NIAGADS Alzheimer's GenomicsDB: A resource for exploring Alzheimer's disease genetic and genomic knowledge.

INTRODUCTION: The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site Alzheimer's Genomics Database (GenomicsDB) is a public knowledge base of Alzheimer's disease (AD) genetic datasets and genomic annotations. METHODS: GenomicsDB uses a custom systems architecture to adopt and enforce rigorous standards that facilitate harmonization of AD-relevant genome-wide association study summary statistics datasets with functional annotations, including over 230 million annotated variants from the AD Sequencing Project. RESULTS: GenomicsDB generates interactive reports compiled from the harmonized datasets and annotations. These reports contextualize AD-risk associations in a broader functional genomic setting and summarize them in the context of functionally annotated genes and variants. DISCUSSION: Created to make AD-genetics knowledge more accessible to AD researchers, the GenomicsDB is designed to guide users unfamiliar with genetic data in not only exploring but also interpreting this ever-growing volume of data. Scalable and interoperable with other genomics resources using data technology standards, the GenomicsDB can serve as a central hub for research and data analysis on AD and related dementias. HIGHLIGHTS: The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) offers to the public a unique, disease-centric collection of AD-relevant GWAS summary statistics datasets. Interpreting these data is challenging and requires significant bioinformatics expertise to standardize datasets and harmonize them with functional annotations on genome-wide scales. The NIAGADS Alzheimer's GenomicsDB helps overcome these challenges by providing a user-friendly public knowledge base for AD-relevant genetics that shares harmonized, annotated summary statistics datasets from the NIAGADS repository in an interpretable, easily searchable format.

OBO Foundry in 2021: operationalizing open data principles to evaluate ontologies.

Biological ontologies are used to organize, curate and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here, we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology's compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable, federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data Findable, Accessible, Interoperable, and Reusable (FAIR). Database URL http://obofoundry.org/.

Standardization of assay representation in the Ontology for Biomedical Investigations.

The Ontology for Biomedical Investigations (OBI) underwent a focused review of assay term annotations, logic and hierarchy with a goal to improve and standardize these terms. As a result, inconsistencies in W3C Web Ontology Language (OWL) expressions were identified and corrected, and additionally, standardized design patterns and a formalized template to maintain them were developed. We describe here this informative and productive process to describe the specific benefits and obstacles for OBI and the universal lessons for similar projects.

Temporal Transcriptome Analysis Reveals Dynamic Gene Expression Patterns Driving ß-Cell Maturation.

Newly differentiated pancreatic ß cells lack proper insulin secretion profiles of mature functional ß cells. The global gene expression differences between paired immature and mature ß cells have been studied, but the dynamics of transcriptional events, correlating with temporal development of glucose-stimulated insulin secretion (GSIS), remain to be fully defined. This aspect is important to identify which genes and pathways are necessary for ß-cell development or for maturation, as defective insulin secretion is linked with diseases such as diabetes. In this study, we assayed through RNA sequencing the global gene expression across six ß-cell developmental stages in mice, spanning from ß-cell progenitor to mature ß cells. A computational pipeline then selected genes differentially expressed with respect to progenitors and clustered them into groups with distinct temporal patterns associated with biological functions and pathways. These patterns were finally correlated with experimental GSIS, calcium influx, and insulin granule formation data. Gene expression temporal profiling revealed the timing of important biological processes across ß-cell maturation, such as the deregulation of ß-cell developmental pathways and the activation of molecular machineries for vesicle biosynthesis and transport, signal transduction of transmembrane receptors, and glucose-induced Ca2+ influx, which were established over a week before ß-cell maturation completes. In particular, ß cells developed robust insulin secretion at high glucose several days after birth, coincident with the establishment of glucose-induced calcium influx. Yet the neonatal ß cells displayed high basal insulin secretion, which decreased to the low levels found in mature ß cells only a week later. Different genes associated with calcium-mediated processes, whose alterations are linked with insulin resistance and deregulation of glucose homeostasis, showed increased expression across ß-cell stages, in accordance with the temporal acquisition of proper GSIS. Our temporal gene expression pattern analysis provided a comprehensive database of the underlying molecular components and biological mechanisms driving ß-cell maturation at different temporal stages, which are fundamental for better control of the in vitro production of functional ß cells from human embryonic stem/induced pluripotent cell for transplantation-based type 1 diabetes therapy.

