Genomic Repositories Research Articles

Comparing Attitudes About Genomic Privacy and Data Sharing in Adolescents and Parents of Children Enrolled in a Genomic Research Repository

Background Sharing of genomic data aims to make efficient use of limited resources, which may be particularly valuable in rare disease research. Adult research participants and parents of pediatric research participants have shown support for data sharing with protections, but little is known about adolescent attitudes on genomic privacy and data sharing. Methods In-depth interviews were conducted with 10 adolescents and 18 parents of children enrolled in a pediatric genomic research repository. Interview transcripts were analyzed for themes on attitudes toward genomic privacy, restricted-access data sharing, and open-access data sharing. Findings in adolescent and parent participants were compared and contrasted. Results No adolescents endorsed privacy concerns for restricted-access data sharing. Both adolescents and parents saw value in data sharing for reaching the goals of research and discussed trust in institutions and researchers to protect their data and use it as intended. Adolescents were more likely than parents to accept open-access data sharing, including after risks were discussed. Conclusions In this exploratory study, adolescents and parents enrolled in a genomic research repository shared many attitudes about genomic data sharing, but adolescents were less concerned about privacy and more agreeable toward open-access data sharing. Future research is needed to investigate this hypothesis in expanded populations and settings, and to clarify whether adolescent attitudes change with age and experiences.

Achieving Procedural Parity in Managing Access to Genomic and Related Health Data: A Global Survey of Data Access Committee Members.

Data access committees (DACs) are critical players in the data sharing ecosystem. DACs review requests for access to data held in one or more repositories and where specific constraints determine how the data may be used and by whom. Our team surveyed DAC members affiliated with genomic data repositories worldwide to understand standard processes and procedures, operational metrics, bottlenecks, and efficiencies, as well as their perspectives on possible improvements to quality review. We found that DAC operations and systemic issues were common across repositories globally. In general, DAC members endeavored to achieve an appropriate balance of review efficiency, quality, and compliance. Our results suggest a similarly proportionate path forward that helps DACs pursue mutual improvements to efficiency and compliance without sacrificing review quality.

Replicated life-history patterns and subsurface origins of the bacterial sister phyla Nitrospirota and Nitrospinota

The phyla Nitrospirota and Nitrospinota have received significant research attention due to their unique nitrogen metabolisms important to biogeochemical and industrial processes. These phyla are common inhabitants of marine and terrestrial subsurface environments and contain members capable of diverse physiologies in addition to nitrite oxidation and complete ammonia oxidation. Here, we use phylogenomics and gene-based analysis with ancestral state reconstruction and gene-tree–species-tree reconciliation methods to investigate the life histories of these two phyla. We find that basal clades of both phyla primarily inhabit marine and terrestrial subsurface environments. The genomes of basal clades in both phyla appear smaller and more densely coded than the later-branching clades. The extant basal clades of both phyla share many traits inferred to be present in their respective common ancestors, including hydrogen, one-carbon, and sulfur-based metabolisms. Later-branching groups, namely the more frequently studied classes Nitrospiria and Nitrospinia, are both characterized by genome expansions driven by either de novo origination or laterally transferred genes that encode functions expanding their metabolic repertoire. These expansions include gene clusters that perform the unique nitrogen metabolisms that both phyla are most well known for. Our analyses support replicated evolutionary histories of these two bacterial phyla, with modern subsurface environments representing a genomic repository for the coding potential of ancestral metabolic traits.

Rare Neuropsychiatric Variants Present in 1% of Individuals

Rare Neuropsychiatric Variants Present in 1% of Individuals

Show me the data! Data sharing practices demonstrated in published research at the University of Massachusetts Chan Medical School

