RADx-UP Testing Core: Access to COVID-19 Diagnostics in Community-Engaged Research with Underserved Populations.

Research on the COVID-19 pandemic revealed a disproportionate burden of COVID-19 infection and death among underserved populations and exposed low rates of SARS-CoV-2 testing in these communities. A landmark National Institutes of Health (NIH) funding initiative, the Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program, was developed to address the research gap in understanding the adoption of COVID-19 testing in underserved populations. This program is the single largest investment in health disparities and community-engaged research in the history of the NIH. The RADx-UP Testing Core (TC) provides community-based investigators with essential scientific expertise and guidance on COVID-19 diagnostics. This commentary describes the first 2 years of the TC's experience, highlighting the challenges faced and insights gained to safely and effectively deploy large-scale diagnostics for community-initiated research in underserved populations during a pandemic. The success of RADx-UP shows that community-based research to increase access and uptake of testing among underserved populations can be accomplished during a pandemic with tools, resources, and multidisciplinary expertise provided by a centralized testing-specific coordinating center. We developed adaptive tools to support individual testing strategies and frameworks for these diverse studies and ensured continuous monitoring of testing strategies and use of study data. In a rapidly evolving setting of tremendous uncertainty, the TC provided essential and real-time technical expertise to support safe, effective, and adaptive testing. The lessons learned go beyond this pandemic and can serve as a framework for rapid deployment of testing in response to future crises, especially when populations are affected inequitably.

Informatics tools to implement late cardiovascular risk prediction modeling for population management of high-risk childhood cancer survivors.

BACKGROUND: Clinical informatics tools to integrate data from multiple sources have the potential to catalyze population health management of childhood cancer survivors at high risk for late heart failure through the implementation of previously validated risk calculators. METHODS: The Oklahoma cohort (n = 365) harnessed data elements from Passport for Care (PFC), and the Duke cohort (n = 274) employed informatics methods to automatically extract chemotherapy exposures from electronic health record (EHR) data for survivors 18 years old and younger at diagnosis. The Childhood Cancer Survivor Study (CCSS) late cardiovascular risk calculator was implemented, and risk groups for heart failure were compared to the Children's Oncology Group (COG) and the International Guidelines Harmonization Group (IGHG) recommendations. Analysis within the Oklahoma cohort assessed disparities in guideline-adherent care. RESULTS: The Oklahoma and Duke cohorts both observed good overall concordance between the CCSS and COG risk groups for late heart failure, with weighted kappa statistics of .70 and .75, respectively. Low-risk groups showed excellent concordance (kappa > .9). Moderate and high-risk groups showed moderate concordance (kappa .44-.60). In the Oklahoma cohort, adolescents at diagnosis were significantly less likely to receive guideline-adherent echocardiogram surveillance compared with survivors younger than 13 years old at diagnosis (odds ratio [OD] 0.22; 95% confidence interval [CI]: 0.10-0.49). CONCLUSIONS: Clinical informatics tools represent a feasible approach to leverage discrete treatment-related data elements from PFC or the EHR to successfully implement previously validated late cardiovascular risk prediction models on a population health level. Concordance of CCSS, COG, and IGHG risk groups using real-world data informs current guidelines and identifies inequities in guideline-adherent care.

At-home testing to mitigate community transmission of SARS-CoV-2: protocol for a public health intervention with a nested prospective cohort study.

