• Journal of Genetic Medicine
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• v.5 no.1
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• pp.15-20
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• 2008

Purpose : Large exon deletions in the DMD gene are found in about 60% of DMD/BMD patients. Multiplex PCR has been employed to detect the deletion mutation, which frequently generates noise PCR products due to the presence of multiple primers in a single reaction as well as the stringency of PCR conditions. This often leads to a false-negative or false-positive result. To address this problematic issue, we introduced the dual primer oligonucleotide (DPO) system. DPO contains two separate priming regions joined by a polydeoxyinosine linker that results in high PCR specificity even under suboptimal PCR conditions. Methods : We tested 50 healthy male controls, 50 patients with deletion mutation as deletion-positive patient controls, and 20 patients with no deletions as deletion-negative patient controls using DPO-multiplex PCR. Both the presence and extent of deletion were verified by simplex PCR spanning the promoter region (PM) and 18 exons including exons 3, 4, 6, 8, 12, 13, 17, 19, 43-48, 50-52, and 60 in all 120 controls. Results : DPO-multiplex PCR showed 100% sensitivity and specificity for the detection a deletion. However, it showed 97.1% sensitivity and 100% specificity for determining the extent of deletions. Conclusion : The DPO-multiplex PCR method is a useful molecular test to detect large deletions of DMD for the diagnosis of patients with DMD/BMD because it is easy to perform, fast, and cost-effective and has excellent sensitivity and specificity.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.