Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data

Kibbe, Warren A.; Arze, Cesar; Felix, Victor; Mitraka, Elvira; Bolton, Evan; Fu, Gang; Mungall, Christopher J.; Binder, Janos X.; Malone, James; Vasant, Drashtti; Parkinson, Helen; Schriml, Lynn M.

doi:10.1093/nar/gku1011

Cited by 528 publications

(372 citation statements)

References 30 publications

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“…The existence of the ontology portals [14][15][16][17] where the terminology from different research fields is collected in the form of ontology is just one of the assumptions to successfully use the terminology. The outcome of the survey highlighted that many laboratories use the standards developed just in the particular laboratory.…”

Section: Resultsmentioning

confidence: 99%

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BioWes – From Design of Experiment, through Protocol to Repository, Control, Standardization and Back-Tracking

Bárta

Císař

Soloviov

et al. 2015

Bioinformatics and Biomedical Engineering

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Background:One of the main challenges in modern science is the amount of data produced by the experimental work; it is difficult to store, organize and share the scientific data and to extract the wealth of knowledge. Experimental method descriptions in scientific publications are often incomplete, which complicates experimental reproducibility. The proposed system was created in order to address these issues. It provides a solution for management of the experimental data and metadata to support the reproducibility. Implementation:The system is implemented as a repository for experiment descriptions and experimental data. It has three main entry points: desktop application for protocol design and data processing, web interface dedicated for protocol and data management, and web-based interface for mobile devices suitable for the field experiments. The functionality of desktop client can be extended using the custom plug-ins for data extraction and data processing. The system provides several methods to support experimental reproducibility: standardized terminology support, data and metadata at a single location, standardized protocol design or protocol evolution. Results and discussion:The system was tested in the framework of international infrastructure project AQUAEXCEL with five pilot installations at different institutes. The general testing in Tissue culture certified laboratory, Institute of complex systems and IFREMER verified the usability under different research infrastructures. The specific testing focused on the data processing modules and plug-ins demonstrated the modularity of the system for the specific conditions. The BioWes system represents experimental data as black box and therefore can handle any data type so as to provide broad usability for a variety of experiments and provide the data management infrastructure to improve the reproducibility and data sharing. Conclusions:The proposed system provides the tools for standard data management operations and extends the support by the standardization possibilities, protocol evolution with visualization features and modularity based on the data processing modules and device communication plug-ins. The software can be used at different organization levels: from a single researcher (to improve data organization) to research consortium through the central protocols management repository. Support from the Cisar et al. BioMed Eng OnLine 2016, 15(Suppl 1):S74 DOI 10.1186 BioMedical Engineering OnLine protocol design until being shared with the standardization features helps to improve the reproducibility of research work. The platform provides support from experimental protocol design to cooperation using simple sharing. RESEARCH

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Section: Resultsmentioning

confidence: 99%

“…Therefore standardization is hidden to the user or it is offered as a list of standardized terms based on the standardization provided by one of the standardization portals [14][15][16][17].…”

Section: Standardizationmentioning

confidence: 99%

BioWes – From Design of Experiment, through Protocol to Repository, Control, Standardization and Back-Tracking

Bárta

Císař

Soloviov

et al. 2015

Bioinformatics and Biomedical Engineering

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“…Information for the genomic coordinates of chromosome breakpoints was extracted from the UCSC Genome Bioinformatics Site (https://genome.ucsc.edu/). Disease Ontology database (http://disease-ontology.org/), an open source ontology for the integration of biomedical data that is associated with human disease [Kibbe et al 2015], was used for extraction of ontology terms and identification numbers (DOID). Furthermore, the availability of ontology terms was also checked in The International Classification of Diseases (ICD) (http://www.who.int/classi fications/icd/en/) and The Vertebrate Trait Ontology (https://bioportal.bioontology.org/ontologies/VT) (VTO) databases.…”

