An experimental metagenome data management and analysis system

IMG/M is a data management and analysis system for microbial community genomes (metagenomes) hosted at the Department of Energy's (DOE) Joint Genome Institute (JGI). IMG/M consists of metagenome data integrated with isolate microbial genomes from the Integrated Microbial Genomes (IMG) system. IMG/M provides IMG's comparative data analysis tools extended to handle metagenome data, together with metagenome-specific analysis tools. IMG/M is available at http://img.jgi.doe.gov/m

본 연구에서는 유역 수문자료를 효율적으로 관리할 수 있는 수문자료관리시스템(HDMS)을 개발하였으며 경기도 화성시에 위치한 발안시험유역을 대상으로 시스템의 적용성을 보여주었다. MySQL 5.0을 사용하여 관계형 데이테베이스를 설계하였으며, MS-Visual Basic 6.0을 이용하여 윈도우 기반의 시스템을 개발하였다. HDMS는 자료검색시스템, 자료관리시스템, 자료분석시스템으로 구성되었다. 자료검색시스템과 자료관리시스템은 자료검색, 자료저장, 자료갱신, 자료추출의 기능을 제공하며, 자료분석시스템은 저장된 자료로부터 다양한 수문통계정보를 제공하며, 자료의 정확성 및 품질을 분석하고 평가한다. In this paper, the Hydrologic Data Management System (HDMS) was developed for the efficient management of hydrologic data. The applicability of the system was demonstrated using the hydrologic data of study watershed located in the southwest from Suwon city MySQL 5.0, relational database management system, and MS Visual Basic 6.0 were used for the development of MS windows based HDMS. The primary components of the HDMS are data search system, data management system, and data analysis system. Data search and management systems provide basic functions for the efficient data search, storage, update and export. Data analysis system enables the users to get the further and diverse hydrologic statistical information from the stored data. Furthermore, the accuracy and quality of hydrologic data was analyzed and evaluated through data analysis system.

Survey of computer-assisted electrocardiography in the United States

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

The analysis of expressed sequence tag (EST) datasets offers a rapid and cost-effective approach to elucidate the transcriptome of an organism, but requiring several computational methods for assembly and annotation. ESTExplorer is a comprehensive workflow system for EST data management and analysis. The pipeline uses a ‘distributed control approach’ in which the most appropriate bioinformatics tools are implemented over different dedicated processors. Species-specific repeat masking and conceptual translation are in-built. ESTExplorer accepts a set of ESTs in FASTA format which can be analysed using programs selected by the user. After pre-processing and assembly, the dataset is annotated at the nucleotide and protein levels, following conceptual translation. Users may optionally provide ESTExplorer with assembled contigs for annotation purposes. Functionally annotated contigs/ESTs can be analysed individually. The overall outputs are gene ontologies, protein functional identifications in terms of mapping to protein domains and metabolic pathways. ESTExplorer has been applied successfully to annotate large EST datasets from parasitic nematodes and to identify novel genes as potential targets for parasite intervention. ESTExplorer runs on a Linux cluster and is freely available for the academic community at http://estexplorer.biolinfo.org.

The core idea of quality work is to create enduring quality by providing users with reliable, easy-to-use, and surprising products. In the Internet+ era, user demand for quality has changed with the times. Customers are no longer satisfied with the requirements of basic quality functions, and more in pursuit of ease of use, pleasure and even identity. Even if the product has "satisfied with the specifications" in quality, users are still dissatisfied and express their dissatisfaction through the Internet. If we can use the Internet, big data, artificial intelligence and other technologies to collect and analyze user expectations or dissatisfaction with product quality and find improvement directions, then quality management will see great progress. Based on this, we can create a new type of quality big data analysis and management system, which we can call the Product Satisfaction Index (PSI) management system. Through the analysis and transformation of PSI, the customer’s textual expectations are turned into numerical quantifiable indicators. Through the management of PSI indicators, quality big data mining and analysis are performed to improve the product to meet customer expectations. In advancing the product satisfaction index (PSI) indicator management system, we should fully implement the definition of PSI indicators, strategic position, data analysis methods, closed-loop management models, review and continuous improvement, and data platforms. Visualize customer needs, analyze the matching between product characteristics and customer needs, and promote continuous improvement of PSI indicators through closed-loop management. The new PSI index management method opens up new ideas and methods for quality performance management, and helps build high-quality development and create customer satisfaction.