A developmental lineage-based gene co-expression network for mouse pancreatic ß-cells reveals a role for Zfp800 in pancreas development.

To gain a deeper understanding of pancreatic ß-cell development, we used iterative weighted gene correlation network analysis to calculate a gene co-expression network (GCN) from 11 temporally and genetically defined murine cell populations. The GCN, which contained 91 distinct modules, was then used to gain three new biological insights. First, we found that the clustered protocadherin genes are differentially expressed during pancreas development. Pcdhγ genes are preferentially expressed in pancreatic endoderm, Pcdhß genes in nascent islets, and Pcdhα genes in mature ß-cells. Second, after extracting sub-networks of transcriptional regulators for each developmental stage, we identified 81 zinc finger protein (ZFP) genes that are preferentially expressed during endocrine specification and ß-cell maturation. Third, we used the GCN to select three ZFPs for further analysis by CRISPR mutagenesis of mice. Zfp800 null mice exhibited early postnatal lethality, and at E18.5 their pancreata exhibited a reduced number of pancreatic endocrine cells, alterations in exocrine cell morphology, and marked changes in expression of genes involved in protein translation, hormone secretion and developmental pathways in the pancreas. Together, our results suggest that developmentally oriented GCNs have utility for gaining new insights into gene regulation during organogenesis.

Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project.

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient's phenotype. The development of two new ontologies - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation - will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.

A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer's Disease Through Expert Curation of Key Protein Targets.

BACKGROUND: The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality. OBJECTIVE: To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer's disease research. METHODS: We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community. RESULTS: Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset. CONCLUSION: This represents a significant enhancement in the expert curated data pertinent to Alzheimer's disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

Phospho-proteomic discovery of novel signal transducers including thioredoxin-interacting protein as mediators of erythropoietin-dependent human erythropoiesis.

Erythroid cell formation critically depends on signals transduced via erythropoietin (EPO)/EPO receptor (EPOR)/JAK2 complexes. This includes not only core response modules (e.g., JAK2/STAT5, RAS/MEK/ERK), but also specialized effectors (e.g., erythroferrone, ASCT2 glutamine transport, Spi2A). By using phospho-proteomics and a human erythroblastic cell model, we identify 121 new EPO target proteins, together with their EPO-modulated domains and phosphosites. Gene ontology (GO) enrichment for "Molecular Function" identified adaptor proteins as one top EPO target category. This includes a novel EPOR/JAK2-coupled network of actin assemblage modifiers, with adaptors DLG-1, DLG-3, WAS, WASL, and CD2AP as prime components. "Cellular Component" GO analysis further identified 19 new EPO-modulated cytoskeletal targets including the erythroid cytoskeletal targets spectrin A, spectrin B, adducin 2, and glycophorin C. In each, EPO-induced phosphorylation occurred at pY sites and subdomains, which suggests coordinated regulation by EPO of the erythroid cytoskeleton. GO analysis of "Biological Processes" further revealed metabolic regulators as a likewise unexpected EPO target set. Targets included aldolase A, pyruvate dehydrogenase α1, and thioredoxin-interacting protein (TXNIP), with EPO-modulated p-Y sites in each occurring within functional subdomains. In TXNIP, EPO-induced phosphorylation occurred at novel p-T349 and p-S358 sites, and was paralleled by rapid increases in TXNIP levels. In UT7epo-E and primary human stem cell (HSC)-derived erythroid progenitor cells, lentivirus-mediated short hairpin RNA knockdown studies revealed novel pro-erythropoietic roles for TXNIP. Specifically, TXNIP's knockdown sharply inhibited c-KIT expression; compromised EPO dose-dependent erythroblast proliferation and survival; and delayed late-stage erythroblast formation. Overall, new insight is provided into EPO's diverse action mechanisms and TXNIP's contributions to EPO-dependent human erythropoiesis.

Phospho-PTM proteomic discovery of novel EPO- modulated kinases and phosphatases, including PTPN18 as a positive regulator of EPOR/JAK2 Signaling.