Section: Methodsmentioning

confidence: 99%

Initiative for standardization of reporting genetics of male infertility

Traven

Ogrinc

Kunej

2016

Systems Biology in Reproductive Medicine

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The number of publications on research of male infertility is increasing. Technologies used in research of male infertility generate complex results and various types of data that need to be appropriately managed, arranged, and made available to other researchers for further use. In our previous study, we collected over 800 candidate loci for male fertility in seven mammalian species. However, the continuation of the work towards a comprehensive database of candidate genes associated with different types of idiopathic human male infertility is challenging due to fragmented information, obtained from a variety of technologies and various omics approaches. Results are published in different forms and usually need to be excavated from the text, which hinders the gathering of information. Standardized reporting of genetic anomalies as well as causative and risk factors of male infertility therefore presents an important issue. The aim of the study was to collect examples of diverse genomic loci published in association with human male infertility and to propose a standardized format for reporting genetic causes of male infertility. From the currently available data we have selected 75 studies reporting 186 representative genomic loci which have been proposed as genetic risk factors for male infertility. Based on collected and formatted data, we suggested a first step towards unification of reporting the genetics of male infertility in original and review studies. The proposed initiative consists of five relevant data types: 1) genetic locus, 2) race/ethnicity, number of participants (infertile/controls), 3) methodology, 4) phenotype (clinical data, disease ontology, and disease comorbidity), and 5) reference. The proposed form for standardized reporting presents a baseline for further optimization with additional genetic and clinical information. This data standardization initiative will enable faster multi-omics data integration, database development and sharing, establishing more targeted hypotheses, and facilitating biomarker discovery.

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“…In the TCGA program from 2005 to 2014, over 30 cancers were studied using microarray and next-generation sequencing platforms, consequently producing large-scale data, such as gene expression, exon expression, miRNA, copy number variation (CNV), single nucleotide polymorphism (SNP), loss of heterozygosity (LOH), mutations, DNA methylation, and protein expression. Referring to Disease Ontology (Kibbe et al, 2015) and the TCGA data matrix (TCGA data matrix, 2015), we developed a controlled vocabulary for the TCGA cancer type (Table 1) and high-throughput platform (Table 2). As shown in Tables 1 and 2, the TCGA-defined terms were used to standardize the program-generated data description; however, they are not the terms used in the full-text articles.…”

Section: Tcga Data Usage Analysismentioning

confidence: 99%

Identifying Scientific Project-generated Data Citation from Full-text Articles: An Investigation of TCGA Data Citation

Zheng

Kang

et al. 2016

Journal of Data and Information Science

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Purpose:In the open science era, it is typical to share project-generated scientific data by depositing it in an open and accessible database. Moreover, scientific publications are preserved in a digital library archive. It is challenging to identify the data usage that is mentioned in literature and associate it with its source. Here, we investigated the data usage of a government-funded cancer genomics project, The Cancer Genome Atlas (TCGA), via a full-text literature analysis.Design/methodology/approach: We focused on identifying articles using the TCGA dataset and constructing linkages between the articles and the specific TCGA dataset. First, we collected 5,372 TCGA-related articles from PubMed Central (PMC). Second, we constructed a benchmark set with 25 full-text articles that truly used the TCGA data in their studies, and we summarized the key features of the benchmark set. Third, the key features were applied to the remaining PMC full-text articles that were collected from PMC. Findings:The amount of publications that use TCGA data has increased significantly since 2011, although the TCGA project was launched in 2005. Additionally, we found that the critical areas of focus in the studies that use the TCGA data were glioblastoma multiforme, lung cancer, and breast cancer; meanwhile, data from the RNA-sequencing (RNA-seq) platform is the most preferable for use.

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Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data

Cited by 528 publications

References 30 publications

BioWes – From Design of Experiment, through Protocol to Repository, Control, Standardization and Back-Tracking

BioWes – From Design of Experiment, through Protocol to Repository, Control, Standardization and Back-Tracking

Initiative for standardization of reporting genetics of male infertility

Identifying Scientific Project-generated Data Citation from Full-text Articles: An Investigation of TCGA Data Citation

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