HeartBioPortal
2019; Wolters Kluwer; Volume: 12; Issue: 4 Linguagem: Inglês
10.1161/circgen.118.002426
ISSN2574-8300
AutoresBohdan B. Khomtchouk, Kasra A. Vand, William C. Koehler, Diem-Trang Tran, Kai Middlebrook, Shyam Sudhakaran, Christopher S. Nelson, Or Gozani, Themistocles L. Assimes,
Tópico(s)RNA modifications and cancer
ResumoHomeCirculation: Genomic and Precision MedicineVol. 12, No. 4HeartBioPortal Free AccessLetterPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessLetterPDF/EPUBHeartBioPortalAn Internet-of-Omics for Human Cardiovascular Disease Data Bohdan B. Khomtchouk, PhD, Kasra A. Vand, BS, William C. Koehler, BS, Diem-Trang Tran, BS, Kai Middlebrook, BS, Shyam Sudhakaran, BS, Christopher S. Nelson, PhD, Or Gozani, MD, PhD and Themistocles L. Assimes, MD, PhD Bohdan B. KhomtchoukBohdan B. Khomtchouk Bohdan Khomtchouk, PhD, Department of Medicine, Division of Cardiovascular Medicine, Stanford University, 1070 Arastradero Rd, Palo Alto, CA 94304. Email E-mail Address: [email protected] Department of Biology, Stanford University, Stanford, CA (B.B.K., O.G.). Department of Medicine, Stanford University School of Medicine, Stanford, CA (B.B.K., T.L.A.). VA Palo Alto Health Care System, Palo Alto, CA (B.B.K., T.L.A.). Search for more papers by this author , Kasra A. VandKasra A. Vand Quiltomics, Palo Alto, CA (K.A.V., W.C.K., K.M., S.S.). *Vand and Koehler contributed equally to this work. Search for more papers by this author , William C. KoehlerWilliam C. Koehler Quiltomics, Palo Alto, CA (K.A.V., W.C.K., K.M., S.S.). *Vand and Koehler contributed equally to this work. Search for more papers by this author , Diem-Trang TranDiem-Trang Tran School of Computing, University of Utah, Salt Lake City, UT (D.-T.T). Search for more papers by this author , Kai MiddlebrookKai Middlebrook Quiltomics, Palo Alto, CA (K.A.V., W.C.K., K.M., S.S.). Search for more papers by this author , Shyam SudhakaranShyam Sudhakaran Quiltomics, Palo Alto, CA (K.A.V., W.C.K., K.M., S.S.). Search for more papers by this author , Christopher S. NelsonChristopher S. Nelson Independent Researcher (C.S.N.). Search for more papers by this author , Or GozaniOr Gozani Department of Biology, Stanford University, Stanford, CA (B.B.K., O.G.). Search for more papers by this author and Themistocles L. AssimesThemistocles L. Assimes Department of Medicine, Stanford University School of Medicine, Stanford, CA (B.B.K., T.L.A.). VA Palo Alto Health Care System, Palo Alto, CA (B.B.K., T.L.A.). Search for more papers by this author Originally published16 Apr 2019https://doi.org/10.1161/CIRCGEN.118.002426Circulation: Genomic and Precision Medicine. 2019;12:e002426Cardiovascular disease (CVD) is the leading cause of death worldwide, responsible for over 17 million deaths annually, a rate which outpaces even that related to cancer. Despite these sobering statistics, the state-of-the-art in computational infrastructure for the study of contemporary datasets related to CVD lags substantially behind that widely available in oncology, where improved data science and visualization methods have delivered publicly available comprehensive cancer genomics resources like Memorial Sloan Kettering Cancer Center’s cBioPortal1,2 and the National Cancer Institute’s Genomic Data Commons Portal.3,4 In our view, such portals do an outstanding job of transforming data from The Cancer Genome Atlas (TCGA) into logical data visualizations that provide additional biological insight. Developing a similar user-friendly computational platform for CVD could significantly lower the barriers of discovery by providing researchers with rapid, intuitive, and high-quality visual access to molecular profiles and clinical attributes from existing CVD projects.To our knowledge, no open-access computational resource for interactive visual exploration of multidimensional -omics datasets focused on CVD exists that rivals the utility, simplicity, and power of cBioPortal or the Genomic Data Commons Portal. Although a large amount of data from various CVD studies has been deposited in dbGaP, these data are only