The formation of erythroid progenitor cells depends sharply upon erythropoietin (EPO), its cell surface receptor (erythropoietin receptor, EPOR), and Janus kinase 2 (JAK2). Clinically, recombinant human EPO (rhEPO) additionally is an important anti-anemia agent for chronic kidney disease (CKD), myelodysplastic syndrome (MDS) and chemotherapy, but induces hypertension, and can exert certain pro-tumorigenic effects. Cellular signals transduced by EPOR/JAK2 complexes, and the nature of EPO-modulated signal transduction factors, therefore are of significant interest. By employing phospho-tyrosine post-translational modification (p-Y PTM) proteomics and human EPO- dependent UT7epo cells, we have identified 22 novel kinases and phosphatases as novel EPO targets, together with their specific sites of p-Y modification. New kinases modified due to EPO include membrane palmitoylated protein 1 (MPP1) and guanylate kinase 1 (GUK1) guanylate kinases, together with the cytoskeleton remodeling kinases, pseudopodium enriched atypical kinase 1 (PEAK1) and AP2 associated kinase 1 (AAK1). Novel EPO- modified phosphatases include protein tyrosine phosphatase receptor type A (PTPRA), phosphohistidine phosphatase 1 (PHPT1), tensin 2 (TENC1), ubiquitin associated and SH3 domain containing B (UBASH3B) and protein tyrosine phosphatase non-receptor type 18 (PTPN18). Based on PTPN18's high expression in hematopoietic progenitors, its novel connection to JAK kinase signaling, and a unique EPO- regulated PTPN18-pY389 motif which is modulated by JAK2 inhibitors, PTPN18's actions in UT7epo cells were investigated. Upon ectopic expression, wt-PTPN18 promoted EPO dose-dependent cell proliferation, and survival. Mechanistically, PTPN18 sustained the EPO- induced activation of not only mitogen-activated protein kinases 1 and 3 (ERK1/2), AKT serine/threonine kinase 1-3 (AKT), and signal transducer and activator of transcription 5A and 5B (STAT5), but also JAK2. Each effect further proved to depend upon PTPN18's EPO- modulated (p)Y389 site. In analyses of the EPOR and the associated adaptor protein RHEX (regulator of hemoglobinization and erythroid cell expansion), wt-PTPN18 increased high molecular weight EPOR forms, while sharply inhibiting the EPO-induced phosphorylation of RHEX-pY141. Each effect likewise depended upon PTPN18-Y389. PTPN18 thus promotes signals for EPO-dependent hematopoietic cell growth, and may represent a new druggable target for myeloproliferative neoplasms.

A novel tool for standardizing clinical data in a semantically rich model.

Standardizing clinical information in a semantically rich data model is useful for promoting interoperability and facilitating high quality research. Semantic Web technologies such as Resource Description Framework can be utilized to their full potential when a model accurately reflects the semantics of the clinical situation it describes. To this end, ontologies that abide by sound organizational principles can be used as the building blocks of a semantically rich model for the storage of clinical data. However, it is a challenge to programmatically define such a model and load data from disparate sources. The PennTURBO Semantic Engine is a tool developed at the University of Pennsylvania that transforms concise RDF data into a source-independent, semantically rich model. This system sources classes from an application ontology and specifically defines how instances of those classes may relate to each other. Additionally, the system defines and executes RDF data transformations by launching dynamically generated SPARQL update statements. The Semantic Engine was designed as a generalizable data standardization tool, and is able to work with various data models and incoming data sources. Its human-readable configuration files can easily be shared between institutions, providing the basis for collaboration on a standard data model.

ClinEpiDB: an open-access clinical epidemiology database resource encouraging online exploration of complex studies.

The concept of open data has been gaining traction as a mechanism to increase data use, ensure that data are preserved over time, and accelerate discovery. While epidemiology data sets are increasingly deposited in databases and repositories, barriers to access still remain. ClinEpiDB was constructed as an open-access online resource for clinical and epidemiologic studies by leveraging the extensive web toolkit and infrastructure of the Eukaryotic Pathogen Database Resources (EuPathDB; a collection of databases covering 170+ eukaryotic pathogens, relevant related species, and select hosts) combined with a unified semantic web framework. Here we present an intuitive point-and-click website that allows users to visualize and subset data directly in the ClinEpiDB browser and immediately explore potential associations. Supporting study documentation aids contextualization, and data can be downloaded for advanced analyses. By facilitating access and interrogation of high-quality, large-scale data sets, ClinEpiDB aims to spur collaboration and discovery that improves global health.

FungiDB: An Integrated Bioinformatic Resource for Fungi and Oomycetes.

