Metformin prescription for U.S. veterans with prediabetes, 2010-2019.

Affecting an estimated 88 million Americans, prediabetes increases the risk for developing type 2 diabetes mellitus (T2DM), and independently, cardiovascular disease, retinopathy, nephropathy, and neuropathy. Nevertheless, little is known about the use of metformin for diabetes prevention among patients in the Veterans Health Administration, the largest integrated healthcare system in the U.S. This is a retrospective observational cohort study of the proportion of Veterans with incident prediabetes who were prescribed metformin at the Veterans Health Administration from October 2010 to September 2019. Among 1,059,605 Veterans with incident prediabetes, 12,009 (1.1%) were prescribed metformin during an average 3.4 years of observation after diagnosis. Metformin prescribing was marginally higher (1.6%) among those with body mass index (BMI) ≥35 kg/m2, age <60 years, HbA1c≥6.0%, or those with a history of gestational diabetes, all subgroups at a higher risk for progression to T2DM. In a multivariable model, metformin was more likely to be prescribed for those with BMI ≥35 kg/m2 incidence rate ratio [IRR] 2.6 [95% confidence intervals (CI): 2.1-3.3], female sex IRR, 2.4 [95% CI: 1.8-3.3], HbA1c≥6% IRR, 1.93 [95% CI: 1.5-2.4], age <60 years IRR, 1.7 [95% CI: 1.3-2.3], hypertriglyceridemia IRR, 1.5 [95% CI: 1.2-1.9], hypertension IRR, 1.5 [95% CI: 1.1-2.1], Major Depressive Disorder IRR, 1.5 [95% CI: 1.1-2.0], or schizophrenia IRR, 2.1 [95% CI: 1.2-3.8]. Over 20% of Veterans with prediabetes attended a comprehensive structured lifestyle modification clinic or program. Among Veterans with prediabetes, metformin was prescribed to 1.1% overall, a proportion that marginally increased to 1.6% in the subset of individuals at highest risk for progression to T2DM.

Implementer report: ICD-10 code F44.5 review for functional seizure disorder.

OBJECTIVE: The study aimed to measure the validity of International Classification of Diseases, 10th Edition (ICD-10) code F44.5 for functional seizure disorder (FSD) in the Veterans Affairs Connecticut Healthcare System electronic health record (VA EHR). METHODS: The study used an informatics search tool, a natural language processing algorithm and a chart review to validate FSD coding. RESULTS: The positive predictive value (PPV) for code F44.5 was calculated to be 44%. DISCUSSION: ICD-10 introduced a specific code for FSD to improve coding validity. However, results revealed a meager (44%) PPV for code F44.5. Evaluation of the low diagnostic precision of FSD identified inconsistencies in the ICD-10 and VA EHR systems. CONCLUSION: Information system improvements may increase the precision of diagnostic coding by clinicians. Specifically, the EHR problem list should include commonly used diagnostic codes and an appropriately curated ICD-10 term list for 'seizure disorder,' and a single ICD code for FSD should be classified under neurology and psychiatry.

From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights.

Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.

Chemical Entity Normalization for Successful Translational Development of Alzheimer's Disease and Dementia Therapeutics.

Background: Identifying chemical mentions within the Alzheimer's and dementia literature can provide a powerful tool to further therapeutic research. Leveraging the Chemical Entities of Biological Interest (ChEBI) ontology, which is rich in hierarchical and other relationship types, for entity normalization can provide an advantage for future downstream applications. We provide a reproducible hybrid approach that combines an ontology-enhanced PubMedBERT model for disambiguation with a dictionary-based method for candidate selection. Results: There were 56,553 chemical mentions in the titles of 44,812 unique PubMed article abstracts. Based on our gold standard, our method of disambiguation improved entity normalization by 25.3 percentage points compared to using only the dictionary-based approach with fuzzy-string matching for disambiguation. For our Alzheimer's and dementia cohort, we were able to add 47.1% more potential mappings between MeSH and ChEBI when compared to BioPortal. Conclusion: Use of natural language models like PubMedBERT and resources such as ChEBI and PubChem provide a beneficial way to link entity mentions to ontology terms, while further supporting downstream tasks like filtering ChEBI mentions based on roles and assertions to find beneficial therapies for Alzheimer's and dementia.

