Genomics and informatics, conjoined tools vital for understanding and protecting plant health

Genomics’ impact on crop production continuously expands. The number of sequenced plant and microbial species and strains representing diverse populations of individual species rapidly increases thanks to the advent of next generation sequencing technologies. Their genomic blueprints revealed candidate genes involved in various functions and processes crucial for crop health and helped understand how the sequenced organisms have evolved at the genome level. Functional genomics quickly translates these blueprints into a detailed mechanistic understanding of how such functions and processes work and are regulated; this understanding guides and empowers efforts to protect crops from diverse biotic and abiotic threats. Metagenome analyses help identify candidate microbes crucial for crop health and uncover how microbial communities associated with crop production respond to environmental conditions and cultural practices, presenting opportunities to enhance crop health by judiciously configuring microbial communities. Efficient conversion of disparate types of massive genomics data into actionable knowledge requires a robust informatics infrastructure supporting data preservation, analysis, and sharing. This review starts with an overview of how genomics came about and has quickly transformed life science. We illuminate how genomics and informatics can be applied to investigate various crop health-related problems using selected studies. We end the review by noting why community empowerment via crowdsourcing is crucial to harnessing genomics to protect global food and nutrition security without continuously expanding the environmental footprint of crop production.

Hematology Patient Explorer - an Informatics Infrastructure and Web Application Empowering Clinicians and Researchers to Use Electronic Health Record Information to Identify Clinical Cohorts of Interest

Introduction: The widespread adoption of electronic health records (EHR) in the US (Adler-Milstein and Jha 2017) presents an opportunity to transform healthcare into rapidly learning health organizations and systems (Etheredge 2007; Abernethy et al. 2010) that use routinely collected clinical data in the course of care to generate evidence, address information disparities in patients underrepresented in clinical trials, such as those who are older, belong to ethnic minorities, or are in medically underserved areas, and continuously improve the quality of care delivered.(Rivera et al. 2019; Penberthy, Rivera, and Ward 2019) Unfortunately, this potential remains largely unrealized due to deficiencies in EHR interoperability (Holmgren, Patel, and Adler-Milstein 2017) and usability.(Dunn Lopez et al. 2021) EHR information remains largely unstructured due to the nature of the patient-clinician interaction and current user interfaces where structured data entry is burdensome.(Khairat et al. 2019) At the University of Utah Huntsman Cancer Institute division of Hematology, clinicians as well as clinical and translational researchers often need detailed information on patients seen at the cancer center to plan and conduct research and evaluate and improve the quality of care delivered. Previous efforts to address these needs relied on a clinical data science and health informatics staff working with clinicians to identify patient cohorts of interest. As part of an effort to improve the efficiency of this service, decrease the latency in information provision, and serve a larger number of clinicians and researchers, we designed and implemented a highly usable, scalable, health information technology solution that allows clinicians and researchers to identify, in near real time, cohorts of interest, based on patient and disease characteristics. Methods: Common information needs at the division were assessed by identifying key stakeholders, including division leadership, clinicians, researchers, and health informatics leadership and staff. A team was formed to include overlapping expertise in Hematology, clinical informatics and data science, and prior experience in extraction of clinical information from EHR data warehouses to generate evidence on patient practices and outcomes. Information sources and architectures were evaluated on their comprehensiveness, validity, extensibility, and ability to integrate multiple data sources. A modern web-based interface was designed to mirror the steps clinicians and clinical researchers frequently used to identify cohorts of interest, provide guidance for these users, and generate population level and patient level information. Usability testing included rounds of initial internal testing, followed by a round of closed beta testing. Results: A data lake architecture was implemented to ingest, harmonize and stage information from various data sources, including the Enterprise Data Warehouse, Tumor Registry, and other data silos. Data cleaning and harmonization was done using Python. A web application using 'Dash' was implemented with four steps split into four consecutive panes. The first two focused on cohort identification using diagnosis (first pane), and patient and disease characteristics (pane 2). Users first input keywords that allow them to identify diagnoses of interest based on ICDO-3 codes. Users can select multiple codes at this stage. In the next step, users can filter down their cohort based on patient and disease characteristics, such as sex, age, year of initial diagnosis, grade and stage of disease, and others. Once all desired refinements to the pilot cohort are made, users move on to the third tab, which displays customizable population level information, such as number of diagnoses by year, sex, or age. Finally, the fourth tab presents individual level data such as Patient Medical Record Numbers, age, or ICDO-3 diagnosis descriptions. Based on this output, users can return to the initial selection criteria and adjust them. Users are able to download or export selection criteria and query results for future work. Conclusion: We describe the process, roles, and informatics infrastructure and tools to implement a web interface that allows clinicians and researchers to leverage EHR information to identify cohorts of patients for research and quality improvement. Further research will focus on tool usability and scope. Disclosures Deininger: Sangamo: Consultancy, Membership on an entity's Board of Directors or advisory committees; Fusion Pharma, Medscape, DisperSol: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Part of a Study Management Committee, Research Funding; SPARC, DisperSol, Leukemia & Lymphoma Society: Research Funding; Novartis: Consultancy, Research Funding; Incyte: Consultancy, Honoraria, Research Funding; Blueprint Medicines Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Part of a Study Management Committee, Research Funding.

