• 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.

• 식품과학과 산업
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• 제52권3호
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• pp.220-228
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• 2019

Rapid development of next-generation sequencing (NGS) technology is available to study microbes in genomic level. This NGS has been widely used in DNA/RNA sequencing for genome sequencing, metagenomics, and transcriptomics. The food microbiology area could be categorized into three groups. Food microbes including probiotics and food-borne pathogens are studied in genomic level using NGS for microbial genomics. While food fermentation or food spoilage are more complicated, their genomic study needs to be done with metagenomics using NGS for compositional analysis. Furthermore, because microbial response in food environments are also important to understand their roles in food fermentation or spoilage, pattern analysis of RNA expression in the specific food microbe is conducted using RNA-Seq. These microbial genomics, metagenomics, and transcriptomics for food fermentation and spoilage would extend our knowledge on effective utilization of fermenting bacteria for health promotion as well as efficient control of food-borne pathogens for food safety.

• Journal of Microbiology and Biotechnology
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• 제32권2호
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• pp.149-159
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• 2022

Cancers of the lung and liver are the top 10 leading causes of cancer death worldwide. Thus, it is essential to identify the genes specifically expressed in these two cancer types to develop new therapeutics. Although many messenger RNA (mRNA) sequencing data related to these cancer cells are available due to the advancement of next-generation sequencing (NGS) technologies, optimized data processing methods need to be developed to identify the novel cancer-specific genes. Here, we conducted an analytical comparison between Bowtie2, a Burrows-Wheeler transform-based alignment tool, and Kallisto, which adopts pseudo alignment based on a transcriptome de Bruijn graph using mRNA sequencing data on normal cells and lung/liver cancer tissues. Before using cancer data, simulated mRNA sequencing reads were generated, and the high Transcripts Per Million (TPM) values were compared. mRNA sequencing reads data on lung/liver cancer cells were also extracted and quantified. While Kallisto could directly give the output in TPM values, Bowtie2 provided the counts. Thus, TPM values were calculated by processing the Sequence Alignment Map (SAM) file in R using package Rsubread and subsequently in python. The analysis of the simulated sequencing data revealed that Kallisto could detect more transcripts and had a higher overlap over Bowtie2. The evaluation of these two data processing methods using the known lung cancer biomarkers concludes that in standard settings without any dedicated quality control, Kallisto is more effective at producing faster and more accurate results than Bowtie2. Such conclusions were also drawn and confirmed with the known biomarkers specific to liver cancer.

• Journal of Genetic Medicine
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• 제20권1호
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• pp.1-5
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• 2023

Until now, rare disease studies have mainly been carried out by detecting simple variants such as single nucleotide substitutions and short insertions and deletions in protein-coding regions of disease-associated gene panels using diagnostic next-generation sequencing in association with patient phenotypes. However, several recent studies reported that the detection rate hardly exceeds 50% even when whole-exome sequencing is applied. Therefore, the necessity of introducing whole-genome sequencing is emerging to discover more diverse genomic variants and examine their association with rare diseases. When no diagnosis is provided by whole-genome sequencing, additional omics techniques such as RNA-seq also can be considered to further interrogate causal variants. This paper will introduce a description of these multi-omics techniques and their applications in rare disease studies.

• Korean Circulation Journal
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• 제53권1호
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• pp.1-16
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• 2023

Recently, single cell RNA sequencing (scRNA-seq) technology has enabled the discovery of novel or rare subtypes of cells and their characteristics. This technique has advanced unprecedented biomedical research by enabling the profiling and analysis of the transcriptomes of single cells at high resolution and throughput. Thus, scRNA-seq has contributed to recent advances in cardiovascular research by the generation of cell atlases of heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and diseases. This review summarizes the overall workflow of the scRNA-seq technique itself and key findings in the cardiovascular development and diseases based on the previous studies. In particular, we focused on how the single-cell sequencing technology can be utilized in clinical field and precision medicine to treat specific diseases.

• Environmental Engineering Research
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• 제14권1호
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• pp.3-9
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• 2009

Microorganisms play an important role in the geochemical cycles, industry, environmental cleanup, and biotechnology among other fields. Given the high microbial diversity, identification of the microorganism is essential in understanding and managing the processes. One of the most popular and powerful method for microbial identification is comparative 16S rRNA gene analysis. Due to the highly conserved nature of this essential gene, sequencing and later comparison of it against known rRNA databases can provide assignment of the bacteria into the taxonomy, and the identity of its closest relatives. Isolation and sequencing of 16S rRNA genes directly from natural environments (either from DNA or RNA) can also be used to study the structure of the whole microbial community. Nowadays, novel sequencing technologies with massive outputs are giving researchers worldwide the chance to study the microbial world with a depth that was previously too expensive to achieve. In this article we describe commonly used research approaches for the study of individual microorganisms and microbial communities using the tools provided by Ribosomal Database Project website.