BACKGROUND: The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve as a global health crisis. Although highly effective vaccines have been developed, non-pharmaceutical interventions remain critical to controlling disease transmission. One such intervention-rapid, at-home antigen self-testing-can ease the burden associated with facility-based testing programs and improve testing access in high-risk communities. However, its impact on SARS-CoV-2 community transmission has yet to be definitively evaluated, and the socio-behavioral aspects of testing in underserved populations remain unknown. METHODS: As part of the Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program funded by the National Institutes of Health, we are implementing a public health intervention titled "Say Yes! COVID Test" (SYCT) involving at-home self-testing using a SARS-CoV-2 rapid antigen assay in North Carolina (Greenville, Pitt County) and Tennessee (Chattanooga City, Hamilton County). The intervention is supported by a multifaceted communication and community engagement strategy to ensure widespread awareness and uptake, particularly in marginalized communities. Participants receive test kits either through online orders or via local community distribution partners. To assess the impact of this intervention on SARS-CoV-2 transmission, we will conduct a non-randomized, ecological study using community-level outcomes. Specifically, we will evaluate trends in SARS-CoV-2 cases and hospitalizations, SARS-CoV-2 viral load in wastewater, and population mobility in each community before, during, and after the SYCT intervention. Individuals who choose to participate in SYCT will also have the option to enroll in an embedded prospective cohort substudy gathering participant-level data to evaluate behavioral determinants of at-home self-testing and socio-behavioral mechanisms of SARS-CoV-2 community transmission. DISCUSSION: This is the first large-scale, public health intervention implementing rapid, at-home SARS-CoV-2 self-testing in the United States. The program consists of a novel combination of an at-home testing program, a broad communications and community engagement strategy, an ecological study to assess impact, and a research substudy of the behavioral aspects of testing. The findings from the SYCT project will provide insights into innovative methods to mitigate viral transmission, advance the science of public health communications and community engagement, and evaluate emerging, novel assessments of community transmission of disease.

Prediction of genome-wide DNA methylation in repetitive elements.

DNA methylation in repetitive elements (RE) suppresses their mobility and maintains genomic stability, and decreases in it are frequently observed in tumor and/or surrogate tissues. Averaging methylation across RE in genome is widely used to quantify global methylation. However, methylation may vary in specific RE and play diverse roles in disease development, thus averaging methylation across RE may lose significant biological information. The ambiguous mapping of short reads by and high cost of current bisulfite sequencing platforms make them impractical for quantifying locus-specific RE methylation. Although microarray-based approaches (particularly Illumina's Infinium methylation arrays) provide cost-effective and robust genome-wide methylation quantification, the number of interrogated CpGs in RE remains limited. We report a random forest-based algorithm (and corresponding R package, REMP) that can accurately predict genome-wide locus-specific RE methylation based on Infinium array profiling data. We validated its prediction performance using alternative sequencing and microarray data. Testing its clinical utility with The Cancer Genome Atlas data demonstrated that our algorithm offers more comprehensively extended locus-specific RE methylation information that can be readily applied to large human studies in a cost-effective manner. Our work has the potential to improve our understanding of the role of global methylation in human diseases, especially cancer.

Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data.

The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.

Linking short tandem repeat polymorphisms with cytosine modifications in human lymphoblastoid cell lines.

Inter-individual variation in cytosine modifications has been linked to complex traits in humans. Cytosine modification variation is partially controlled by single nucleotide polymorphisms (SNPs), known as modified cytosine quantitative trait loci (mQTL). However, little is known about the role of short tandem repeat polymorphisms (STRPs), a class of structural genetic variants, in regulating cytosine modifications. Utilizing the published data on the International HapMap Project lymphoblastoid cell lines (LCLs), we assessed the relationships between 721 STRPs and the modification levels of 283,540 autosomal CpG sites. Our findings suggest that, in contrast to the predominant cis-acting mode for SNP-based mQTL, STRPs are associated with cytosine modification levels in both cis-acting (local) and trans-acting (distant) modes. In local scans within the ±1 Mb windows of target CpGs, 21, 9, and 21 cis-acting STRP-based mQTL were detected in CEU (Caucasian residents from Utah, USA), YRI (Yoruba people from Ibadan, Nigeria), and the combined samples, respectively. In contrast, 139,420, 76,817, and 121,866 trans-acting STRP-based mQTL were identified in CEU, YRI, and the combined samples, respectively. A substantial proportion of CpG sites detected with local STRP-based mQTL were not associated with SNP-based mQTL, suggesting that STRPs represent an independent class of mQTL. Functionally, genetic variants neighboring CpG-associated STRPs are enriched with genome-wide association study (GWAS) loci for a variety of complex traits and diseases, including cancers, based on the National Human Genome Research Institute (NHGRI) GWAS Catalog. Therefore, elucidating these STRP-based mQTL in addition to SNP-based mQTL can provide novel insights into the genetic architectures of complex traits.