Characteristics of Chiropractic Patients in the Veterans Health Administration During the COVID-19 Pandemic: A Cross-Sectional Analysis.

OBJECTIVE: The purpose of this study was to determine whether patient characteristics were associated with face-to-face (F2F) and telehealth visits for those receiving chiropractic care for musculoskeletal conditions in the US Veterans Health Administration (VHA) during the COVID-19 pandemic. METHODS: A retrospective cross-sectional analysis of all patients (veterans, dependents, and spouses) who received chiropractic care nationwide at the VHA from March 1, 2020, to February 28, 2021, was performed. Patients were allocated into 1 of the following 3 groups: only telehealth visits, only F2F visits, and combined F2F and telehealth visits. Patient characteristics included age, sex, race, ethnicity, marital status, and Charlson Comorbidity Index. Multinomial logistic regression estimated associations of these variables with visit type. RESULTS: The total number of unique patients seen by chiropractors between March 2020 and February 2021 was 62 658. Key findings were that patients of non-White race and Hispanic or Latino ethnicity were more likely to attend telehealth-only visits (Black [odds ratio 1.20, 95% confidence interval {1.10-1.31}], other races [1.36 {1.16-1.59}], and Hispanic or Latino [1.35 {1.20-1.52}]) and combination telehealth and F2F care (Black [1.32 {1.25-1.40}], other races [1.37 {1.23-1.52}], and Hispanic or Latino [1.63 {1.51-1.76}]). Patients younger than 40 years of age were more likely to choose telehealth visits ([1.13 {1.02-1.26}], 66-75 years [1.17 {1.01-1.35}], and >75 years [1.26 {1.06-1.51}] vs those 40-55 years of age). Sex, visit frequency, and Charlson Comorbidity Index showed significant relationships as well, while marital status did not. CONCLUSION: During the COVID-19 pandemic, VHA patients with musculoskeletal complaints using chiropractic telehealth were more ethnically and racially diverse than those using F2F care alone.

Suicide and Seizures: A National Cohort Study in Veterans.

OBJECTIVE: The increased rate of suicide associated with epilepsy has been described, but no studies have reported the rates of suicide and suicide-related behavior (SRB) associated with psychogenic nonepileptic seizures (PNESs). METHODS: This retrospective cohort study analyzed data from October 2002 to October 2017 within Veterans Health Administration services. Of 801,734 veterans, 0.09% had PNES, 1.37% had epilepsy, and 98.5% had no documented seizures. Veterans coded for completed suicide, suicide attempts, and suicidal ideation were identified from electronic health records. The primary measure was the suicide-specific standardized mortality ratio (SMR) based on the number of suicide deaths and CDC national suicide mortality database. A Poisson regression was used to calculate the relative risk (RR) of suicide across groups. RESULTS: A total of 1,870 veterans (mean age [SD] 33.76 [7.81] years) completed suicide. Veterans with PNES (RR = 1.75, 95% confidence interval [CI] 0.84-4.24) and veterans with epilepsy (RR = 2.19, 95% CI 2.10-2.28) had a higher risk of suicide compared with the general veteran population. Veterans with PNES or epilepsy had a higher risk of suicide and SRB if they had comorbid alcohol abuse, illicit drug abuse, major depression, posttraumatic stress disorder, and use of psychotropic medications. Conversely, those who were married or attained higher education were at a decreased risk. The SMR for completed suicide for PNES, epilepsy, and the comparison group was 2.65 (95% CI 1.95-5.52), 2.04 (95% CI 1.60-2.55), and 0.70 (95% CI 0.67-0.74), respectively. CONCLUSIONS: Veterans with seizures (both psychogenic and epileptic) are at a greater risk of death by suicide and SRB than the comparison group. These findings suggest that although the pathophysiology of PNES and epilepsy is different, the negative impact of seizures is evident in the psychosocial outcomes in both groups.

LinkedImm: a linked data graph database for integrating immunological data.

BACKGROUND: Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration. RESULTS: We have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language. CONCLUSION: We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.

Genome-Wide Association Studies of Schizophrenia and Bipolar Disorder in a Diverse Cohort of US Veterans.

