ONETOOL for the analysis of family-based big data.
Ontology highlight
ABSTRACT:
SUBMITTER: Song YE
PROVIDER: S-EPMC6084591 | biostudies-literature | 2018 Aug
REPOSITORIES: biostudies-literature
Publications
ONETOOL for the analysis of family-based big data.
Song Yeunjoo E YE Lee Sungyoung S Park Kyungtaek K Elston Robert C RC Yang Hyeon-Jong HJ Won Sungho S
Bioinformatics (Oxford, England) 20180801 16
<h4>Motivation</h4>Despite the need for separate tools to analyze family-based data, there are only a handful of tools optimized for family-based big data compared to the number of tools available for analyzing population-based data.<h4>Results</h4>ONETOOL implements the properties of well-known existing family data analysis tools and recently developed methods in a computationally efficient manner, and so is suitable for analyzing the vast amount of variant data available from sequencing family ...[more]
Similar Datasets
Project description:BackgroundWhole-genome sequencing (WGS) and whole-exome sequencing (WES) technologies are increasingly used to identify disease-contributing mutations in human genomic studies. It can be a significant challenge to process such data, especially when a large family or cohort is sequenced. Our objective was to develop a big data toolset to efficiently manipulate genome-wide variants, functional annotations and coverage, together with conducting family based sequencing data analysis.MethodsHadoop is a framework for reliable, scalable, distributed processing of large data sets using MapReduce programming models. Based on Hadoop and HBase, we developed SeqHBase, a big data-based toolset for analysing family based sequencing data to detect de novo, inherited homozygous, or compound heterozygous mutations that may contribute to disease manifestations. SeqHBase takes as input BAM files (for coverage at every site), variant call format (VCF) files (for variant calls) and functional annotations (for variant prioritisation).ResultsWe applied SeqHBase to a 5-member nuclear family and a 10-member 3-generation family with WGS data, as well as a 4-member nuclear family with WES data. Analysis times were almost linearly scalable with number of data nodes. With 20 data nodes, SeqHBase took about 5 secs to analyse WES familial data and approximately 1 min to analyse WGS familial data.ConclusionsThese results demonstrate SeqHBase's high efficiency and scalability, which is necessary as WGS and WES are rapidly becoming standard methods to study the genetics of familial disorders.
| S-EPMC4382803 | biostudies-literature
Project description:Medical data processing and analytics exert significant influence in furnishing dependable decision support for prospective biomedical applications. Given the sensitive nature of medical data, specialized techniques and frameworks tailored for application-centric processing are imperative. This article presents a conceptualization for the analysis and uniformitarian of datasets through the implementation of Federated Learning (FL). The realm of medical big data stems from diverse origins, necessitating the delineation of data provenance and attribute paradigms to facilitate feature extraction and dependency assessment. The architecture governing the data collection framework is intricately linked to remote data transmission, thereby engendering efficient customization oversight. The operational methodology unfolds across four strata: the data origin layer, data acquisition layer, data classification layer, and data optimization layer. Central to this endeavor are multi-objective optimal datasets (MooM), characterized by attribute-driven feature cartography and cluster categorization through the conduit of federated learning models. The orchestration of feature synchronization and parameter extraction transpires across multiple tiers of neural networking, culminating in the provisioning of a steadfast remedy through dataset standardization and labeling. The empirical findings reflect the efficacy of the proposed technique, boasting an impressive 97.34% accuracy rate in the disentanglement and clustering of telemedicine data, facilitated by the operational servers within the ambit of the federated model.
| S-EPMC10550167 | biostudies-literature
Project description:PurposeTo systematically compare and rank ocular measurements with optical and ultrasound biometers based on big data.MethodsPubMed, Embase, the Cochrane Library and the US trial registry ( www.Clinicaltrialgov ) were used to systematically search trials published up to October 22nd, 2020. We included comparative studies reporting the following parameters measured by at least two devices: axial length (AL), flattest meridian keratometry (Kf), steepest meridian keratometry (Ks), mean keratometry (Km), astigmatism (AST), astigmatism vectors J0 and J45, anterior chamber depth (ACD), aqueous depth (AQD), central corneal thickness (CCT), corneal diameter (CD) and lens thickness (LT). A network-based big data analysis was conducted using STATA version 13.1.ResultsAcross 129 studies involving 17,181 eyes, 12 optical biometers and two ultrasound biometers (with both contact and immersion techniques) were identified. A network meta-analysis for AL and ACD measurements found that statistically significant differences existed when contact ultrasound biometry was compared with the optical biometers. There were no statistically significant differences among the four swept-source optical coherence tomography (SS-OCT) based devices (IOLMaster 700, OA-2000, Argos and ANTERION). As for Ks, Km and CD, statistically significant differences were found when the Pentacam AXL was compared with the IOLMaster and IOLMaster 500. There were statistically significant differences for CCT when the OA-2000 was compared to Pentacam AXL, IOLMaster 700, Lenstar, AL-Scan and Galilei G6.ConclusionFor AL and ACD, contact ultrasound biometry obtains the lower values compared with optical biometers. The Pentacam AXL achieves the lowest values for keratometry and CD. The smallest value for CCT measurement is found with the OA-2000.
