• Molecules and Cells
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• v.42 no.3
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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.

• Journal of Genetic Medicine
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• v.20 no.1
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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.

• Genomics & Informatics
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• v.18 no.3
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• pp.26.1-26.6
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• 2020

Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type-specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.

• Journal of Animal Environmental Science
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• v.21 no.3
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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.

• Korean Journal of Microbiology
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• v.24 no.2
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• pp.79-85
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• 1986

We have determined the sequence of $tRNA^{Arg}$ of A. nidulans partially by enzymatic rapid RNA sequencing technique. The sequence was 5'GGCCGGCUGGCCCAAXUGGCAAGGXUCUGAXUACGAAXCAGGAGAUUGCACXXXXXGAGCXXUXXGUCGGUCACCA3' The cloverleaf structure was made from above data. As a result, the anticodon sequence was identified as ACG. This result was confirmed with charging test. The complete sequence was proposed by supplementing the DNA sequence to and by assigning the position of minor bases to this RNA sequence.

• Journal of Life Science
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• v.30 no.6
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• pp.501-512
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• 2020

Sasa quelpaertensis Nakai leaf has been used as a folk medicine for the treatment of gastric ulcer, dipsosis, and hematemesis based on its anti-inflammatory, antipyretic, and diuretic characteristics. We have previously reported the procedure for deriving a phytochemical-rich extract (PRE) from S. quelpaertensis and how PRE and its ethyl acetate fraction (EPRE) exhibits an anticancer effect by inducing apoptosis in various gastric cancer cells. To explore the molecular targets involved in this apoptosis, we investigated the mRNA and microRNA profiles of EPRE-treated SNU-16 human gastric cancer cells. In total, 2,875 differentially expressed genes were identified by RNA sequencing, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the EPRE-modulated genes are associated with apoptosis, mitogen-activated protein kinase, inflammatory response, tumor necrosis factor signaling, and cancer pathways. Subsequently, protein-protein interaction network analysis confirmed interactions among genes associated with cell death and apoptosis, and 27 differentially expressed microRNAs were identified by further sequencing. Here, GO and KEGG pathway analysis revealed that EPRE modified the expression of microRNAs associated with the cell cycle and cell death, as well as signaling of tropomyosin-receptor-kinase receptor, transforming growth factor-b, nuclear factor kB, and cancer pathways. Taken together, these results provide insight into the mechanisms underlying the anticancer effect of EPRE.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.4
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• pp.235-243
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• 2023

As next-generation sequencing has been developed and used widely, RNA-sequencing (RNA-seq) has rapidly emerged as the first choice of tools to validate global transcriptome profiling. With the significant advances in RNA-seq, various types of RNA-seq have evolved in conjunction with the progress in bioinformatic tools. On the other hand, it is difficult to interpret the complex data underlying the biological meaning without a general understanding of the types of RNA-seq and bioinformatic approaches. In this regard, this paper discusses the two main sections of RNA-seq. First, two major variants of RNA-seq are described and compared with the standard RNA-seq. This provides insights into which RNA-seq method is most appropriate for their research. Second, the most widely used RNA-seq data analyses are discussed: (1) exploratory data analysis and (2) pathway enrichment analysis. This paper introduces the most widely used exploratory data analysis for RNA-seq, such as principal component analysis, heatmap, and volcano plot, which can provide the overall trends in the dataset. The pathway enrichment analysis section introduces three generations of pathway enrichment analysis and how they generate enriched pathways with the RNA-seq dataset.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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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.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.9
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• pp.1235-1243
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• 2015

The goat Capra hircus is one of several economically important livestock in China. Advances in molecular genetics have led to the identification of several single nucleotide variation markers associated with genes affecting economic traits. Validation of single nucleotide variations in a whole-transcriptome sequencing is critical for understanding the information of molecular genetics. In this paper, we aim to develop a large amount of convinced single nucleotide polymorphisms (SNPs) for Cashmere goat through transcriptome sequencing. In this study, the transcriptomes of Cashmere goat skin at four stages were measured using RNA-sequencing and 90% to 92% unique-mapped-reads were obtained from total-mapped-reads. A total of 56,231 putative SNPs distributed among 10,057 genes were identified. The average minor allele frequency of total SNPs was 18%. GO and KEGG pathway analysis were conducted to analyze the genes containing SNPs. Our follow up biological validation revealed that 64% of SNPs were true SNPs. Our results show that RNA-sequencing is a fast and efficient method for identification of a large number of SNPs. This work provides significant genetic resources for further research on Cashmere goats, especially for the high density linkage map construction and genome-wide association studies.

• Food Science and Industry
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• v.52 no.3
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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.