accessible to researchers after a time-consuming manual review of databases to identify relevant datasets and a somewhat onerous administrative data request process. Within the field of CVD research, the American Heart Association’s Precision Medicine Platform has significantly facilitated access to such data by streamlining the search, request, and transfer of controlled-access data and harmonizing datasets for cloud-based analyses.5 Notwithstanding these important contributions of the Precision Medicine Platform, we note that the Platform’s output currently mimics the content of TCGA (ie, raw and harmonized data with no preprocessing or visualization of its internals). Although the strength of the Precision Medicine Platform centers on providing curated CVD datasets, programming tools/tutorials, and forums for collaboration, a need that remains unmet is an informatics infrastructure that can quickly distill CVD-related datasets into easy-to-interpret, insightful figures and charts in a manner analogous to the cBioPortal and Genomic Data Commons Portal, but whose content is updated on a regular basis.Here we present HeartBioPortal (https://www.heartbioportal.com), a publicly available web application that takes the first important steps towards fulfilling this need by integrating existing CVD-related omics datasets in real time across the biological dataverse to provide intuitive visualization and analyses in addition to data downloads. The complementary focus of the HeartBioPortal platform is to provide community support for issuing gene/disease/variant-specific requests and visualizing the search results in a multi-omics context. By establishing an output like that of cBioPortal or the Genomic Data Commons Portal—containing preprocessed/analyzed data plus interactive visualizations accessible to the broader CVD and stroke research communities—we hope that HeartBioPortal significantly lowers the barriers between complex CVD datasets and researchers who require rapid, intuitive, and high-quality data visualization of molecular profiles and clinical attributes embedded within these datasets.Currently, HeartBioPortal features gene expression, genetic association, and ancestry allele frequency information for 23 606 human genes and 5792 variants across 12 broadly defined CVDs spanning 199 research studies. A workflow architecture diagram is depicted (Figure). From a biological database perspective, HeartBioPortal currently syncs and harmonizes CVD-relevant data in real time across at least the following publicly available resources: ClinVar, NHGRI-EBI GWAS Catalog, OMIM, dbGaP, GTEx, CREEDS, HapMap, 1000Genomes, TOPMed, ExAC/gnomAD, Ensembl, and GEO. We coin the phrase internet-of-omics (an omics-centric hybrid of the internet-of-things and internet-of-data fields) to describe the methods we have deployed to achieve such heterogeneous database interoperability. We believe the phrase appropriately emphasizes the challenges of extracting relevant biological information, in this case CVD omics datasets, across various biomedical resource facilities (eg, PubMed publications, metadata entries of data files stored in biological databases, etc), where we have abstracted the concept of things from hardware (devices) to software (databases). The overarching goal of our internet-of-omics algorithms is to establish massively integrated e-connectivity of omics datasets from multiple biological data silos linked to each other by communication networks based on metadata context. Harmonized tidy data downloads are provided directly through HeartBioPortal’s application programming interface to support the data needs of academic clinical/laboratory CVD researchers and facilitate community transparency/cross-validation of the original data sources.Download figureDownload PowerPointFigure. HeartBioPortal workflow architecture diagram. HeartBioPortal features a simple user interface that accepts queries for gene (current), disease/variant (future). User’s input is processed through multiple stages on the server into queries to be run on an integrated database, and