Objective: In the interest of making data findable, accessible, interoperable, and reusable (FAIR), the National Institutes of Health (NIH) will institute a new Data Management and Sharing Policy in January 2023. This policy will require researchers applying for NIH funding to submit a Data Management and Sharing Plan. As 63% of grant dollars received by University of Massachusetts Chan Medical School (UMass Chan) researchers comes from the NIH, we explored whether UMass Chan researchers are currently sharing data associated with their published research and how they shared their data. Methods: PubMed was searched for articles published in 2019 with a UMass Chan researcher as either the first or last author. These articles were examined for evidence of original or reused data, the type of data, whether the article stated that data was available, and where and how to find that data. Results: Of the 361 articles with original data, 26% had a data availability statement. However, most articles (71%) did not mention where data could be accessed. The data storage location of the estimated 1551 original datasets was similarly not mentioned for 74% the datasets with the next largest category being available upon request (8.6%). Genomic data repositories such as the Gene Expression Omnibus were among the top repositories used by authors. Similar areas for improvement were noted for permanent identifier use (46% had a permanent identifier), using non-proprietary file formats (most popular format was Excel), and citing reused data. Authors who published open access were more likely to share their data. Conclusions: While some researchers at UMass Chan have embraced data sharing, particularly genomic data sharing, we expect there will be more data shared in the coming years with the implementation of the new NIH Data Management and Sharing Policy.

Insurance denials and diagnostic rates in a pediatric genomic research cohort

PurposeThis study aimed to assess the amount and types of clinical genetic testing denied by insurance and the rate of diagnostic and candidate genetic findings identified through research in patients who faced insurance denials. MethodsAnalysis consisted of review of insurance denials in 801 patients enrolled in a pediatric genomic research repository with either no previous genetic testing or previous negative genetic testing result identified through cross-referencing with insurance prior-authorizations in patient medical records. Patients and denials were also categorized by type of insurance coverage. Diagnostic findings and candidate genetic findings in these groups were determined through review of our internal variant database and patient charts. ResultsOf the 801 patients analyzed, 147 had insurance prior-authorization denials on record (18.3%). Exome sequencing and microarray were the most frequently denied genetic tests. Private insurance was significantly more likely to deny testing than public insurance (odds ratio = 2.03 [95% CI = 1.38-2.99] P = .0003). Of the 147 patients with insurance denials, 53.7% had at least 1 diagnostic or candidate finding and 10.9% specifically had a clinically diagnostic finding. Fifty percent of patients with clinically diagnostic results had immediate medical management changes (5.4% of all patients experiencing denials). ConclusionMany patients face a major barrier to genetic testing in the form of lack of insurance coverage. A number of these patients have clinically diagnostic findings with medical management implications that would not have been identified without access to research testing. These findings support re-evaluation of insurance carriers’ coverage policies.

NIAGADS Alzheimer’s Genomics Database: version GRCh38

AbstractBackgroundThe Alzheimer’s Genomics Database (GenomicsDB) is an interactive knowledgebase for Alzheimer's disease (AD) genetics that offers a platform for data sharing, discovery, and analysis to accelerate research on AD and AD related dementias (ADRD). The resource provides unrestricted access to GWAS summary statistics datasets, variant annotations, and meta‐analysis results deposited at the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS). Here we introduce an updated version of the GenomicsDB, now mapped against the latest human genome assembly, GRCh38.MethodThe GenomicsDB allows users to interactively mine AD/ADRD summary statistics datasets or visually inspect them and compare with annotated variant tracks and GRCh38 aligned functional genomics tracks from the FILER functional genomics repository (Kuksa et al. 2022). Available tracks include key AD datasets from large‐scale sequencing projects such as IGAP (Lambert et al. 2013; Kunkle et al. 2019) and the ADGC (e.g., Naj et al. 2011) that have been lifted over from earlier genome builds. Available annotated variants include those from the AD Sequencing Project’s (ADSP) Release3 WGS, called from nearly 17k samples. All variants reported in the GenomicsDB are annotated using an assembly specific version of ADSP annotation pipeline (Butkiewicz et al. 2018). The GenomicsDB provides a REST API for programmatic access. Supported queries return complete annotations for single or bulk lookups of GenomicsDB entries (genes, variants, genomic loci) and ranged queries of datasets as JSON documents.ResultThe GenomicsDB currently hosts >70 datasets and reports >150 million annotated variants from the ADSP and 23 AD/ADRD GWAS studies, mapped to GRCh38. Keeping up with community standards allows this resource to provide ongoing support for AD/ADRD research by making it possible to integrate AD/ADRD relevant `omics datasets generated by the ADSP and others who have adopted GRCh38 as their reference genome.ConclusionThe NIAGADS Alzheimer’s Genomics Database v. GRCh38 (www.niagads.org/genomics) provides an up‐to‐date and accessible platform that facilitates unrestricted sharing of genetic knowledge underpinning AD/ADRD. To best serve our user community, NIAGADS will continue to provide the GRCh37.p19 version of the Alzheimer’s Genomics Database (including programmatic access) in archival format.