• Genomics & Informatics
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• 제15권1호
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• pp.51-53
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• 2017

High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specific aspects of the data analysis pipeline, and are difficult to appropriately integrate with one another due to their disparate data structures and processing methods. They also lack visualization methods to confirm the integrity of the data and the process. In this paper, we propose an R-based RNA-Seq analysis pipeline called TRAPR, an integrated tool that facilitates the statistical analysis and visualization of RNA-Seq expression data. TRAPR provides various functions for data management, the filtering of low-quality data, normalization, transformation, statistical analysis, data visualization, and result visualization that allow researchers to build customized analysis pipelines.

• 생명과학회지
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• 제30권6호
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• pp.501-512
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• 2020

제주조릿대 잎은 항염, 해열 및 이뇨작용을 가지고 있어 위궤양, 목마름 및 토혈 치료를 위한 민간의약으로 사용되어 왔다. 본 저자들은 제주조리대 잎에서 분리한 피토케미칼 풍부 추출물(PRE)과 그 에틸아세테이트 분획물(EPRE)은 여러 위암 세포주에서 세포사멸을 유도하는 항암 효과가 있다고 보고한 바 있다. 본 연구는 EPRE의 세포사멸 유도 기전에 관여하는 분자표적들을 탐색하기 위하여 EPRE을 처리한 SNU-16 세포에서 mRNA와 microRNA (miRNA)의 프로파일 변화를 분석하였다. RNA sequencing 분석을 통해 총 2,875개의 차등적으로 발현되는 유전자들(DEGs)을 동정하였다. 유전자 온톨로지(GO)와 KEGG 경로 분석 결과, EPRE는 세포사멸, 유사 분열-활성화 단백질 키나제(MAPK) 및 염증 반응, 종양 괴사 인자(TNF) 신호 전달 및 암 경로에 관여하는 유전자들의 발현을 조절하는 것으로 나타났다. 단백질-단백질 상호 작용(PPI) 네트워크 분석으로 세포사멸 및 세포죽음과 관련된 유전자들 간의 상호작용들을 확인할 수 있었다. 그리고, miRNA sequencing 분석을 통해 총 27개의 차별적으로 발현되는 miRNAs (DEMs)를 동정하였다. GO와 KEGG 경로 분석 결과, EPRE는 세포주기, 세포사멸 및 tropomyosin-receptor-kinase (TRK) 수용체 신호 전달, 성장인자-β(TGF-β), 핵인자 κB (NF-κB) 및 암 경로에 관여하는 miRNAs의 발현을 조정하였다. 본 연구결과는 EPRE의 항암 효과의 근본적인 메커니즘에 대한 통찰력을 제공한다.

• 한국축산시설환경학회지
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• 제21권3호
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• pp.93-98
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• 2015

The objective of this study was to review application of next-generation sequencing (NGS) to investigate microbiome in the livestock sector. Since the 16S rRNA gene is used as a phylogenetic marker, unculturable members of microbiome in nature or managed environments have been investigated using the NGS technique based on 16S rRNA genes. However, few NGS studies have been conducted to investigate microbiome in the livestock sector. The 16S rRNA gene sequences obtained from NGS are classified to microbial taxa against the 16S rRNA gene reference database such as RDP, Greengenes and Silva databases. The sequences also are clustered into species-level OTUs at 97% sequence similarity. Microbiome similarity among treatment groups is visualized using principal coordinates analysis, while microbiome shared among treatment groups is visualized using a venn diagram. The use of the NGS technique will contribute to elucidating roles of microbiome in the livestock sector.

• Molecules and Cells
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• 제44권3호
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• pp.127-135
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• 2021

Since the introduction of RNA sequencing (RNA-seq) as a high-throughput mRNA expression analysis tool, this procedure has been increasingly implemented to identify cell-level transcriptome changes in a myriad of model systems. However, early methods processed cell samples in bulk, and therefore the unique transcriptomic patterns of individual cells would be lost due to data averaging. Nonetheless, the recent and continuous development of new single-cell RNA sequencing (scRNA-seq) toolkits has enabled researchers to compare transcriptomes at a single-cell resolution, thus facilitating the analysis of individual cellular features and a deeper understanding of cellular functions. Nonetheless, the rapid evolution of high throughput single-cell "omics" tools has created the need for effective hypothesis verification strategies. Particularly, this issue could be addressed by coupling cell engineering techniques with single-cell sequencing. This approach has been successfully employed to gain further insights into disease pathogenesis and the dynamics of differentiation trajectories. Therefore, this review will discuss the current status of cell engineering toolkits and their contributions to single-cell and genome-wide data collection and analyses.