Challenges encountered in comparing international policy responses to COVID-19 and their effects

Background A variety of policies have been implemented around the world in response to the COVID-19 pandemic. This study originally aimed to identify and compare policy responses of different countries and their effects on the pandemic. It quickly evolved into an identification of the heterogeneity among existing policies and the challenges in making meaningful comparisons of the impact of these policies. Methods The process of collecting and comparing data from different sources was analysed through inductive thematic analysis to understand the obstacles that impede research designed to compare COVID-19 data and related policies. Results We identified the following obstacles: (1) no single reputable source of information and too much noise; (2) a lack of standards for how to measure and report data across countries; (3) variations in the content, implementation and enforcement of policies; and (4) politics, instead of science, leading the efforts in pandemic management. Conclusion Heterogeneity in existing policies makes it challenging to compare the effects of various policies worldwide on the COVID-19 pandemic. Our findings call for an automatically updated informatics infrastructure across the globe for collecting and maintaining standardized data from multiple sources. There is a strong need for steadfast utilization of scientific and technical experts to inform and influence health policy. Increased investment in public health and emergency planning is essential to overcome the current pandemic, as well as future public health emergencies. Focused leadership and collaboration from world leaders in a unified mission to decrease the mortality and morbidity of the COVID-19 pandemic is imperative.

Innovation in Informatics to Improve Clinical Care and Drug Accessibility for Rare Diseases in China

Background: In China, there are severe unmet medical needs of people living with rare diseases. Relatedly, there is a dearth of data to inform rare diseases policy. This is historically partially due to the lack of informatics infrastructure, including standards and terminology, data sharing mechanisms and network; and concerns over patient privacy protection.Objective: This study aims to introduce the progress of China's rare disease informatics platform and knowledgebase, and to discuss critical enablers of rare disease informatics innovation, including: data standardization; knowledgebase construction; national policy support; and multi-stakeholder participation.Methods: A systemic national strategy, delivered through multi-stakeholder engagement, has been implemented to create and accelerate the informatics infrastructure to support rare diseases management. This includes a disease registry system, together with more than 80 hospitals, to perform comprehensive research information collection, including clinical, genomic and bio-sample data. And a case reporting system, with a network of 324 hospitals, covering all mainland Chinese provinces, to further support reporting of rare diseases data. International standards were incorporated, and privacy issues were addressed through HIPAA compliant rules.Results: The National Rare Diseases Registry System of China (NRDRS) now covers 166 rare diseases and more than 63,000 registered patients. The National Rare Diseases Case Reporting System of China (NRDCRS) was primarily founded on the National Network of Rare Diseases (NNRD) of 324 hospitals and focused on real-time rare diseases case reporting; more than 400,000 cases have been reported. Based on the data available in the two systems, the National Center for Health Technology Assessment (HTA) of Orphan Medicinal Products (OMP) has been established and the expert consensus on HTA of OMP was produced. The largest knowledgebase for rare disease in Chinese has also been developed.Conclusion: A national strategy and the coordinating mechanism is the key to success in the improvement of Chinese rare disease clinical care and drug accessibility. Application of innovative informatics solutions can help accelerate the process, improve quality and increase efficiency.