A Catalog of over 5,000 Metagenome-Assembled Microbial Genomes from the Caprinae Gut Microbiota.

Most microbiome studies regarding the ruminant digestive tract have focused on the rumen microbiota, whereas only a few studies were performed on investigating the gut microbiota of ruminants, which limits our understanding of this important component. Herein, the gut microbiota of 30 Caprinae animals (sheep and goats) from six provinces in China was characterized using ultradeep (>100 Gbp per sample) metagenome shotgun sequencing. An inventory of Caprinae gut microbial species containing 5,046 metagenomic assembly genomes (MAGs) was constructed. Particularly, 2,530 of the genomes belonged to uncultured candidate species. These genomes largely expanded the genomic repository of the current microbes in the Caprinae gut. Several enzymes and biosynthetic gene clusters encoded by these Caprinae gut species were identified. In summary, our study extends the gut microbiota characteristics of Caprinae and provides a basis for future studies on animal production and animal health. IMPORTANCE We constructed a microbiota catalog containing 5,046 MAGs from Caprinae gut from six regions of China. Most of the MAGs do not overlap known databases and appear to be potentially new species. We also characterized the functional spectrum of these MAGs and analyzed the differences between different regions. Our study enriches the understanding of taxonomic, functional, and metabolic diversity of Caprinae gut microbiota. We are confident that the manuscript will be of utmost interest to a wide range of readers and be widely applied in future research.

Twenty-five years of Genomes OnLine Database (GOLD): data updates and new features in v.9.

The Genomes OnLine Database (GOLD) (https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute (DOE-JGI) continues to maintain its role as one of the flagship genomic metadata repositories of the world. The ever-increasing number of projects and metadata are freely available to the user community world-wide. GOLD's metadata is consumed by scientists and remains an important source for large-scale comparative genomics analysis initiatives. Encouraged by this active user engagement and growth, GOLD has continued to add new components and capabilities. The new features such as a public Application Programming Interface (API) and Ecosystem landing page as well as the growth of different entities in this current GOLD v.9 edition are described in detail in this manuscript.

GROP: A genomic information repository for oilplants.

Biomass energy is an essential component of the agriculture economy and represents an important and particularly significant renewable energy source in the fight against fossil fuel depletion and global warming. The recognition that many plants naturally synthesize hydrocarbons makes these oil plants indispensable resources for biomass energy, and the advancement of next-generation sequencing technology in recent years has now made available mountains of data on plants that synthesize oil. We have utilized a combination of bioinformatic protocols to acquire key information from this massive amount of genomic data and to assemble it into an oil plant genomic information repository, built through website technology, including Django, Bootstrap, and echarts, to create the Genomic Information Repository for Oil Plants (GROP) portal (http://grop.site/) for genomics research on oil plants. The current version of GROP integrates the coding sequences, protein sequences, genome structure, functional annotation information, and other information from 18 species, 22 genome assemblies, and 46 transcriptomes. GROP also provides BLAST, genome browser, functional enrichment, and search tools. The integration of the massive amounts of oil plant genomic data with key bioinformatics tools in a database with a user-friendly interface allows GROP to serve as a central information repository to facilitate studies on oil plants by researchers worldwide.

The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition.

Concerns regarding inappropriate leakage of sensitive personal information as well as unauthorized data use are increasing with the growth of genomic data repositories. Therefore, privacy and security of genomic data have become increasingly important and need to be studied. With many proposed protection techniques, their applicability in support of biomedical research should be well understood. For this purpose, we have organized a community effort in the past 8 years through the integrating data for analysis, anonymization and sharing consortium to address this practical challenge. In this article, we summarize our experience from these competitions, report lessons learned from the events in 2020/2021 as examples, and discuss potential future research directions in this emerging field.

