypotheses, in managing and 'wrangling' data to prepare it for analysis, and in using both common and advanced methods to analyze the data. These needs varied by experience and skill of the researcher. We therefore propose three educational components: the first is to recruit a diverse set of participants and work to inculcate a fundamental set of competencies in basic big data skills; the second is to create realistic but synthetic large datasets with embedded signals where students can practice their skills in collaborative and interactive exercises; the third is to create advanced challenges to help researchers test and implement more advanced methodologies. We anticipate the impact to be substantial in enabling researchers to improve their use of big data to more rapidly and accurately address important research hypotheses. We will disseminate this work via online curricula resources and public venues and share the datasets created so that others may train their own teams.

This award provides funds to support travel of students and post-doctoral fellows from the US to attend the Future of Research Communication and e-Scholarship conference (FORCE2016) to be held in Portland, OR, April 17-19, 2016. This conference is the successor to the highly regarded Beyond the PDF conferences, which has been held twice since 2011, and the Force2015 conference, which was held in Oxford, UK, in January 2015. The meeting is organized by the FORCE11, a grass roots community dedicated to transforming scholarly communication through technology. FORCE11 brings together researchers, librarians, publishers, technologists, funders and administrators for a truly trans-disciplinary summit and lively discussion around opportunities, challenges, and progress in advancing research scholarship through new technologies and practices. FORCE2016 examines current issues and tools, and connects individuals and communities who are working on overlapping or complementary aspects of various problems. The conference is unique in its breadth of participants and its success in mobilizing and coordinating global activities across groups that have traditionally worked separately, but each of whom has something unique to contribute to the discussion. We believe that the topic of the conference is particularly important in the sciences, with issues like incorporating new practices and content into current systems, such as data sharing, data citation, nanopublications, and research objects. When science is done more efficiently and effectively, all of society benefits. The conference exposes students and postdocs to new disciplines, tools, role models and ways of thinking around the issues of scholarly communication.

DESCRIPTION: The overall goal of this project is to develop open educational resources for use in courses, programs, and related activities in Big Data to Knowledge (BD2K). The focus of the materials will be at the advanced introductory level, including such people as beginning informatics graduate students, established investigators and senior trainees seeking to learn more about data science to expand their research programs, advanced undergraduates exploring future career paths into data science, and established professionals who would benefit from knowledge of the application of BD2K concepts. The content developed will use the same format as the highly successful Office of the National Coordinator for Health IT (ONC) curriculum materials. The development of the materials was led by Oregon Health & Science University (OHSU) and four other universities, with support and dissemination led by OHSU. The PI of this proposal served as one of five PIs leading the development of content as well as lead for the ONC National Training and Dissemination Center (NTDC) that provided support and Web-based dissemination of the materials. The value of using the ONC Health IT Curriculum approach includes an open format that provides both out of the box content as well as the source materials for that content. This will also provide us the ability to map content from the new BD2K materials back to other content in the ONC curriculum, such as structure and function of the health care system, basic computer and information science, standards and interoperability, electronic health records, and project management and leadership. It will also enable us to build on the foundation of the ONC materials to emphasize the value of Big Data and analytical approaches in clinical and translational research. Finally, it will allow us to draw on the experience and leadership of OHSU and others who contributed to developing, evaluating, and disseminating the curricular content. In the project, we will carry out a needs assessment of a wide variety of potential BD2K users to refine our initial views on what should be part of a curriculum, develop and disseminate the curricular materials using the ONC curriculum format, evaluate the materials with all major user groups, build a community of users of the materials who can participate in evaluation and provide other feedback. We will repeat the cycle of needs assessment, development and dissemination, and evaluation for two 18-month periods.

PROJECT SUMMARY Genomic approaches are poised to revolutionize medicine by providing the basis for precision therapies and diagnostics, but it remains difficult and in many cases impossible to interpret genomic data without knowledge of the clinical situation. Phenomics is a new field that aims to collect, exchange, and analyze clinical phenotype data and integrate it into the clinical and translational assessment of genomics data. A critical challenge for genomic medicine is therefore to identify the genetic etiologies and environmental factors that underlie Mendelian disease, cancer, and common and complex diseases. However, interpretation of genomic data requires a fundamental understanding of phenotypic data, as well as new algorithms, tools, and data structures that can operate on them. Here, we propose to create the Forums For Integrative Phenomics (FFIP) to enable and accelerate scientific discoveries for maximizing use of phenotypic data across the translational landscape for improved patient care, and for precision medicine. FFIP events will governed by Phenopackets, a community-based organization with currently >400 members spanning diverse stakeholder groups whose vision is to develop and promote Open and Computable Knowledge-sharing Technologies designed for capturing, sharing, and exploiting structured bioinformatic phenotype knowledge. The FFIP events will enable phenomics researchers from different areas and roles in basic, clinical, and translational research to share their ideas, tools, algorithms and data in order to address these shared challenges. For each of three years, the FFIP will facilitate one annual meeting coupled with three community workshops and hackathons. All events will focus on state-of-the-art approaches for phenotype data analysis that complement and work synergistically with genomic analyses. FFIP events will facilitate the development of and training in emerging software and standards for Big Data in phenomics, and will foster collaboration amongst young researchers, underrepresented minorities, women, and those in less prominent or traditional research roles. A key theme will be transitioning from traditional, low-throughput methods of measuring and reporting phenotypes towards big-data approaches. The FFIP events will therefore enable participants to contribute to, access, integrate, and analyze diverse phenomic data that will enable diagnoses, treatment selection, and new discoveries.