CitSci.org & PPSR Core: Sharing biodiversity observations across platforms

CitSci.org is a global citizen science software platform and support organization housed at Colorado State University. The mission of CitSci is to help people do high quality citizen science by amplifying impacts and outcomes. This platform hosts over one thousand projects and a diverse volunteer base that has amassed over one million observations of the natural world, focused on biodiversity and ecosystem sustainability. It is a custom platform built using open source components including: PostgreSQL, Symfony, Vue.js, with React Native for the mobile apps. CitSci sets itself apart from other Citizen Science platforms through the flexibility in the types of projects it supports rather than having a singular focus. This flexibility allows projects to define their own datasheets and methodologies. The diversity of programs we host motivated us to take a founding role in the design of the PPSR Core, a set of global, transdisciplinary data and metadata standards for use in Public Participation in Scientific Research (Citizen Science) projects. Through an international partnership between the Citizen Science Association, European Citizen Science Association, and Australian Citizen Science Association, the PPSR team and associated standards enable interoperability of citizen science projects, datasets, and observations. Here we share our experience over the past 10+ years of supporting biodiversity research both as developers of the CitSci.org platform and as stewards of, and contributors to, the PPSR Core standard. Specifically, we share details about: the origin, development, and informatics infrastructure for CitSci our support for biodiversity projects such as population and community surveys our experiences in platform interoperability through PPSR Core working with the Zooniverse, SciStarter, and CyberTracker data quality data sharing goals and use cases. the origin, development, and informatics infrastructure for CitSci our support for biodiversity projects such as population and community surveys our experiences in platform interoperability through PPSR Core working with the Zooniverse, SciStarter, and CyberTracker data quality data sharing goals and use cases. We conclude by sharing overall successes, limitations, and recommendations as they pertain to trust and rigor in citizen science data sharing and interoperability. As the scientific community moves forward, we show that Citizen Science is a key tool to enabling a systems-based approach to ecosystem problems.

Annotated expression and activity data for murine recombinase alleles and transgenes: the CrePortal resource

Recombinase alleles and transgenes can be used to facilitate spatio-temporal specificity of gene disruption or transgene expression. However, the versatility of this in vivo recombination system relies on having detailed and accurate characterization of recombinase expression and activity to enable selection of the appropriate allele or transgene. The CrePortal (http://www.informatics.jax.org/home/recombinase) leverages the informatics infrastructure of Mouse Genome Informatics to integrate data from the scientific literature, direct data submissions from the scientific community at-large, and from major projects developing new recombinase lines and characterizing recombinase expression and specificity patterns. Searching the CrePortal by recombinase activity or specific recombinase gene driver provides users with a recombinase alleles and transgenes activity tissue summary and matrix comparison of gene expression and recombinase activity with links to generation details, a recombinase activity grid, and associated phenotype annotations. Future improvements will add cell type-based activity annotations. The CrePortal provides a comprehensive presentation of recombinase allele and transgene data to assist researchers in selection of the recombinase allele or transgene based on where and when recombination is desired.