Building an Australian Reference Genome Atlas

Currently, genomics data for living species are stored in public and private repositories online. These repositories remain largely disconnected and only partially findable. The Australian Reference Genome Atlas (ARGA) Project is solving the problem of genomics data obscurity by creating an online platform where life sciences researchers can comprehensively and confidently search for data for taxa relevant to Australian research. At its most basic, ARGA is a tool for aggregating and indexing publicly available genomics (and genetics) data. We aim to improve the experience of discovering and accessing this data by building search functionality, based on features such as phenotypic traits and predicted and observed species distributions, and supporting data packaging and transfer to analysis environments. ARGA will index GenBank (National Institutes of Health (NIH), USA), the European Nucleotide Archive (EMBL-ENA), the database of Bioplatforms Australia, and selected DNA repositories in Australian faunal collections and herbaria. We will integrate these records with the occurrence records and taxonomic framework of the Atlas of Living Australia (ALA) to enrich the data and make it searchable using taxonomy, location, ecological characteristics and selected phenotypic data. The chief aims and outputs for the project are to: create a system to enable contextual metadata about a species to be used as a pointer to a variety of genomic data associated with that species; add functionality to that system to enable additional contextual information groupings, and community curation of these created groupings; create a user-facing web-accessible interface for the system; and devise a mechanism that allows the researchers searching the multiple genomic repositories, via ARGA, to select files for subsequent analysis and export them to other cloud-based analysis infrastructure. create a system to enable contextual metadata about a species to be used as a pointer to a variety of genomic data associated with that species; add functionality to that system to enable additional contextual information groupings, and community curation of these created groupings; create a user-facing web-accessible interface for the system; and devise a mechanism that allows the researchers searching the multiple genomic repositories, via ARGA, to select files for subsequent analysis and export them to other cloud-based analysis infrastructure. Our approach to ARGA incorporates: ingesting species metadata from multiple sequence repositories into a consistent data format using Darwin Core Archive (DwC-A); processing metadata using the Pipelines system developed by the Global Biodiversity Information Facility (GBIF), and as implemented in the ALA and other Living Atlases. indexing metadata using a Solr search engine; and providing a front-end web interface for users to find, select and export sequence files to a number of cloud-based analysis platforms. ingesting species metadata from multiple sequence repositories into a consistent data format using Darwin Core Archive (DwC-A); processing metadata using the Pipelines system developed by the Global Biodiversity Information Facility (GBIF), and as implemented in the ALA and other Living Atlases. indexing metadata using a Solr search engine; and providing a front-end web interface for users to find, select and export sequence files to a number of cloud-based analysis platforms. Here we will present an overview of the ARGA infrastructure and demonstrate an early prototype of the platform. We will show how ARGA can be used to interrogate DNA sequence records for taxa relevant to Australian research questions, realising a vision where genomics-based solutions to biological questions in conservation, ecology, agriculture and biosecurity can be manifested.

A blockchain-based application for genomic access and variant discovery using smart contracts and homomorphic encryption

Genomic data repositories are rapidly growing due to the decline in the cost of DNA sequencing. This has increased the demand from stakeholders such as researchers to analyze these datasets to advance areas in biomedical research. Genomic datasets are mostly maintained by third-party direct-to-consumer (DTC) genomic companies who operate a business model of collecting DNA data from their customers and selling them to pharmaceutical companies. This puts the privacy of their customers at risk since each individual’s human genome is unique. In addition, customers lose ownership of and access to their genomic data to DTCs and DTCs do not share profits from data sales with them. In this paper, we propose a system based on blockchain technology and homomorphic computation to address the aforementioned problems. We use blockchain transactions and smart contracts to allow genomic data owners (DOs) to have control of their data and sell access to it, and homomorphic computation with secure two-party protocol to enable genomic data users (DUs) to run queries to securely discover DOs of interest. We further optimize the query response time by proposing an approach based on genomic data partitions, binary search trees and bloom filters to reduce the search space. We also propose a blockchain penalty mechanism to encourage parties to behave honestly to avoid malicious behaviors such as uploading fake or non-human genomic data. We conduct our experiments on real genomic datasets and demonstrate that our proposed scheme allows DOs to control access to their data and is feasible and efficient in terms of computation cost, query response time and scalability.

Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR

Background: Recent estimates indicate that the COVID-19 pandemic, which is caused by the SARS-CoV-2 virus, could be effectively controlled via the development and implementation of diagnostic tools such as quantitative reverse transcription PCR (RT-qPCR). However, this reaction often generates false-negative results due to novel mutations and can also be affected by the secondary structure of the RNA transcripts that derive from the gene sequence used for diagnostic purposes. Methods: Using high-performance computing, we consolidated a global SARS-CoV-2 genome repository encompassing 19,317 genomes from the GenBank database and 107,259 from the GISAID database to generate monthly SARS-CoV-2 consensus sequences from January to December 2020. Results: These sequences were then used to create ORF8-specific primers and probes to validate single and multiplex RT-qPCR protocols both in silico and experimentally using genes E (Berlin protocol) and N (CDC protocol) as targets. Conclusions: Our findings demonstrated that RT-qPCR Ct values were improved by the inclusion of either a denaturing solution composed of tetraethylammonium chloride (TEA) and dimethyl sulfoxide (DMSO) and by adjusting nucleotide proportions based on the SARS-CoV-2 genome.

Public human microbiome data are dominated by highly developed countries.

The importance of sampling from globally representative populations has been well established in human genomics. In human microbiome research, however, we lack a full understanding of the global distribution of sampling in research studies. This information is crucial to better understand global patterns of microbiome-associated diseases and to extend the health benefits of this research to all populations. Here, we analyze the country of origin of all 444,829 human microbiome samples that are available from the world’s 3 largest genomic data repositories, including the Sequence Read Archive (SRA). The samples are from 2,592 studies of 19 body sites, including 220,017 samples of the gut microbiome. We show that more than 71% of samples with a known origin come from Europe, the United States, and Canada, including 46.8% from the US alone, despite the country representing only 4.3% of the global population. We also find that central and southern Asia is the most underrepresented region: Countries such as India, Pakistan, and Bangladesh account for more than a quarter of the world population but make up only 1.8% of human microbiome samples. These results demonstrate a critical need to ensure more global representation of participants in microbiome studies.

FILER: a framework for harmonizing and querying large-scale functional genomics knowledge.

ABSTRACTQuerying massive functional genomic and annotation data collections, linking and summarizing the query results across data sources/data types are important steps in high-throughput genomic and genetic analytical workflows. However, these steps are made difficult by the heterogeneity and breadth of data sources, experimental assays, biological conditions/tissues/cell types and file formats. FILER (FunctIonaL gEnomics Repository) is a framework for querying large-scale genomics knowledge with a large, curated integrated catalog of harmonized functional genomic and annotation data coupled with a scalable genomic search and querying interface. FILER uniquely provides: (i) streamlined access to >50 000 harmonized, annotated genomic datasets across >20 integrated data sources, >1100 tissues/cell types and >20 experimental assays; (ii) a scalable genomic querying interface; and (iii) ability to analyze and annotate user’s experimental data. This rich resource spans >17 billion GRCh37/hg19 and GRCh38/hg38 genomic records. Our benchmark querying 7 × 109 hg19 FILER records shows FILER is highly scalable, with a sub-linear 32-fold increase in querying time when increasing the number of queries 1000-fold from 1000 to 1 000 000 intervals. Together, these features facilitate reproducible research and streamline integrating/querying large-scale genomic data within analyses/workflows. FILER can be deployed on cloud or local servers (https://bitbucket.org/wanglab-upenn/FILER) for integration with custom pipelines and is freely available (https://lisanwanglab.org/FILER).

A novel risk signature with 6 RNA binding proteins for prognosis prediction in patients with glioblastoma.