BACKGROUND: Schizophrenia (SCZ) and bipolar disorder (BIP) are debilitating neuropsychiatric disorders, collectively affecting 2% of the world's population. Recognizing the major impact of these psychiatric disorders on the psychosocial function of more than 200 000 US Veterans, the Department of Veterans Affairs (VA) recently completed genotyping of more than 8000 veterans with SCZ and BIP in the Cooperative Studies Program (CSP) #572. METHODS: We performed genome-wide association studies (GWAS) in CSP #572 and benchmarked the predictive value of polygenic risk scores (PRS) constructed from published findings. We combined our results with available summary statistics from several recent GWAS, realizing the largest and most diverse studies of these disorders to date. RESULTS: Our primary GWAS uncovered new associations between CHD7 variants and SCZ, and novel BIP associations with variants in Sortilin Related VPS10 Domain Containing Receptor 3 (SORCS3) and downstream of PCDH11X. Combining our results with published summary statistics for SCZ yielded 39 novel susceptibility loci including CRHR1, and we identified 10 additional findings for BIP (28 326 cases and 90 570 controls). PRS trained on published GWAS were significantly associated with case-control status among European American (P < 10-30) and African American (P < .0005) participants in CSP #572. CONCLUSIONS: We have demonstrated that published findings for SCZ and BIP are robustly generalizable to a diverse cohort of US veterans. Leveraging available summary statistics from GWAS of global populations, we report 52 new susceptibility loci and improved fine-mapping resolution for dozens of previously reported associations.

A structured model for immune exposures.

An Immune Exposure is the process by which components of the immune system first encounter a potential trigger. The ability to describe consistently the details of the Immune Exposure process was needed for data resources responsible for housing scientific data related to the immune response. This need was met through the development of a structured model for Immune Exposures. This model was created during curation of the immunology literature, resulting in a robust model capable of meeting the requirements of such data. We present this model with the hope that overlapping projects will adopt and or contribute to this work.

Extracting lung function measurements to enhance phenotyping of chronic obstructive pulmonary disease (COPD) in an electronic health record using automated tools.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is associated with poor quality of life, hospitalization and mortality. COPD phenotype includes using pulmonary function tests to determine airflow obstruction from the forced expiratory volume in one second (FEV1):forced vital capacity. FEV1 is a commonly used value for severity but is difficult to identify in structured electronic health record (EHR) data. DATA SOURCE AND METHODS: Using the Microsoft SQL Server's full-text search feature and string functions supporting regular-expression-like operations, we developed an automated tool to extract FEV1 values from progress notes to improve ascertainment of FEV1 in EHR in the Veterans Aging Cohort Study (VACS). RESULTS: The automated tool increased quantifiable FEV1 values from 12,425 to 16,274 (24% increase in numeric FEV1). Using chart review as the reference, positive predictive value of the tool was 99% (95% Confidence interval: 98.2-100.0%) for identifying quantifiable FEV1 values and a recall value of 100%, yielding an F-measure of 0.99. The tool correctly identified FEV1 measurements in 95% of cases. CONCLUSION: A SQL-based full text search of clinical notes for quantifiable FEV1 is efficient and improves the number of values available in VA data. Future work will examine how these methods can improve phenotyping of patients with COPD in the VA.

Genome-wide association study of cognitive performance in U.S. veterans with schizophrenia or bipolar disorder.

Cognitive impairment is a frequent and serious problem in patients with various forms of severe mental illnesses (SMI), including schizophrenia (SZ) and bipolar disorder (BP). Recent research suggests genetic links to several cognitive phenotypes in both SMI and in the general population. Our goal in this study was to identify potential genomic signatures of cognitive functioning in veterans with severe mental illness and compare them to previous findings for cognition across different populations. Veterans Affairs (VA) Cooperative Studies Program (CSP) Study #572 evaluated cognitive and functional capacity measures among SZ and BP patients. In conjunction with the VA Million Veteran Program, 3,959 European American (1,095 SZ, 2,864 BP) and 2,601 African American (1,095 SZ, 2,864 BP) patients were genotyped using a custom Affymetrix Axiom Biobank array. We performed a genome-wide association study of global cognitive functioning, constructed polygenic scores for SZ and cognition in the general population, and examined genetic correlations with 2,626 UK Biobank traits. Although no single locus attained genome-wide significance, observed allelic effects were strongly consistent with previous studies. We observed robust associations between global cognitive functioning and polygenic scores for cognitive performance, intelligence, and SZ risk. We also identified significant genetic correlations with several cognition-related traits in UK Biobank. In a diverse cohort of U.S. veterans with SZ or BP, we demonstrate broad overlap of common genetic effects on cognition in the general population, and find that greater polygenic loading for SZ risk is associated with poorer cognitive performance.

Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans.

Post-traumatic stress disorder (PTSD) is a major problem among military veterans and civilians alike, yet its pathophysiology remains poorly understood. We performed a genome-wide association study and bioinformatic analyses, which included 146,660 European Americans and 19,983 African Americans in the US Million Veteran Program, to identify genetic risk factors relevant to intrusive reexperiencing of trauma, which is the most characteristic symptom cluster of PTSD. In European Americans, eight distinct significant regions were identified. Three regions had values of P < 5 × 10-10: CAMKV; chromosome 17 closest to KANSL1, but within a large high linkage disequilibrium region that also includes CRHR1; and TCF4. Associations were enriched with respect to the transcriptomic profiles of striatal medium spiny neurons. No significant associations were observed in the African American cohort of the sample. Results in European Americans were replicated in the UK Biobank data. These results provide new insights into the biology of PTSD in a well-powered genome-wide association study.

Reporting and connecting cell type names and gating definitions through ontologies.

BACKGROUND: Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a pattern of markers. The description of the cell populations targeted in such experiments typically have two complementary components: the description of the cell type targeted (e.g. 'T cells'), and the description of the marker pattern utilized (e.g. CD14-, CD3+). RESULTS: We here describe our attempts to use ontologies to cross-compare cell types and marker patterns (also referred to as gating definitions). We used a large set of such gating definitions and corresponding cell types submitted by different investigators into ImmPort, a central database for immunology studies, to examine the ability to parse gating definitions using terms from the Protein Ontology (PRO) and cell type descriptions, using the Cell Ontology (CL). We then used logical axioms from CL to detect discrepancies between the two. CONCLUSIONS: We suggest adoption of our proposed format for describing gating and cell type definitions to make comparisons easier. We also suggest a number of new terms to describe gating definitions in flow cytometry that are not based on molecular markers captured in PRO, but on forward- and side-scatter of light during data acquisition, which is more appropriate to capture in the Ontology for Biomedical Investigations (OBI). Finally, our approach results in suggestions on what logical axioms and new cell types could be considered for addition to the Cell Ontology.

Genome-wide Association Study of Maximum Habitual Alcohol Intake in >140,000 U.S. European and African American Veterans Yields Novel Risk Loci.

BACKGROUND: Habitual alcohol use can be an indicator of alcohol dependence, which is associated with a wide range of serious health problems. METHODS: We completed a genome-wide association study in 126,936 European American and 17,029 African American subjects in the Veterans Affairs Million Veteran Program for a quantitative phenotype based on maximum habitual alcohol consumption. RESULTS: ADH1B, on chromosome 4, was the lead locus for both populations: for the European American sample, rs1229984 (p = 4.9 × 10-47); for African American, rs2066702 (p = 2.3 × 10-12). In the European American sample, we identified three additional genome-wide-significant maximum habitual alcohol consumption loci: on chromosome 17, rs77804065 (p = 1.5 × 10-12), at CRHR1 (corticotropin-releasing hormone receptor 1); the protein product of this gene is involved in stress and immune responses; and on chromosomes 8 and 10. European American and African American samples were then meta-analyzed; the associated region at CRHR1 increased in significance to 1.02 × 10-13, and we identified two additional genome-wide significant loci, FGF14 (p = 9.86 × 10-9) (chromosome 13) and a locus on chromosome 11. Besides ADH1B, none of the five loci have prior genome-wide significant support. Post-genome-wide association study analysis identified genetic correlation to other alcohol-related traits, smoking-related traits, and many others. Replications were observed in UK Biobank data. Genetic correlation between maximum habitual alcohol consumption and alcohol dependence was 0.87 (p = 4.78 × 10-9). Enrichment for cell types included dopaminergic and gamma-aminobutyric acidergic neurons in midbrain, and pancreatic delta cells. CONCLUSIONS: The present study supports five novel alcohol-use risk loci, with particularly strong statistical support for CRHR1. Additionally, we provide novel insight regarding the biology of harmful alcohol use.

