• Journal of the Korean Data and Information Science Society
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• 제28권6호
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• pp.1279-1290
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• 2017

R의 {shiny} 패키지는 R 스크립트만으로 웹 어플리케이션을 제작할 수 있는 환경을 제공한다. Shiny는 별도의 웹 프로그래밍 언어에 대한 지식을 요구하지 않으며 그 개발이 매우 쉽고 간명하다. 또한 Shiny는 다양한 확장성을 가지고 있으며, 그 기능이 날로 확대되고 있다. 따라서 완성도 높은 결과물의 제시가 절실한 R 기반의 분석전문가들에게는 더 없이 훌륭한 도구이다. 본 논문에서는 Shiny를 활용하여 대용량 데이터를 분석한 실제 사례를 소개한다. 먼저, 공간 자료와 관계된 분석으로 등고선 등의 형태로 표현되는 지형자료를 분석하여 지질 이상대를 추출한다. 다음으로, 기상, 환경, 소셜미디어 정보를 이용하여 전국의 16개 시, 도별 주요 질환을 예측하는 모형을 구축한다. 이 과정에서 Shiny가 데이터의 시각화와 분석에 매우 효과적임을 보이고자 한다.

• Genomics & Informatics
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• 제15권4호
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• pp.178-182
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• 2017

Next-generation sequencing (NGS) technology has become a trend in the genomics research area. There are many software programs and automated pipelines to analyze NGS data, which can ease the pain for traditional scientists who are not familiar with computer programming. However, downstream analyses, such as finding differentially expressed genes or visualizing linkage disequilibrium maps and genome-wide association study (GWAS) data, still remain a challenge. Here, we introduce a dockerized web application written in R using the Shiny platform to visualize pre-analyzed RNA sequencing and GWAS data. In addition, we have integrated a genome browser based on the JBrowse platform and an automated intermediate parsing process required for custom track construction, so that users can easily build and navigate their personal genome tracks with in-house datasets. This application will help scientists perform series of downstream analyses and obtain a more integrative understanding about various types of genomic data by interactively visualizing them with customizable options.

• Genomics & Informatics
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• 제16권4호
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• pp.36.1-36.3
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• 2018

Next generation sequencing (NGS), a high-throughput DNA sequencing technology, is widely used for molecular biological studies. In NGS, RNA-sequencing (RNA-Seq), which is a short-read massively parallel sequencing, is a major quantitative transcriptome tool for different transcriptome studies. To utilize the RNA-Seq data, various quantification and analysis methods have been developed to solve specific research goals, including identification of differentially expressed genes and detection of novel transcripts. Because of the accumulation of RNA-Seq data in the public databases, there is a demand for integrative analysis. However, the available RNA-Seq data are stored in different formats such as read count, transcripts per million, and fragments per kilobase million. This hinders the integrative analysis of the RNA-Seq data. To solve this problem, we have developed a web-based application using Shiny, COEX-seq (Convert a Variety of Measurements of Gene Expression in RNA-Seq) that easily converts data in a variety of measurement formats of gene expression used in most bioinformatic tools for RNA-Seq. It provides a workflow that includes loading data set, selecting measurement formats of gene expression, and identifying gene names. COEX-seq is freely available for academic purposes and can be run on Windows, Mac OS, and Linux operating systems. Source code, sample data sets, and supplementary documentation are available as well.