| S-EPMC9808957 | biostudies-literature
Project description:Preventing exacerbation in chronic obstructive pulmonary disease (COPD) patients is crucial, but requires identification of the exacerbating factors. To date, no integrated analysis of patient-derived and external factors has been reported. To identify factors associated with COPD exacerbation, we collected data, including smoking status, lung function, and COPD assessment test scores, from 594 COPD patients in the Korean COPD subgroup study (KOCOSS), and merged these data with patients' Korean Health Insurance Review and Assessment Service data for 2007-2012. We also collected primary weather variables, including levels of particulate matter <10 microns in diameter, daily minimum ambient temperature, as well as respiratory virus activities, and the logs of web queries on COPD-related issues. We then assessed the associations between these patient-derived and external factors and COPD exacerbations. Univariate analysis showed that patient factors, air pollution, various types of viruses, temperature, and the number of COPD-related web queries were associated with COPD exacerbation. Multivariate analysis revealed that the number of exacerbations in the preceding year, female sex, COPD grade, and influenza virus detection rate, and lowest temperature showed significant association with exacerbation. Our findings may help COPD patients predict when exacerbations are likely, and provide intervention as early as possible.
| S-EPMC6491439 | biostudies-literature
Project description:The increasing complexity of data and analysis methods has created an environment where scientists, who may not have formal training, are finding themselves playing the impromptu role of software engineer. While several resources are available for introducing scientists to the basics of programming, researchers have been left with little guidance on approaches needed to advance to the next level for the development of robust, large-scale data analysis tools that are amenable to integration into workflow management systems, tools, and frameworks. The integration into such workflow systems necessitates additional requirements on computational tools, such as adherence to standard conventions for robustness, data input, output, logging, and flow control. Here we provide a set of 10 guidelines to steer the creation of command-line computational tools that are usable, reliable, extensible, and in line with standards of modern coding practices.
| S-EPMC6532757 | biostudies-literature
Project description:This paper aims to understand the characteristics of domestic big data jobs requirements through k-means text clustering, help enterprises, and employees to identify big data talents, and promote the further development of big data-related research. Firstly, the crawler software is used to crawl the recruitment information about "big data" on the zhaopin.com recruitment website. Then, Jieba word segmentation and K-means text clustering are used to cluster big data recruitment positions, and the number of clustering was determined by the average sum of squares within the group. Finally, big data jobs are divided into 10 categories, and the urban distribution, salary level, education requirements, and experience requirements of big data jobs are discussed and analyzed from the perspectives of the overall data set and clustering results, to clarify the characteristics of big data job demands. The analysis results show that the job demands of big data are mainly distributed in first-tier cities and new first-tier cities. Enterprises are more inclined to job seekers with a college degree or bachelor's degree and more than one year's relevant experience. There are wage differences among different types of jobs. The higher the position, the higher the requirement for education and experience will be.
| S-EPMC8341572 | biostudies-literature
Project description:Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality. The purpose of this paper is to open a discourse on the potential for modern decision theoretic optimal experimental design methods, which by their very nature have traditionally been applied prospectively, to improve the analysis of Big Data through retrospective designed sampling in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has the potential for wide generality and advantageous inferential and computational properties. We highlight current hurdles and open research questions surrounding efficient computational optimisation in using retrospective designs, and in part this paper is a call to the optimisation and experimental design communities to work together in the field of Big Data analysis.
| S-EPMC5584669 | biostudies-literature
Project description:The term "big data" refers broadly to large volumes of data, often gathered from several sources, that are then analyzed, for example, for predictive analytics. Combining and mining genetic data from varied sources including clinical genetic testing, for example, electronic health records, what might be termed as "recreational" genetic testing such as ancestry testing, as well as research studies, provide one type of "big data." Challenges and cautions in analyzing big data include recognizing the lack of systematic collection of the source data, the variety of assay technologies used, the potential variation in classification and interpretation of genetic variants. While advanced technologies such as microarrays and, more recently, next-generation sequencing, that enable testing an individual's DNA for thousands of genes and variants simultaneously are briefly discussed, attention is focused more closely on challenges to analysis of the massive data generated by these genomic technologies. The main theme of this review is to evaluate challenges associated with big data in general and specifically to bring the sophisticated technology of genetic/genomic testing down to the individual level, keeping in mind the human aspect of the data source and considering where the impact of the data will be translated and applied. Considerations in this "humanizing" process include providing adequate counseling and consent for genetic testing in all settings, as well as understanding the strengths and limitations of assays and their interpretation.
| S-EPMC7082327 | biostudies-literature
Project description:With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.
| S-EPMC4090507 | biostudies-literature
Project description:The exponential accumulation of environmental and ecological data together with the adoption of open data initiatives bring opportunities and challenges for integrating and synthesising relevant knowledge that need to be addressed, given the ongoing environmental crises. Here we present Biospytial, a modular open source knowledge engine designed to import, organise, analyse and visualise big spatial ecological datasets using the power of graph theory. The engine uses a hybrid graph-relational approach to store and access information. A graph data structure uses linkage relationships to build semantic structures represented as complex data structures stored in a graph database, while tabular and geospatial data are stored in an efficient spatial relational database system. We provide an application using information on species occurrences, their taxonomic classification and climatic datasets. We built a knowledge graph of the Tree of Life embedded in an environmental and geographical grid to perform an analysis on threatened species co-occurring with jaguars (Panthera onca). The Biospytial approach reduces the complexity of joining datasets using multiple tabular relations, while its scalable design eases the problem of merging datasets from different sources. Its modular design makes it possible to distribute several instances simultaneously, allowing fast and efficient handling of big ecological datasets. The provided example demonstrates the engine's capabilities in performing basic graph manipulation, analysis and visualizations of taxonomic groups co-occurring in space. The example shows potential avenues for performing novel ecological analyses, biodiversity syntheses and species distribution models aided by a network of taxonomic and spatial relationships.
| S-EPMC7213554 | biostudies-literature