Recent studies suggested that RNA binding proteins (RBPs) were related to the tumorigenesis and progression of glioma. This study was conducted to identify prognostic RBPs of glioblastoma (GBM) and construct an RBP signature to predict the prognosis of GBM.Univariate Cox regression analysis was carried out to identify the RBPs associated with overall survival of GBM in the The Cancer Genome Atlas (TCGA), GSE16011, and Repository for Molecular Brain Neoplasia data (Rembrandt) datasets, respectively. Overlapping RBPs from the TCGA, GSE16011, and Rembrandt datasets were selected. The biological role of prognostic RBPs was assessed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein–protein interaction analyses. Least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis were used to construct an RBP-related risk signature. The prognostic value of RBP signature was measured by Kaplan–Meier method and time-dependent receiver operating characteristic curve. A nomogram based on independent prognostic factors was established to predict survival for GBM. The CGGA cohort was used as the validation cohort for external validation.This study identified 27 RBPs associated with the prognosis of GBM and constructed a 6-RPBs signature. Kaplan–Meier curves suggested that high-risk score was associated with a poor prognosis. Area under the curve of 1-, 3-, and 5-year overall survival was 0.618, 0.728, and 0.833 for TCGA cohort, 0.655, 0.909, and 0.911 for GSE16011 cohort, and 0.665, 0.792, and 0.781 for Rembrandt cohort, respectively. A nomogram with 4 parameters (age, chemotherapy, O6-methylguanine-DNA methyltransferase promoter status, and risk score) was constructed. The calibration curve showed that the nomogram prediction was in good agreement with the actual observation.The 6-RBPs signature could effectively predict the prognosis of GBM, and our findings supplemented the prognostic index of GBM to a certain extent.

Identification of Selenoprotein H Isoforms and Impact of Selenoprotein H Overexpression on Protein But Not mRNA Levels of 2 Other Selenoproteins in 293T Cells

BackgroundSelenoprotein H (SELONOH), a member of the thioredoxin-like family proteins, is prioritized to degradation in selenium (Se) insufficiency. Recent studies implicate protective roles of SELENOH in oxidative stress, cellular senescence, and intestinal tumorigenesis. Although the nonselenoprotein H0YE28 is suggested as shortened SELENOH according to genomic and proteomic data repositories, this variant has not been verified biochemically. ObjectivesWe sought to identify SELENOH isoforms and explore the impact of Se flux on selenoprotein expression in SELENOH-overexpressing cells. MethodsA vector expressing a FLAG (the DYKDDDDK sequence) tag on the N-terminal end of wild-type SELENOH was constructed and transiently transfected into 293T cells incubated with graded concentrations of Na2SeO3 (0–200 nM). Cells were subjected to immunoprecipitation, LC-MS/MS protein analysis, immunoblotting, qRT-PCR, and senescence assays. Data were analyzed by 1-way or 2-way ANOVA. ResultsResults of anti-FLAG immunoblotting showed that FLAG-SELENOH transfection increased (3.7-fold; P < 0.05) protein levels of the long, but not the short, SELENOH variants in the presence of Na2SeO3 (100 nM). By contrast, SELENOH mRNA levels were increased by 53-fold upon FLAG-SELENOH transfection but were comparable with or without supplemental Se (100 nM). LC-MS/MS analyses of anti-FLAG immunoprecipitates designated both anti-FLAG bands as SELENOH and co-identified three 60S ribosomal and 9 other proteins. Overexpression of FLAG-SELENOH 1) reduced glutathione peroxidase 1 and thioredoxin reductase 1 expression at the protein rather than the mRNA level in the absence but not presence of supplemental Se (100 nM; P < 0.05); 2) increased mRNA levels of 3 heat shock proteins (HSP27, HSP70-1A, and HSP70-1B; P < 0.05); and 3) reduced senescence induced by H2O2 (20 μM, 4 hours; P < 0.05). ConclusionsThese cellular studies demonstrate a Se-independent, shortened SELENOH variant and suggest competition of overexpressed FLAG-SELENOH with 2 other selenoproteins for the expression at the protein but not the mRNA level in Se insufficiency.