DictyBase 2013: integrating multiple Dictyostelid species.

dictyBase (http://dictybase.org) is the model organism database for the social amoeba Dictyostelium discoideum. This contribution provides an update on dictyBase that has been previously presented. During the past 3 years, dictyBase has taken significant strides toward becoming a genome portal for the whole Amoebozoa clade. In its latest release, dictyBase has scaled up to host multiple Dictyostelids, including Dictyostelium purpureum [Sucgang, Kuo, Tian, Salerno, Parikh, Feasley, Dalin, Tu, Huang, Barry et al.(2011) (Comparative genomics of the social amoebae Dictyostelium discoideum and Dictyostelium purpureum. Genome Biol., 12, R20)], Dictyostelium fasciculatum and Polysphondylium pallidum [Heidel, Lawal, Felder, Schilde, Helps, Tunggal, Rivero, John, Schleicher, Eichinger et al. (2011) (Phylogeny-wide analysis of social amoeba genomes highlights ancient origins for complex intercellular communication. Genome Res., 21, 1882-1891)]. The new release includes a new Genome Browser with RNAseq expression, interspecies Basic Local Alignment Search Tool alignments and a unified Basic Local Alignment Search Tool search for cross-species comparisons.

The Disease and Gene Annotations (DGA): an annotation resource for human disease.

Disease and Gene Annotations database (DGA, http://dga.nubic.northwestern.edu) is a collaborative effort aiming to provide a comprehensive and integrative annotation of the human genes in disease network context by integrating computable controlled vocabulary of the Disease Ontology (DO version 3 revision 2510, which has 8043 inherited, developmental and acquired human diseases), NCBI Gene Reference Into Function (GeneRIF) and molecular interaction network (MIN). DGA integrates these resources together using semantic mappings to build an integrative set of disease-to-gene and gene-to-gene relationships with excellent coverage based on current knowledge. DGA is kept current by periodically reparsing DO, GeneRIF, and MINs. DGA provides a user-friendly and interactive web interface system enabling users to efficiently query, download and visualize the DO tree structure and annotations as a tree, a network graph or a tabular list. To facilitate integrative analysis, DGA provides a web service Application Programming Interface for integration with external analytic tools.

dictyBase update 2011: web 2.0 functionality and the initial steps towards a genome portal for the Amoebozoa.

dictyBase (http://www.dictybase.org), the model organism database for Dictyostelium, aims to provide the broad biomedical research community with well integrated, high quality data and tools for Dictyostelium discoideum and related species. dictyBase houses the complete genome sequence, ESTs, and the entire body of literature relevant to Dictyostelium. This information is curated to provide accurate gene models and functional annotations, with the goal of fully annotating the genome to provide a 'reference genome' in the Amoebozoa clade. We highlight several new features in the present update: (i) new annotations; (ii) improved interface with web 2.0 functionality; (iii) the initial steps towards a genome portal for the Amoebozoa; (iv) ortholog display; and (v) the complete integration of the Dicty Stock Center with dictyBase.

Access to COVID-19 testing by individuals with housing insecurity during the early days of the COVID-19 pandemic in the United States: a scoping review.