The metagenomic method directly sequences and analyses genome information from microbial communities. The main computational tasks for metagenomic analyses include taxonomical and functional structure analysis for all genomes in a microbial community (also referred to as a metagenomic sample). With the advancement of Next Generation Sequencing (NGS) techniques, the number of metagenomic samples and the data size for each sample are increasing rapidly. Current metagenomic analysis is both data- and computation- intensive, especially when there are many species in a metagenomic sample, and each has a large number of sequences. As such, metagenomic analyses require extensive computational power. The increasing analytical requirements further augment the challenges for computation analysis. In this work, we have proposed Parallel-META 2.0, a metagenomic analysis software package, to cope with such needs for efficient and fast analyses of taxonomical and functional structures for microbial communities. Parallel-META 2.0 is an extended and improved version of Parallel-META 1.0, which enhances the taxonomical analysis using multiple databases, improves computation efficiency by optimized parallel computing, and supports interactive visualization of results in multiple views. Furthermore, it enables functional analysis for metagenomic samples including short-reads assembly, gene prediction and functional annotation. Therefore, it could provide accurate taxonomical and functional analyses of the metagenomic samples in high-throughput manner and on large scale.

With the development of information technology, information systems have been widely used in medical institutions, and more and more clinical research data has been digitized, which provides the possibility to carry out clinical research with data as the source. However, the complexity and multi-dimensionality of clinical data make medical scientists' progress slow, and comprehensive use of various big data technologies is needed to help improve the efficiency of clinical research. Visualization technology can display data in an intuitive and easy-to-read way, helping medical researchers understand data, while parallel computing can greatly improve computing efficiency. Therefore, this paper explores the application strategies of data analysis technology and visualization technology in the management and analysis of clinical research data, and builds a set of clinical research data management analysis system, which combines various technologies to help effectively promote medical clinical.

Simple SummaryNormally, researchers use feces or rectal swabs to characterize a gut microbiome, but because there is significant spatiotemporal variation across different intestinal segments there are marked differences in composition and function of microbiomes among various gut sites. Hence, a consensus has not been reached on the location for sampling for gut microbial metagenome sequencing. This study provides a comparative perspective on gut microbial function that takes into account different sampling resources when conducting a metagenomic sequence analysis, highlighting the differences in the choice of a gut microbiome sampling site, and investigating whether feces and rectal swab samples are efficient proxies for gut microbiome sampling.When conducting metagenomic analysis on gut microbiomes, there is no general consensus concerning the mode of sampling: non-contact (feces), noninvasive (rectal swabs), or cecal. This study aimed to determine the feasibility and comparative merits and disadvantages of using fecal samples or rectal swabs as a proxy for the cecal microbiome. Using broiler as a model, gut microbiomes were obtained from cecal, cloacal, and fecal samples and were characterized according to an analysis of the microbial community, function, and resistome. Cecal samples had higher microbial diversity than feces, while the cecum and cloaca exhibited higher levels of microbial community structure similarity compared with fecal samples. Cecal microbiota possessed higher levels of DNA replicative viability than feces, while fecal microbiota were correlated with increased metabolic activity. When feces were excreted, the abundance of antibiotic resistance genes like tet and ErmG decreased, but some antibiotic genes became more prevalent, such as fexA, tetL, and vatE. Interestingly, Lactobacillus was a dominant bacterial genus in feces that led to differences in microbial community structure, metabolism, and resistome. In conclusion, fecal microbiota have limited potential as a proxy in chicken gut microbial community studies. Thus, feces should be used with caution for characterizing gut microbiomes by metagenomic analysis.