FungiDB (fungidb.org) is a free online resource for data mining and functional genomics analysis for fungal and oomycete species. FungiDB is part of the Eukaryotic Pathogen Genomics Database Resource (EuPathDB, eupathdb.org) platform that integrates genomic, transcriptomic, proteomic, and phenotypic datasets, and other types of data for pathogenic and nonpathogenic, free-living and parasitic organisms. FungiDB is one of the largest EuPathDB databases containing nearly 100 genomes obtained from GenBank, Aspergillus Genome Database (AspGD), The Broad Institute, Joint Genome Institute (JGI), Ensembl, and other sources. FungiDB offers a user-friendly web interface with embedded bioinformatics tools that support custom in silico experiments that leverage FungiDB-integrated data. In addition, a Galaxy-based workspace enables users to generate custom pipelines for large-scale data analysis (e.g., RNA-Seq, variant calling, etc.). This review provides an introduction to the FungiDB resources and focuses on available features, tools, and queries and how they can be used to mine data across a diverse range of integrated FungiDB datasets and records.

Synaptotagmin 4 Regulates Pancreatic ß Cell Maturation by Modulating the Ca2+ Sensitivity of Insulin Secretion Vesicles.

Islet ß cells from newborn mammals exhibit high basal insulin secretion and poor glucose-stimulated insulin secretion (GSIS). Here we show that ß cells of newborns secrete more insulin than adults in response to similar intracellular Ca2+ concentrations, suggesting differences in the Ca2+ sensitivity of insulin secretion. Synaptotagmin 4 (Syt4), a non-Ca2+ binding paralog of the ß cell Ca2+ sensor Syt7, increased by ∼8-fold during ß cell maturation. Syt4 ablation increased basal insulin secretion and compromised GSIS. Precocious Syt4 expression repressed basal insulin secretion but also impaired islet morphogenesis and GSIS. Syt4 was localized on insulin granules and Syt4 levels inversely related to the number of readily releasable vesicles. Thus, transcriptional regulation of Syt4 affects insulin secretion; Syt4 expression is regulated in part by Myt transcription factors, which repress Syt4 transcription. Finally, human SYT4 regulated GSIS in EndoC-ßH1 cells, a human ß cell line. These findings reveal the role that altered Ca2+ sensing plays in regulating ß cell maturation.

MicrobiomeDB: a systems biology platform for integrating, mining and analyzing microbiome experiments.

MicrobiomeDB (http://microbiomeDB.org) is a data discovery and analysis platform that empowers researchers to fully leverage experimental variables to interrogate microbiome datasets. MicrobiomeDB was developed in collaboration with the Eukaryotic Pathogens Bioinformatics Resource Center (http://EuPathDB.org) and leverages the infrastructure and user interface of EuPathDB, which allows users to construct in silico experiments using an intuitive graphical 'strategy' approach. The current release of the database integrates microbial census data with sample details for nearly 14 000 samples originating from human, animal and environmental sources, including over 9000 samples from healthy human subjects in the Human Microbiome Project (http://portal.ihmpdcc.org/). Query results can be statistically analyzed and graphically visualized via interactive web applications launched directly in the browser, providing insight into microbial community diversity and allowing users to identify taxa associated with any experimental covariate.

Spatial phenotyping of the endocardial endothelium as a function of intracardiac hemodynamic shear stress.

Despite substantial evidence for the central role of hemodynamic shear stress in the functional integrity of vascular endothelial cells, hemodynamic and molecular regulation of the endocardial endothelium lining the heart chambers remains understudied. We propose that regional differences in intracardiac hemodynamics influence differential endocardial gene expression leading to phenotypic heterogeneity of this cell layer. Measurement of intracardiac hemodynamics was performed using 4-dimensional flow MRI in healthy humans (n=8) and pigs (n=5). Local wall shear stress (WSS) and oscillatory shear indices (OSI) were calculated in three distinct regions of the LV - base, mid-ventricle (midV), and apex. In both the humans and pigs, WSS values were significantly lower in the apex and midV relative to the base. Additionally, both the apex and midV had greater oscillatory shear indices (OSI) than the base. To investigate regional phenotype, endocardial endothelial cells (EEC) were isolated from an additional 8 pigs and RNA sequencing was performed. A false discovery rate of 0.10 identified 1051 differentially expressed genes between the base and apex, and 321 between base and midV. Pathway analyses revealed apical upregulation of genes associated with translation initiation. Furthermore, tissue factor pathway inhibitor (TFPI; mean 50-fold) and prostacyclin synthase (PTGIS; 5-fold), genes prominently associated with antithrombotic protection, were consistently upregulated in LV apex. These spatio-temporal WSS values in defined regions of the left ventricle link local hemodynamics to regional heterogeneity in endocardial gene expression.