Introduction: The COVID-19 pandemic focused attention on healthcare disparities and inequities faced by individuals within marginalized and structurally disadvantaged groups in the United States. These individuals bore the heaviest burden across this pandemic as they faced increased risk of infection and difficulty in accessing testing and medical care. Individuals experiencing housing insecurity are a particularly vulnerable population given the additional barriers they face. In this scoping review, we identify some of the barriers this high-risk group experienced during the early days of the pandemic and assess novel solutions to overcome these barriers. Methods: A scoping review was performed following PRISMA-Sc guidelines looking for studies focusing on COVID-19 testing among individuals experiencing housing insecurity. Barriers as well as solutions to barriers were identified as applicable and summarized using qualitative methods, highlighting particular ways that proved effective in facilitating access to testing access and delivery. Results: Ultimately, 42 studies were included in the scoping review, with 143 barriers grouped into four categories: lack of cultural understanding, systemic racism, and stigma; medical care cost, insurance, and logistics; immigration policies, language, and fear of deportation; and other. Out of these 42 studies, 30 of these studies also suggested solutions to address them. Conclusion: A paucity of studies have analyzed COVID-19 testing barriers among those experiencing housing insecurity, and this is even more pronounced in terms of solutions to address those barriers. Expanding resources and supporting investigators within this space is necessary to ensure equitable healthcare delivery.

The Childhood Cancer Data Initiative: Using the Power of Data to Learn From and Improve Outcomes for Every Child and Young Adult With Pediatric Cancer.

Data-driven basic, translational, and clinical research has resulted in improved outcomes for children, adolescents, and young adults (AYAs) with pediatric cancers. However, challenges in sharing data between institutions, particularly in research, prevent addressing substantial unmet needs in children and AYA patients diagnosed with certain pediatric cancers. Systematically collecting and sharing data from every child and AYA can enable greater understanding of pediatric cancers, improve survivorship, and accelerate development of new and more effective therapies. To accomplish this goal, the Childhood Cancer Data Initiative (CCDI) was launched in 2019 at the National Cancer Institute. CCDI is a collaborative community endeavor supported by a 10-year, $50-million (in US dollars) annual federal investment. CCDI aims to learn from every patient diagnosed with a pediatric cancer by designing and building a data ecosystem that facilitates data collection, sharing, and analysis for researchers, clinicians, and patients across the cancer community. For example, CCDI's Molecular Characterization Initiative provides comprehensive clinical molecular characterization for children and AYAs with newly diagnosed cancers. Through these efforts, the CCDI strives to provide clinical benefit to patients and improvements in diagnosis and care through data-focused research support and to build expandable, sustainable data resources and workflows to advance research well past the planned 10 years of the initiative. Importantly, if CCDI demonstrates the success of this model for pediatric cancers, similar approaches can be applied to adults, transforming both clinical research and treatment to improve outcomes for all patients with cancer.

Text data extraction for a prospective, research-focused data mart: implementation and validation.

BACKGROUND: Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review. METHODS: Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined. RESULTS: There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012. CONCLUSIONS: Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public.

Standardizing, harmonizing, and protecting data collection to broaden the impact of COVID-19 research: the rapid acceleration of diagnostics-underserved populations (RADx-UP) initiative.

OBJECTIVE: The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program is a consortium of community-engaged research projects with the goal of increasing access to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests in underserved populations. To accelerate clinical research, common data elements (CDEs) were selected and refined to standardize data collection and enhance cross-consortium analysis. MATERIALS AND METHODS: The RADx-UP consortium began with more than 700 CDEs from the National Institutes of Health (NIH) CDE Repository, Disaster Research Response (DR2) guidelines, and the PHENotypes and eXposures (PhenX) Toolkit. Following a review of initial CDEs, we made selections and further refinements through an iterative process that included live forums, consultations, and surveys completed by the first 69 RADx-UP projects. RESULTS: Following a multistep CDE development process, we decreased the number of CDEs, modified the question types, and changed the CDE wording. Most research projects were willing to collect and share demographic NIH Tier 1 CDEs, with the top exception reason being a lack of CDE applicability to the project. The NIH RADx-UP Tier 1 CDE with the lowest frequency of collection and sharing was sexual orientation. DISCUSSION: We engaged a wide range of projects and solicited bidirectional input to create CDEs. These RADx-UP CDEs could serve as the foundation for a patient-centered informatics architecture allowing the integration of disease-specific databases to support hypothesis-driven clinical research in underserved populations. CONCLUSION: A community-engaged approach using bidirectional feedback can lead to the better development and implementation of CDEs in underserved populations during public health emergencies.