In the past, we have developed and displayed a multiple sclerosis eFolder system for patient data storage, image viewing, and automatic lesion quantification results stored in DICOM-SR format. The web-based system aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient treatments and disease tracking. This year, we have further developed the eFolder system to handle big data analysis and data mining in today’s medical imaging field. The database has been updated to allow data mining and data look-up from DICOM-SR lesion analysis contents. Longitudinal studies are tracked, and any changes in lesion volumes and brain parenchyma volumes are calculated and shown on the webbased user interface as graphical representations. Longitudinal lesion characteristic changes are compared with patients’ disease history, including treatments, symptom progressions, and any other changes in the disease profile. The image viewer is updated such that imaging studies can be viewed side-by-side to allow visual comparisons. We aim to use the web-based medical imaging informatics eFolder system to demonstrate big data analysis in medical imaging, and use the analysis results to predict MS disease trends and patterns in Hispanic and Caucasian populations in our pilot study. The discovery of disease patterns among the two ethnicities is a big data analysis result that will help lead to personalized patient care and treatment planning.

An interactive data management system for river water quality data

Introduction:Portable handheld computers and electronic data management systems have been used for national surveys in many high-income countries, however their use in developing countries has been challenging due to varying geographical, economic, climatic, political and cultural environments. In order to monitor and measure global adult tobacco use, the World Health Organization and the US Centers for Disease Control and Prevention initiated the Global Adult Tobacco Survey, a nationally representative household survey of adults, 15 years of age or older, using a standard core questionnaire, sample design, and data collection and management procedures. The Survey has been conducted in 14 low- and middle-income countries, using an electronic data collection and management system. This paper describes implementation of the electronic data collection system and associated findings.Methods:The Survey was based on a comprehensive data management protocol, to enable standardized, globally comparable high quality data collection and management. It included adaptation to specific country needs, selection of appropriate handheld hardware devices, use of open source software, and building country capacity and provide technical support.Results:In its first phase, the Global Adult Tobacco Survey was successfully conducted between 2008 and 2010, using an electronic data collection and management system for interviews in 302,800 households in 14 countries. More than 2,644 handheld computers were fielded and over 2,634 fieldworkers, supervisors and monitors were trained to use them. Questionnaires were developed and programmed in 38 languages and scripts. The global hardware failure rate was < 1% and data loss was almost 0%.Conclusion:Electronic data collection and management systems can be used effectively for conducting nationally representative surveys, particularly in low- and middle-income countries, irrespective of geographical, climatic, political and cultural environments, and capacity-building at the country level is an important vehicle for Health System Strengthening.

A rapidly increasing number of microbial genomes are sequenced by organizations worldwide and are eventually included into various public genome data resources. The quality of the annotations depends largely on the original dataset providers, with erroneous or incomplete annotations often carried over into the public resources and difficult to correct. We have developed an Expert Review (ER) version of the Integrated Microbial Genomes (IMG) system, with the goal of supporting systematic and efficient revision of microbial genome annotations. IMG ER provides tools for the review and curation of annotations of both new and publicly available microbial genomes within IMG's rich integrated genome framework. New genome datasets are included into IMG ER prior to their public release either with their native annotations or with annotations generated by IMG ER's annotation pipeline. IMG ER tools allow addressing annotation problems detected with IMG's comparative analysis tools, such as genes missed by gene prediction pipelines or genes without an associated function. Over the past year, IMG ER was used for improving the annotations of about 150 microbial genomes.

Integrated data analysis and management for the problem solving environment

In time-domain astronomy, STLF (Short-Timescale and Large Field-of-view) sky survey is the latest way of sky observation. Compared to traditional sky survey who can only find astronomical phenomena, STLF sky survey can even reveal how short astronomical phenomena evolve. The difference does not only lead the new survey data but also the new analysis style. It requires that database behind STLF sky survey should support continuous analysis on data streaming, real-time analysis on short-term data and complex analysis on long-term historical data. In addition, both insertion and query latencies have strict requirements to ensure that scientific phenomena can be discovered. However, the existing databases cannot support our scenario. In this paper, we propose AstroServ, a distributed system for analysis and management of large-scale and full life-cycle astronomical data. AstroServ's core components include three data service layers and a query engine. Each data service layer serves for a specific time period of data and query engine can provide the uniform analysis interface on different data. In addition, we also provide many applications including interactive analysis interface and data mining tool to help scientists efficiently use data. The experimental results show that AstroServ can meet the strict performance requirements and the good recognition accuracy.