Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics

Disease Ontology: a backbone for disease semantic integration

The Disease Ontology (DO) database (http://disease-ontology.org) represents a comprehensive knowledge base of 8043 inherited, developmental and acquired human diseases (DO version 3, revision 2510). The DO web browser has been designed for speed, efficiency and robustness through the use of a graph database. Full-text contextual searching functionality using Lucene allows the querying of name, synonym, definition, DOID and cross-reference (xrefs) with complex Boolean search strings. The DO semantically integrates disease and medical vocabularies through extensive cross mapping and integration of MeSH, ICD, NCI's thesaurus, SNOMED CT and OMIM disease-specific terms and identifiers. The DO is utilized for disease annotation by major biomedical databases (e.g. Array Express, NIF, IEDB), as a standard representation of human disease in biomedical ontologies (e.g. IDO, Cell line ontology, NIFSTD ontology, Experimental Factor Ontology, Influenza Ontology), and as an ontological cross mappings resource between DO, MeSH and OMIM (e.g. GeneWiki). The DO project (http://diseaseontology.sf.net) has been incorporated into open source tools (e.g. Gene Answers, FunDO) to connect gene and disease biomedical data through the lens of human disease. The next iteration of the DO web browser will integrate DO's extended relations and logical definition representation along with these biomedical resource cross-mappings

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

From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations

Subjective methods have been reported to adapt a general-purpose ontology for a specific application. For example, Gene Ontology (GO) Slim was created from GO to generate a highly aggregated report of the human-genome annotation. We propose statistical methods to adapt the general purpose, OBO Foundry Disease Ontology (DO) for the identification of gene-disease associations. Thus, we need a simplified definition of disease categories derived from implicated genes. On the basis of the assumption that the DO terms having similar associated genes are closely related, we group the DO terms based on the similarity of gene-to-DO mapping profiles. Two types of binary distance metrics are defined to measure the overall and subset similarity between DO terms. A compactness-scalable fuzzy clustering method is then applied to group similar DO terms. To reduce false clustering, the semantic similarities between DO terms are also used to constrain clustering results. As such, the DO terms are aggregated and the redundant DO terms are largely removed. Using these methods, we constructed a simplified vocabulary list from the DO called Disease Ontology Lite (DOLite). We demonstrated that DOLite results in more interpretable results than DO for gene-disease association tests. The resultant DOLite has been used in the Functional Disease Ontology (FunDO) Web application at http://www.projects.bioinformatics.northwestern.edu/fundo

Exploring barriers and facilitators of implementing an at-home SARS-CoV-2 antigen self-testing intervention: The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) initiatives

BACKGROUND: Evaluating community-based programs provides value to researchers, funding entities, and community stakeholders involved in program implementation, and can increase program impact and sustainability. To understand factors related to program implementation, we aimed to capture the perspective of community partners engaged in organizing and executing community-engaged programs to distribute COVID-19 at-home tests in underserved communities. METHODS: We conducted semi-structured interviews and focus groups with community-based stakeholders informed by the Outcomes for Implementation Research framework. RESULTS: Findings describe how community-engaged communication and dissemination strategies drove program adoption among grassroots stakeholders. Establishing and sustaining trusted relationships was vital to engaging partners with aligned values and capacity. Respondents characterized the programs as generally feasible and appropriate, and community partners felt capable of delivering the program successfully. However, they also described an increased burden on their workforce and desired more significant support. Respondents recognized the programs' community engagement practices as a critical facilitator of acceptability and impact. DISCUSSION: Implementation evaluation aims to inform current and future community outreach and engagement efforts with best practices. As we continue to inform and advance community-engaged disaster response practice, a parallel reimagining of public health funding mechanisms and timelines could provide a foundation for trust, collaboration, and community resiliency that endures beyond a given crisis

Electronic Health Record Functionality Needed to Better Support Primary Care

Electronic health records (EHRs) must support primary care clinicians and patients, yet many clinicians remain dissatisfied with their system. This manuscript presents a consensus statement about gaps in current EHR functionality and needed enhancements to support primary care. The Institute of Medicine primary care attributes were used to define needs and Meaningful Use (MU) objectives to define EHR functionality. Current objectives remain disease- rather than whole-person focused, ignoring factors like personal risks, behaviors, family structure, and occupational and environmental influences. Primary care needs EHRs to move beyond documentation to interpreting and tracking information over time as well as patient partnering activities, support for team based care, population management tools that deliver care, and reduced documentation burden. While Stage 3 MU’s focus on outcomes is laudable, enhanced functionality is still needed including EHR modifications, expanded use of patient portals, seamless integration with external applications, and advancement of national infrastructure and policies

Adapting the Evidence Academy model for virtual stakeholder engagement in a national setting during the COVID-19 pandemic

The COVID-19 pandemic raised the importance of adaptive capacity and preparedness when engaging historically marginalized populations in research and practice. The Rapid Acceleration of Diagnostics in Underserved Populations' COVID-19 Equity Evidence Academy Series (RADx-UP EA) is a virtual, national, interactive conference model designed to support and engage community-academic partnerships in a collaborative effort to improve practices that overcome disparities in SARS-CoV-2 testing and testing technologies. The RADx-UP EA promotes information sharing, critical reflection and discussion, and creation of translatable strategies for health equity. Staff and faculty from the RADx-UP Coordination and Data Collection Center developed three EA events with diverse geographic, racial, and ethnic representation of attendees from RADx-UP community-academic project teams: February 2021 (n = 319); November 2021 (n = 242); and September 2022 (n = 254). Each EA event included a data profile; 2-day, virtual event; event summary report; community dissemination product; and an evaluation strategy. Operational and translational delivery processes were iteratively adapted for each EA across one or more of five adaptive capacity domains: assets, knowledge and learning, social organization, flexibility, and innovation. The RADx-UP EA model can be generalized beyond RADx-UP and tailored by community and academic input to respond to local or national health emergencies

Crystal structures of the NO sensor NsrR reveal how its iron-sulfur cluster modulates DNA binding

NsrR from Streptomyces coelicolor (Sc) regulates the expression of three genes through the progressive degradation of its [4Fe–4S] cluster on nitric oxide (NO) exposure. We report the 1.95 Å resolution crystal structure of dimeric holo-ScNsrR and show that the cluster is coordinated by the three invariant Cys residues from one monomer and, unexpectedly, Asp8 from the other. A cavity map suggests that NO displaces Asp8 as a cluster ligand and, while D8A and D8C variants remain NO sensitive, DNA binding is affected. A structural comparison of holo-ScNsrR with an apo-IscR-DNA complex shows that the [4Fe–4S] cluster stabilizes a turn between ScNsrR Cys93 and Cys99 properly oriented to interact with the DNA backbone. In addition, an apo ScNsrR structure suggests that Asn97 from this turn, along with Arg12, which forms a salt-bridge with Asp8, are instrumental in modulating the position of the DNA recognition helix region relative to its major groove

Mining the Gene Wiki for functional genomic knowledge

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Using WormBase: A Genome Biology Resource for Caenorhabditis elegans and Related Nematodes

WormBase (www.wormbase.org) provides the nematode research community with a centralized database for information pertaining to nematode genes and genomes. As more nematode genome sequences are becoming available and as richer data sets are published, WormBase strives to maintain updated information, displays, and services to facilitate efficient access to and understanding of the knowledge generated by the published nematode genetics literature. This chapter aims to provide an explanation of how to use basic features of WormBase, new features, and some commonly used tools and data queries. Explanations of the curated data and step-by-step instructions of how to access the data via the WormBase website and available data mining tools are provided