Development of a Big Data Radiation Oncology Dashboard

<h3>Purpose/Objective(s)</h3> Healthcare data often exist in silos and in unstructured formats that limit interoperability and require tedious manual extraction. Our institution has adopted a flexible and scalable big data platform built on Hadoop that integrates data from Epic/Clarity as well as Aria and allows users to leverage modern data science tools to facilitate access. We hypothesize that a data analytics and visualization dashboard can be built using open-source tools that will (1) allow non-technical users to explore de-identified clinical data within our institutional big data platform and (2) connect with repositories of molecular data to demonstrate potential methods of integrating clinical and basic science data. <h3>Materials/Methods</h3> De-identified patient-level radiation oncology data from the institutional big data platform (Hadoop) were extracted with the python packages pyodbc and pandas. For the purposes of this dashboard, radiation oncology specific clinical data elements were queried including the date of first radiation treatment, treatment location, treatment modality (SBRT, external beam, SRS, TBI, LDR/HDR brachytherapy), ICD10 codes, anatomic treatment site, number of fractions, treatment prescription, and dose per fraction. A python client connection with the publicly accessible instance of cBioPortal for Cancer Genomics was established using the Bravado library. Data transformation and cleaning was performed in python using panda's data frames. A web-based dashboard to facilitate user-defined visualizations was implemented using the Dash python library and interactive visualizations of subsets of extracted data were generated in real-time using the plotly plotting library. <h3>Results</h3> We developed a web-based dashboard that gives users without extensive programming expertise the ability to explore de-identified clinical data extracted from Hadoop. As proof of principle, the dashboard was used to visualize the clinical impact of the COVID-19 pandemic on radiation oncology patient volumes, revealing a significant decline in new radiation treatments in April and May of 2020 (-54% and -36% compared to 2019) during the initial COVID-19 surge. Furthermore, the dashboard allows users to interact with the cBioPortal for Cancer Genomics repository, which currently houses clinical and molecular data from 301 publicly available studies spanning 869 different cancer types. This interface with cBioPortal illustrates the potential for future integration of clinically meaningful sequencing results with clinical outcomes data. <h3>Conclusion</h3> We built an interactive web-based dashboard to enable general users' easy access to de-identified clinical data stored within the institutional big data platform. Additional data sources, including external molecular data can be connected to the dashboard allowing for future integration.

Biallelic AOPEP Loss-of-Function Variants Cause Progressive Dystonia with Prominent Limb Involvement.

Monogenic causes of isolated dystonia are heterogeneous. Assembling cohorts of affected individuals sufficiently large to establish new gene-disease relationships can be challenging. We sought to expand the catalogue of monogenic etiologies for isolated dystonia. After the discovery of a candidate variant in a multicenter exome-sequenced cohort of affected individuals with dystonia, we queried online platforms and genomic data repositories worldwide to identify subjects with matching genotypic profiles. Seven different biallelic loss-of-function variants in AOPEP were detected in five probands from four unrelated families with strongly overlapping phenotypes. In one proband, we observed a homozygous nonsense variant (c.1477C>T [p.Arg493*]). A second proband harbored compound heterozygous nonsense variants (c.763C>T [p.Arg255*]; c.777G>A [p.Trp259*]), whereas a third proband possessed a frameshift variant (c.696_697delAG [p.Ala234Serfs*5]) in trans with a splice-disrupting alteration (c.2041-1G>A). Two probands (siblings) from a fourth family shared compound heterozygous frameshift alleles (c.1215delT [p.Val406Cysfs*14]; c.1744delA [p.Met582Cysfs*6]). All variants were rare and expected to result in truncated proteins devoid of functionally important amino acid sequence. AOPEP, widely expressed in developing and adult human brain, encodes a zinc-dependent aminopeptidase, a member of a class of proteolytic enzymes implicated in synaptogenesis and neural maintenance. The probands presented with disabling progressive dystonia predominantly affecting upper and lower extremities, with variable involvement of craniocervical muscles. Dystonia was unaccompanied by any additional symptoms in three families, whereas the fourth family presented co-occurring late-onset parkinsonism. Our findings suggest a likely causative role of predicted inactivating biallelic AOPEP variants in cases of autosomal recessive dystonia. Additional studies are warranted to understand the pathophysiology associated with loss-of-function variation in AOPEP. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.