Increasing access and uptake of SARS-CoV-2 at-home tests using a community-engaged approach.

Inequalities around COVID-19 testing and vaccination persist in the U.S. health system. We investigated whether a community-engaged approach could be used to distribute free, at-home, rapid SARS-CoV-2 tests to underserved populations. Between November 18-December 31, 2021, 400,000 tests were successfully distributed via 67 community partners and a mobile unit to a majority Hispanic/Latino/Spanish population in Merced County, California. Testing before gathering (59 %) was the most common testing reason. Asians versus Whites were more likely to test for COVID-19 if they had close contact with someone who may have been positive (odds ratio [OR] = 3.4, 95 % confidence interval [CI] = 1.7-6.7). Minors versus adults were more likely to test if they had close contact with someone who was confirmed positive (OR = 1.7, 95 % CI = 1.0-3.0), whereas Asian (OR = 4.1, 95 % CI = 1.2-13.7) and Hispanic/Latino/Spanish (OR = 2.5, 95 % CI = 1.0-6.6) versus White individuals were more likely to test if they had a positive household member. Asians versus Whites were more likely to receive a positive test result. Minors were less likely than adults to have been vaccinated (OR = 0.2, 95 % CI = 0.1-0.3). Among unvaccinated individuals, those who completed the survey in English versus Spanish indicated they were more likely to get vaccinated in the future (OR = 8.2, 95 % CI = 1.5-44.4). Asians versus Whites were less likely to prefer accessing oral COVID medications from a pharmacy/drug store only compared with a doctor's office or community setting (OR = 0.3, 95 % CI = 0.2-0.6). Study findings reinforce the need for replicable and scalable community-engaged strategies for reducing COVID-19 disparities by increasing SARS-CoV-2 test and vaccine access and uptake.

Mining the Gene Wiki for functional genomic knowledge.

BACKGROUND: Ontology-based gene annotations are important tools for organizing and analyzing genome-scale biological data. Collecting these annotations is a valuable but costly endeavor. The Gene Wiki makes use of Wikipedia as a low-cost, mass-collaborative platform for assembling text-based gene annotations. The Gene Wiki is comprised of more than 10,000 review articles, each describing one human gene. The goal of this study is to define and assess a computational strategy for translating the text of Gene Wiki articles into ontology-based gene annotations. We specifically explore the generation of structured annotations using the Gene Ontology and the Human Disease Ontology. RESULTS: Our system produced 2,983 candidate gene annotations using the Disease Ontology and 11,022 candidate annotations using the Gene Ontology from the text of the Gene Wiki. Based on manual evaluations and comparisons to reference annotation sets, we estimate a precision of 90-93% for the Disease Ontology annotations and 48-64% for the Gene Ontology annotations. We further demonstrate that this data set can systematically improve the results from gene set enrichment analyses. CONCLUSIONS: The Gene Wiki is a rapidly growing corpus of text focused on human gene function. Here, we demonstrate that the Gene Wiki can be a powerful resource for generating ontology-based gene annotations. These annotations can be used immediately to improve workflows for building curated gene annotation databases and knowledge-based statistical analyses.

dictyBase--a Dictyostelium bioinformatics resource update.

dictyBase (http://dictybase.org) is the model organism database for Dictyostelium discoideum. It houses the complete genome sequence, ESTs and the entire body of literature relevant to Dictyostelium. This information is curated to provide accurate gene models and functional annotations, with the goal of fully annotating the genome. This dictyBase update describes the annotations and features implemented since 2006, including improved strain and phenotype representation, integration of predicted transcriptional regulatory elements, protein domain information, biochemical pathways, improved searching and a wiki tool that allows members of the research community to provide annotations.