EuPathDB: the eukaryotic pathogen genomics database resource.

The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host-pathogen interactions.

Pancreatic Inflammation Redirects Acinar to ß Cell Reprogramming.

Using a transgenic mouse model to express MafA, Pdx1, and Neurog3 (3TF) in a pancreatic acinar cell- and doxycycline-dependent manner, we discovered that the outcome of transcription factor-mediated acinar to ß-like cellular reprogramming is dependent on both the magnitude of 3TF expression and on reprogramming-induced inflammation. Overly robust 3TF expression causes acinar cell necrosis, resulting in marked inflammation and acinar-to-ductal metaplasia. Generation of new ß-like cells requires limiting reprogramming-induced inflammation, either by reducing 3TF expression or by eliminating macrophages. The new ß-like cells were able to reverse streptozotocin-induced diabetes 6 days after inducing 3TF expression but failed to sustain their function after removal of the reprogramming factors.

OBIB-a novel ontology for biobanking.

BACKGROUND: Biobanking necessitates extensive integration of data to allow data analysis and specimen sharing. Ontologies have been demonstrated to be a promising approach in fostering better semantic integration of biobank-related data. Hitherto no ontology provided the coverage needed to capture a broad spectrum of biobank user scenarios. METHODS: Based in the principles laid out by the Open Biological and Biomedical Ontologies Foundry two biobanking ontologies have been developed. These two ontologies were merged using a modular approach consistent with the initial development principles. The merging was facilitated by the fact that both ontologies use the same Upper Ontology and re-use classes from a similar set of pre-existing ontologies. RESULTS: Based on the two previous ontologies the Ontology for Biobanking (http://purl.obolibrary.org/obo/obib.owl) was created. Due to the fact that there was no overlap between the two source ontologies the coverage of the resulting ontology is significantly larger than of the two source ontologies. The ontology is successfully used in managing biobank information of the Penn Medicine BioBank. CONCLUSIONS: Sharing development principles and Upper Ontologies facilitates subsequent merging of ontologies to achieve a broader coverage.

Integrated Regional Cardiac Hemodynamic Imaging and RNA Sequencing Reveal Corresponding Heterogeneity of Ventricular Wall Shear Stress and Endocardial Transcriptome.

BACKGROUND: Unlike arteries, in which regionally distinct hemodynamics are associated with phenotypic heterogeneity, the relationships between endocardial endothelial cell phenotype and intraventricular flow remain largely unexplored. We investigated regional differences in left ventricular wall shear stress and their association with endocardial endothelial cell gene expression. METHODS AND RESULTS: Local wall shear stress was calculated from 4-dimensional flow magnetic resonance imaging in 3 distinct regions of human (n=8) and pig (n=5) left ventricle: base, adjacent to the outflow tract; midventricle; and apex. In both species, wall shear stress values were significantly lower in the apex and midventricle relative to the base; oscillatory shear index was elevated in the apex. RNA sequencing of the endocardial endothelial cell transcriptome in pig left ventricle (n=8) at a false discovery rate ≤10% identified 1051 genes differentially expressed between the base and the apex and 327 between the base and the midventricle; no differentially expressed genes were detected at this false discovery rate between the apex and the midventricle. Enrichment analyses identified apical upregulation of genes associated with translation initiation including mammalian target of rapamycin, and eukaryotic initiation factor 2 signaling. Genes of mitochondrial dysfunction and oxidative phosphorylation were also consistently upregulated in the left ventricular apex, as were tissue factor pathway inhibitor (mean 50-fold) and prostacyclin synthase (5-fold)-genes prominently associated with antithrombotic protection. CONCLUSIONS: We report the first spatiotemporal measurements of wall shear stress within the left ventricle and linked regional hemodynamics to heterogeneity in ventricular endothelial gene expression, most notably to translation initiation and anticoagulation properties in the left ventricular apex, in which oscillatory shear index is increased and wall shear stress is decreased.

The Ontology for Biomedical Investigations.

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.