• 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.

• The Korean Journal of Physiology and Pharmacology
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• 제28권5호
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• pp.403-411
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• 2024

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools. RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNA-seq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks.

• 대한임상검사과학회지
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• 제55권4호
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• pp.235-243
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• 2023

차세대 염기서열 분석이 개발되고 널리 사용됨에 따라 RNA-시퀀싱(RNA-sequencing, RNA-seq)이 글로벌 전사체 프로파일링을 검증하기 위한 도구의 첫번째 선택으로 급부상하게 되었다. RNA-seq의 상당한 발전으로 다양한 유형의 RNA-seq가 생물정보학(bioinformatics) 발전과 함께 진화했으나, 다양한 RNA-seq 기법 및 생물정보학에 대한 전반적인 이해 없이는 RNA-seq의 복잡한 데이터를 해석하여 생물학적 의미를 도출하기는 어렵다. 이와 관련하여 본 리뷰에서는 RNA-seq의 두 가지 주요 섹션을 논의하고 있다. 첫째, Standard RNA-seq과 주요하게 자주 사용되는 두 가지 RNA-seq variant method를 비교하였다. 이 비교는 어떤 RNA-seq 방법이 연구 목적에 가장 적절한지에 대한 시사점을 제공한다. 둘째, 가장 널리 사용되는 RNA-seq에서 생성된 데이터 분석; (1) 탐색적 자료 분석 및 (2) enriched pathway 분석에 대해 논의하였다. 데이터 세트의 전반적인 추세를 제공할 수 있는 주 성분 분석, Heatmap 및 Volcano plot과 같이 RNA-seq에 대해 가장 널리 사용되는 탐색적 자료 분석을 소개하였다. Enriched pathway 분석 섹션에서는 3가지 세대의 enriched pathway 분석에 대해 소개하고 각 세대가 어떤 식으로 RNA-seq 데이터 세트로부터 enriched pathway를 도출하는지를 소개하였다.

• Genomics & Informatics
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• 제13권4호
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• pp.119-125
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• 2015

RNA is a polymeric molecule implicated in various biological processes, such as the coding, decoding, regulation, and expression of genes. Numerous studies have examined RNA features using whole transcriptome sequencing (RNA-seq) approaches. RNA-seq is a powerful technique for characterizing and quantifying the transcriptome and accelerates the development of bioinformatics software. In this review, we introduce routine RNA-seq workflow together with related software, focusing particularly on transcriptome reconstruction and expression quantification.

• 한국정보통신학회논문지
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• 제25권11호
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• pp.1512-1518
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• 2021

단일세포 RNA-Seq 은 개별 세포의 유전자 발현을 제공하므로 세포마다 차등적인 고해상도 정보를 준다. 단일세포 RNA-Seq 자료에 대하여 군집화는 세포의 유형과 고수준의 생물 과정을 이해하기 위하여 수행된다. 매우 고차원이고 대용량인 단일세포 RNA-Seq을 효과적으로 처리하기 위하여, 본 논문은 변이 자동인코더를 사용하여 고차원의 자료공간을 저차원의 잠재공간으로 변환하여, 보다 정확한 군집화를 수행할 수 있는 특징공간을 만든다. 차원이 축소된 잠재공간에 다양한 군집화 방법을 적용하는 접근을 다양한 전통적인 단일세포 RNA-Seq 군집화 방법과 성능을 비교하였다. 군집화 실험을 통하여, 제안한 방법은 기존 방법들보다 다양한 군집화 성능기준에서 성능이 개선되었다.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.31-36
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• 2021

RNA sequencing (RNA-Seq) is a technology that facilitates transcriptome analysis using next-generation sequencing (NSG) tools. Information on the quantity and sequences of RNA is vital to relate our genomes to functional protein expression. RNA-Seq data are characterized as being high-dimensional in that the number of variables (i.e., transcripts) far exceeds the number of observations (e.g., experiments). Given the wide range of dimensionality reduction techniques, it is not clear which is best for RNA-Seq data analysis. In this paper, we study the effect of three dimensionality reduction techniques to improve the classification of the RNA-Seq dataset. In particular, we use PCA, SVD, and SOM to obtain a reduced feature space. We built nine classification models for a cancer dataset and compared their performance. Our experimental results indicate that better classification performance is obtained with PCA and SOM. Overall, the combinations PCA+KNN, SOM+RF, and SOM+KNN produce preferred results.

• Molecules and Cells
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• 제46권2호
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• pp.106-119
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• 2023

With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.

• Tuberculosis and Respiratory Diseases
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• 제79권2호
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• pp.85-90
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• 2016

Background: Lung cancer is the most common cause of cancer related death. Alterations in gene sequence, structure, and expression have an important role in the pathogenesis of lung cancer. Fusion genes and alternative splicing of cancer-related genes have the potential to be oncogenic. In the current study, we performed RNA-sequencing (RNA-seq) to investigate potential fusion genes and alternative splicing in non-small cell lung cancer. Methods: RNA was isolated from lung tissues obtained from 86 subjects with lung cancer. The RNA samples from lung cancer and normal tissues were processed with RNA-seq using the HiSeq 2000 system. Fusion genes were evaluated using Defuse and ChimeraScan. Candidate fusion transcripts were validated by Sanger sequencing. Alternative splicing was analyzed using multivariate analysis of transcript sequencing and validated using quantitative real time polymerase chain reaction. Results: RNA-seq data identified oncogenic fusion genes EML4-ALK and SLC34A2-ROS1 in three of 86 normal-cancer paired samples. Nine distinct fusion transcripts were selected using DeFuse and ChimeraScan; of which, four fusion transcripts were validated by Sanger sequencing. In 33 squamous cell carcinoma, 29 tumor specific skipped exon events and six mutually exclusive exon events were identified. ITGB4 and PYCR1 were top genes that showed significant tumor specific splice variants. Conclusion: In conclusion, RNA-seq data identified novel potential fusion transcripts and splice variants. Further evaluation of their functional significance in the pathogenesis of lung cancer is required.

• 응용통계연구
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• 제33권4호
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• pp.511-526
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• 2020

단세포 RNA 시퀀싱 데이터(single-cell RNA-sequencing data, 이하 단세포 RNA 데이터)는 세포 조직으로부터 추출한 각 단세포 별 유전자의 신호를 기록한 데이터로, 세포 간의 이질성을 파악하는 것을 주요 목적으로 한다. 그러나 단세포 RNA 데이터는 샘플링 및 기술적인 한계로 인해 결측비율이 높고, 노이즈가 크다. 이러한 이유 때문에 기존의 군집화 방법을 적용하는 데에 한계가 존재한다. 본 논문에서는 단세포 RNA 데이터 분석에서 모티브를 얻어 스펙트럼 군집화(spectral clustering) 기반의 방법을 제안한다. 특히 유사도 행렬(similarity matrix) 계산에서 유전자 별로 가중치를 부여하여 기존의 단세포 데이터 분석 방법과 차별화하였다. 제안하는 군집화 방법은 유전자별 가중치를 부여함과 동시에 세포를 군집화한다. 군집화는 반복 알고리즘을 통해 제안하는 비볼록식(non-convex optimization)을 풀어 진행한다. 또한 실데이터 적용과 시뮬레이션을 통해 제안하는 군집화 방법이 기존의 방법보다 군집을 잘 구분하는 것을 보인다.

• Molecules and Cells
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• 제42권3호
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• pp.189-199
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• 2019

Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability. In this review, we provide an overview of scRNA-seq protocols, computational approaches for dissecting cellular heterogeneity, and future directions of single-cell transcriptomic analysis.