Measuring coordination of epilepsy care: A mixed methods evaluation of social network analysis versus relational coordination.

OBJECTIVES: Coordination of multidisciplinary care is critical to address the complex needs of people with neurological disorders; however, quality improvement and research tools to measure coordination of neurological care are not well-developed. This study explored and compared the value of social network analysis (SNA) and relational coordination (RC) in measuring coordination of care in a neurology setting. The Department of Veterans Affairs Healthcare System (VA) established an Epilepsy Centers of Excellence (ECOE) hub and spoke model of care, which provides a setting to measure coordination of care across networks of providers. METHODS: In a parallel mixed methods approach, we compared coordination of care of VA providers who formally engage the ECOE system to VA providers outside the ECOE system using SNA and RC. Coordination of care scores were compiled from provider teams across 66 VA facilities, and key informant interviews of 80 epilepsy care team members were conducted concurrently to describe the quality of epilepsy care coordinating in the VA healthcare system. RESULTS: On average, members of healthcare teams affiliated with the ECOE program rated quality of communication and respect higher than non-ECOE physicians. Connectivity between neurologist and primary care providers as well as between neurologists and mental health providers were higher within ECOE hub facilities compared to spoke referring facilities. Key informant interviews reported the important role of formal and informal programming, social support and social capital, and social influence on epilepsy care networks. CONCLUSION: For quality improvement and research purposes, SNA and RC can be used to measure coordination of neurological care; RC provides a detailed assessment of the quality of communication within and across healthcare teams but is difficult to administer and analyze; SNA provides large scale coordination of care maps and metrics to compare across large healthcare systems. The two measures provide complimentary coordination of care data at a local as well as population level. Interviews describe the mechanisms of developing and sustaining health professional networks that are not captured in either SNA or RC measures.

A linked data graph approach to integration of immunological data.

Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. While many existing biological databases are implemented using the traditional SQL (Structured Query Language) database technology, NoSQL database technologies have been explored as a more relationship-based, flexible and scalable method of data integration. In this paper, we describe how to use the Neo4J graph database to integrate a variety of types of data sets in the context of systems vaccinology. Specifically, we have converted into a common graph model diverse types of vaccine response measurement data from the NIH/NIAID ImmPort data repository, pathway data from Reactome, influenza virus strains from WHO, and taxonomic data from NCBI Taxon. While Neo4J provides a graph-based query language (Cypher) for data retrieval, we develop a web-based dashboard for users to easily browse and visualize data without the need to learn Cypher. In addition, we have prototyped a natural language query interface for users to interact with our system. In conclusion, we demonstrate the feasibility of using a graph-based database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to reveal novel relationships among heterogeneous biological data.

Managing Functional Neurological Disorders: Protocol of a Cohort Study on Psychogenic Non-Epileptic Seizures Study.

BACKGROUND: Functional neurological disorders (FNDs) are neurological symptoms that cannot be explained by an underlying neurological lesion or other medical illness and that do not have clear neuropathological correlates. Psychogenic non-epileptic seizures (PNES) are a common and highly disabling form of FND, characterized by paroxysmal episodes of involuntary movements and altered consciousness that can appear clinically similar to epileptic seizures. PNES are unique among FNDs in that they are diagnosed by video electroencephalographic (VEEG), a well-established biomarker for the disorder. The course of illness and response to treatment of PNES remain controversial. This study aims to describe the epidemiology of PNES in the Department of Veterans Affairs Healthcare System (VA), evaluate outcomes of veterans offered different treatments, and compare models of care for PNES. METHODS: This electronic health record (EHR) cohort study utilizes an informatics search tool and a natural language processing algorithm to identify cases of PNES nationally. We will use VA inpatient, outpatient, pharmacy, and chart abstraction data across all 170 medical centers to identify cases in fiscal years 2002-2018. Outcome measurements such as seizure frequency, emergency room visits, hospital admissions, suicide-related behavior, and the utilization of psychotherapy prior to and after PNES diagnosis will be used to assess the effectiveness of models of care. DISCUSSION: This study will describe the risk factors and course of treatment of a large cohort of people with PNES. Since PNES are cared for by a variety of different modalities, treatment orientations, and models of care, effectiveness outcomes such as seizure outcomes and utilization of emergency visits for seizures will be assessed. Outcome measurements such as seizure frequency, emergency room visits, hospital admissions, suicide-related behavior, and psychotherapy prior to and after PNES diagnosis will be used to assess the effectiveness of models of care.