eventually into preprocessed data that can be visualized in a meaningful way. Visualization components are highly extensible as the multi-omics database grows to include larger and more diverse sources of cardiovascular disease (CVD) data.Future directions in the development of HeartBioPortal include substantially enriching the current offering of studies focused on gene expression by refining the phenotype definitions and integrating alternative splicing information (eg, differential transcript usage and isoform-level expression) as well as adding CVD drug target identification and prioritization features to enable precompetitive drug discovery by assisting early CVD candidate target decisions, thereby bridging cutting-edge computational biology with clinical research. We also plan to expand HeartBioPortal’s genetic association content (currently restricted to the NHGRI-EBI GWAS Catalog) to include diverse GWAS consortium efforts as incorporated in the Broad Institute’s CVD and Cerebrovascular Disease Knowledge Portals. The end goal is to facilitate exploratory data analysis of integrative multi-omics CVD data spanning a diverse trove of expression, association, drug discovery, and population genetic information.AcknowledgmentsB.B. Khomtchouk acknowledges and thanks the American Heart Association (AHA) for financial support through the AHA Postdoctoral Fellowship program. B.B. Khomtchouk also thanks Komal Vadnagara and Evan Kaeding for useful discussions.Sources of FundingResearch reported in this publication was supported by the American Heart Association (AHA) Postdoctoral Fellowship grant number 18POST34030375 (Khomtchouk).DisclosuresThe authors declare that all supporting data are available within the article and can be accessed at http://www.heartbioportal.com/user_api. Stanford University has filed OTL disclosure on the methods described, and B.B. Khomtchouk, O. Gozani, and T.L. Assimes are named as inventors. B.B. Khomtchouk is a cofounder of Quiltomics. O. Gozani is a cofounder of EpiCypher, Inc and Athelas Therapeutics, Inc. The other authors report no conflicts.Footnotes*Vand and Koehler contributed equally to this work.Bohdan Khomtchouk, PhD, Department of Medicine, Division of Cardiovascular Medicine, Stanford University, 1070 Arastradero Rd, Palo Alto, CA 94304. Email [email protected]eduReferences1. Cerami E, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data.Cancer Discov. 2012; 2:401–404. doi: 10.1158/2159-8290.CD-12-0095CrossrefMedlineGoogle Scholar2. Gao J, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.Sci Signal. 2013; 6:pl1. doi: 10.1126/scisignal.2004088CrossrefMedlineGoogle Scholar3. Grossman RL, et al. Toward a shared vision for cancer genomic data.N Engl J Med. 2016; 375:1109–1112. doi: 10.1056/NEJMp1607591CrossrefMedlineGoogle Scholar4. Jensen MA, et al. The NCI genomic sata commons as an engine for precision medicine.Blood. 2017; 130:453–459. doi: 10.1182/blood-2017-03-735654CrossrefMedlineGoogle Scholar5. Kass-Hout TA, et al. American Heart Association precision medicine platform.Circulation. 2018; 137:647–649. doi: 10.1161/CIRCULATIONAHA.117.032041LinkGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Caufield J, Sigdel D, Fu J, Choi H, Guevara-Gonzalez V, Wang D and Ping P (2021) Cardiovascular informatics: building a bridge to data harmony, Cardiovascular Research, 10.1093/cvr/cvab067, 118:3, (732-745), Online publication date: 21-Feb-2022. Khomtchouk B, Tran D, Vand K, Might M, Gozani O and Assimes T (2019) Cardioinformatics: the nexus of bioinformatics and precision cardiology, Briefings in Bioinformatics, 10.1093/bib/bbz119, 21:6, (2031-2051), Online publication date: 1-Dec-2020. April 2019Vol 12, Issue 4 Advertisement Article InformationMetrics © 2019 American Heart Association, Inc.https://doi.org/10.1161/CIRCGEN.118.002426PMID: 31294639 Originally publishedApril 16, 2019 Keywordscardiovascular diseasealgorithmsgeneticsdatabasephenotypePDF download Advertisement SubjectsComputational BiologyGene Expression and RegulationGenetics
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