A Phenomics-First Resource (PFR) for interpretation of variants Genomics is key to precision medicine; however, despite the ease of sequencing, clinical interpretation is still thwarted because relevant data (disease, phenotype, and variant) is complex, heterogeneous, and disaggregated across sources. Moreover, this evidence is sometimes incomplete, conflicting, and erroneous. Consequently, clinicians face long lists of candidate diseases, genes, and countless variants of unknown significance. This situation will not improve without capturing and harmonizing the underlying phenotypic information; computability of this information is the bedrock for the emerging field of ?phenomics?. From basic science to clinical care, communities need structured ways to represent and exchange phenotypes and disease definitions. Addressing these fundamental phenomics needs makes it possible to computationally assess and reveal links between diseases and variants. We have previously shown how the addition of phenotypic information using the Human Phenotype Ontology (HPO) can improve the diagnostic yield for hard-to-diagnose patients, and HPO is therefore now a global standard for ?deep phenotyping?. We have demonstrated the applicability of deep phenotyping in the evaluation of rare diseases which have overlapping mechanistic underpinnings with common/complex diseases as well as evolutionarily conserved mechanisms in model organisms. Having coordinated the community and prototyped the underlying computational platforms, we will now align both phenotype ontologies and clinical terminologies, enabling better comparison and inference of phenotypes for improved diagnostic efficacy. We propose to develop a Phenomics-First Resource (PFR). ?Specifically we will: 1. Create a community-driven framework of interoperable phenotype definitions across species? (uPheno) 2. Harmonize human disease definitions with the ?MONDO? disease alignment resource 3. Create a community-wide exchange standard for clinical and model-organism phenotypes (?Phenopackets?) 4. Develop an integrated phenomics platform ?to provide the research ?(e.g. BioLink) and clinical (?FHIR?) communities with programmatic access to phenomics ontologies, data, and algorithms The dynamic suite of interlinked technologies will together leverage community-developed knowledge in order to make variant interpretation more reliable, better provenanced, and more clinically actionable.

PROJECT SUMMARY Over the past decade, the CTSAs have made incredible progress in developing informatics tools and services to support institutional or hub-centric translational research. However, robust collaboration between the hubs is required to realize the full potential of the CTSA network, a living ecosystem of collaboration and sharing. We have brought together pioneers of open science, clinical and biomedical informatics, health science libraries, and the NCATS Translator initiative to propose the creation of a National Center for Digital Health Innovation to coalesce the CTSA informatics community together with the open science community to catalyze the next steps in the evolution of biomedical informatics. We will create an open governance model that includes all who would like to participate and collaboratively forge the next steps. We will embrace an ?idea to implementation? model (I2I), supporting the creation of collaboratively developed sustainable infrastructure. We will do this via the axes of open data, open software, open resources, and through two integrated thematic areas of urgent need: rare disease and human health across the lifespan. Underpinning these efforts are innovative models for evaluation, training, and the creation of community challenges as mechanisms to stimulate new and impactful tools and methods. We will leverage the power of an open and team science approach to demonstrate new research opportunities. Our leadership include Oregon Health Sciences University, Johns Hopkins University, the University of Washington, Washington University in St. Louis, Northwestern University, Sage BioNetworks, the Jackson Laboratory and the Scripps Research Institute. We have collaborators from all areas of translational informatics including health record vendors, clinical research networks and leaders in open software and open data. Together our efforts will catalyze the next stage in the evolution of the national informatics infrastructure for clinical and translational research.

PROJECT SUMMARY Over the past decade, the CTSAs have made incredible progress in developing informatics tools and services to support institutional or hub-centric translational research. However, robust collaboration between the hubs is required to realize the full potential of the CTSA network, a living ecosystem of collaboration and sharing. We have brought together pioneers of open science, clinical and biomedical informatics, health science libraries, and the NCATS Translator initiative to propose the creation of a National Center for Digital Health Innovation to coalesce the CTSA informatics community together with the open science community to catalyze the next steps in the evolution of biomedical informatics. We will create an open governance model that includes all who would like to participate and collaboratively forge the next steps. We will embrace an `idea to implementation' model (I2I), supporting the creation of collaboratively developed sustainable infrastructure. We will do this via the axes of open data, open software, open resources, and through two integrated thematic areas of urgent need: rare disease and human health across the lifespan. Underpinning these efforts are innovative models for evaluation, training, and the creation of community challenges as mechanisms to stimulate new and impactful tools and methods. We will leverage the power of an open and team science approach to demonstrate new research opportunities. Our leadership include Oregon Health Sciences University, Johns Hopkins University, the University of Washington, Washington University in St. Louis, Northwestern University, Sage BioNetworks, the Jackson Laboratory and the Scripps Research Institute. We have collaborators from all areas of translational informatics including health record vendors, clinical research networks and leaders in open software and open data. Together our efforts will catalyze the next stage in the evolution of the national informatics infrastructure for clinical and translational research.