Design and Implementation of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage, and Export in Psychiatric Clinical Routine: Feasibility Study

Background Empirically driven personalized diagnostic applications and treatment stratification is widely perceived as a major hallmark in psychiatry. However, databased personalized decision making requires standardized data acquisition and data access, which are currently absent in psychiatric clinical routine. Objective Here, we describe the informatics infrastructure implemented at the psychiatric Münster University Hospital, which allows standardized acquisition, transfer, storage, and export of clinical data for future real-time predictive modelling in psychiatric routine. Methods We designed and implemented a technical architecture that includes an extension of the electronic health record (EHR) via scalable standardized data collection and data transfer between EHRs and research databases, thus allowing the pooling of EHRs and research data in a unified database and technical solutions for the visual presentation of collected data and analyses results in the EHR. The Single-source Metadata ARchitecture Transformation (SMA:T) was used as the software architecture. SMA:T is an extension of the EHR system and uses module-driven engineering to generate standardized applications and interfaces. The operational data model was used as the standard. Standardized data were entered on iPads via the Mobile Patient Survey (MoPat) and the web application Mopat@home, and the standardized transmission, processing, display, and export of data were realized via SMA:T. Results The technical feasibility of the informatics infrastructure was demonstrated in the course of this study. We created 19 standardized documentation forms with 241 items. For 317 patients, 6451 instances were automatically transferred to the EHR system without errors. Moreover, 96,323 instances were automatically transferred from the EHR system to the research database for further analyses. Conclusions In this study, we present the successful implementation of the informatics infrastructure enabling standardized data acquisition and data access for future real-time predictive modelling in clinical routine in psychiatry. The technical solution presented here might guide similar initiatives at other sites and thus help to pave the way toward future application of predictive models in psychiatric clinical routine.

An Informatics Infrastructure to Catalyze Cancer Control Research and Practice

PurposeA disconnect often exists between those with the expertise to manage and analyze complex, multi-source data sets, and the clinical, social services, advocacy, and public health professionals who can pose the most relevant questions and best apply the answers. We describe development and implementation of a cancer informatics infrastructure aimed at broadening the usability of community cancer data to inform cancer control research and practice; and we share lessons learned. MethodsWe built a multi-level database known as The Ohio Cancer Assessment and Surveillance Engine (OH-CASE) to link data from Ohio’s cancer registry with community data from the U.S. Census and other sources. Space- and place-based characteristics were assigned to individuals according to residential address. Stakeholder input informed development of an interface for generating queries based on geographic, demographic, and disease inputs and for outputting results aggregated at the state, county, municipality, or zip code levels. ResultsOH-CASE contains 723,410 patient records for Ohioans diagnosed with cancer from 1/1/2006 – 12/31/2017 across 88 counties containing 1215 municipalities and 1197 zip codes. Stakeholder feedback from cancer center community outreach teams, advocacy organizations, public health, and researchers suggests a broad range of uses of such multi-level data resources accessible via a user interface. ConclusionOH-CASE represents a prototype of a transportable model for curating and synthesizing data to understand cancer burden across communities. Beyond supporting collaborative research, this infrastructure can serve the clinical, social services, public health, and advocacy communities by enabling targeting of outreach, funding, and interventions to narrow cancer disparities.

Need and Importance of Nutrition Informatics in India: A Perspective

Nutrition informatics (NI) is the effective retrieval, organization, storage, and optimum use of information, data and knowledge for food-and-nutrition-related problem-solving and decision-making. There is a growing opportunity to facilitate technology-enabled behavioral change interventions to support NI research and practice. This paper highlights the changing landscape of food and nutrition practices in India to prepare a NI workforce that could provide some valuable tools to address the double burden of nutrition. Management and interpretation of data could help clarify the relationships and interrelationships of diet and disease in India on both national and regional levels. Individuals with expertise in food and nutrition may receive training in informatics to develop national informatics systems. NI professionals develop tools and techniques, manage various projects and conduct informatics research. These professionals should be well prepared to work in technological settings and communicate data and information effectively. Opportunities for training in NI are very limited in developing countries. Given the current progress in developing platforms and informatics infrastructure, India could serve as an example to other countries to promote NI to support achieving SDGs and other public health initiatives.