The Share Project: Building Capacity of Justice-Involved Individuals, Policymakers, and Researchers to Collectively Transform Health Care Delivery.

The Share Project (TSP), a US health justice initiative, convened key stakeholders to advance the use of inclusive research methods and data sharing to engage groups that are typically marginalized from research. TSP trained justice-involved patients, community health workers, policymakers, and researchers in participatory research and the use of a data-sharing platform developed with justice-involved patients. The platform allowed users to analyze health and criminal justice data to develop new research that is patient driven and responsive to the needs of providers.

The CAIRR Pipeline for Submitting Standards-Compliant B and T Cell Receptor Repertoire Sequencing Studies to the National Center for Biotechnology Information Repositories.

The adaptation of high-throughput sequencing to the B cell receptor and T cell receptor has made it possible to characterize the adaptive immune receptor repertoire (AIRR) at unprecedented depth. These AIRR sequencing (AIRR-seq) studies offer tremendous potential to increase the understanding of adaptive immune responses in vaccinology, infectious disease, autoimmunity, and cancer. The increasingly wide application of AIRR-seq is leading to a critical mass of studies being deposited in the public domain, offering the possibility of novel scientific insights through secondary analyses and meta-analyses. However, effective sharing of these large-scale data remains a challenge. The AIRR community has proposed minimal information about adaptive immune receptor repertoire (MiAIRR), a standard for reporting AIRR-seq studies. The MiAIRR standard has been operationalized using the National Center for Biotechnology Information (NCBI) repositories. Submissions of AIRR-seq data to the NCBI repositories typically use a combination of web-based and flat-file templates and include only a minimal amount of terminology validation. As a result, AIRR-seq studies at the NCBI are often described using inconsistent terminologies, limiting scientists' ability to access, find, interoperate, and reuse the data sets. In order to improve metadata quality and ease submission of AIRR-seq studies to the NCBI, we have leveraged the software framework developed by the Center for Expanded Data Annotation and Retrieval (CEDAR), which develops technologies involving the use of data standards and ontologies to improve metadata quality. The resulting CEDAR-AIRR (CAIRR) pipeline enables data submitters to: (i) create web-based templates whose entries are controlled by ontology terms, (ii) generate and validate metadata, and (iii) submit the ontology-linked metadata and sequence files (FASTQ) to the NCBI BioProject, BioSample, and Sequence Read Archive databases. Overall, CAIRR provides a web-based metadata submission interface that supports compliance with the MiAIRR standard. This pipeline is available at http://cairr.miairr.org, and will facilitate the NCBI submission process and improve the metadata quality of AIRR-seq studies.

CEDAR OnDemand: a browser extension to generate ontology-based scientific metadata.

BACKGROUND: Public biomedical data repositories often provide web-based interfaces to collect experimental metadata. However, these interfaces typically reflect the ad hoc metadata specification practices of the associated repositories, leading to a lack of standardization in the collected metadata. This lack of standardization limits the ability of the source datasets to be broadly discovered, reused, and integrated with other datasets. To increase reuse, discoverability, and reproducibility of the described experiments, datasets should be appropriately annotated by using agreed-upon terms, ideally from ontologies or other controlled term sources. RESULTS: This work presents "CEDAR OnDemand", a browser extension powered by the NCBO (National Center for Biomedical Ontology) BioPortal that enables users to seamlessly enter ontology-based metadata through existing web forms native to individual repositories. CEDAR OnDemand analyzes the web page contents to identify the text input fields and associate them with relevant ontologies which are recommended automatically based upon input fields' labels (using the NCBO ontology recommender) and a pre-defined list of ontologies. These field-specific ontologies are used for controlling metadata entry. CEDAR OnDemand works for any web form designed in the HTML format. We demonstrate how CEDAR OnDemand works through the NCBI (National Center for Biotechnology Information) BioSample web-based metadata entry. CONCLUSION: CEDAR OnDemand helps lower the barrier of incorporating ontologies into standardized metadata entry for public data repositories. CEDAR OnDemand is available freely on the Google Chrome store https://chrome.google.com/webstore/search/CEDAROnDemand.