• 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.

• Animal Bioscience
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• v.36 no.2
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• pp.175-190
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• 2023

Objective: The study was conducted to screen differentially expressed long noncoding RNA (lncRNA) in chickens by high-throughput sequencing and explore its mechanism of action on intramuscular fat deposition. Methods: Herein, Rose crown and Cbb broiler chicken embryo breast and leg muscle lncRNA and mRNA expression profiles were constructed by RNA sequencing. A total of 96 and 42 differentially expressed lncRNAs were obtained in Rose crown vs Cobb broiler chicken breast and leg muscle, respectively. lncRNA-ENSGALT00000046546, with high interspecific variability and a potential regulatory role in lipid metabolism, and its predicted downstream target gene 1-acylglycerol-3-phosphate-O-acyltransferase 2 (AGPAT2), were selected for further study on the preadipocytes. Results: lncRNA-46546 overexpression in chicken preadipocyte 2 cells significantly increased (p<0.01) the expression levels of AGPAT2 and its downstream genes diacylglycerol acyltransferase 1 and diacylglycerol acyltransferase 2 and those of the fat metabolism-related genes peroxisome proliferator-activated receptor γ, CCAAT/enhancer binding protein α, fatty acid synthase, sterol regulatory element-binding transcription factor 1, and fatty acid binding protein 4. The lipid droplet concentration was higher in the overexpression group than in the control cells, and the triglyceride content in cells and medium was also significantly increased (p<0.01). Conclusion: This study preliminarily concludes that lncRNA-46546 may promote intramuscular fat deposition in chickens, laying a foundation for the study of lncRNAs in chicken early embryonic development and fat deposition.

• Molecules and Cells
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• v.46 no.2
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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.

• Environmental Engineering Research
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• v.14 no.1
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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.

• Molecules and Cells
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• v.44 no.3
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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.

• Molecules and Cells
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• v.45 no.9
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• pp.610-619
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• 2022

Cellular senescence plays a paradoxical role in tumorigenesis through the expression of diverse senescence-associated (SA) secretory phenotypes (SASPs). The heterogeneity of SA gene expression in cancer cells not only promotes cancer stemness but also protects these cells from chemotherapy. Despite the potential correlation between cancer and SA biomarkers, many transcriptional changes across distinct cell populations remain largely unknown. During the past decade, single-cell RNA sequencing (scRNA-seq) technologies have emerged as powerful experimental and analytical tools to dissect such diverse senescence-derived transcriptional changes. Here, we review the recent sequencing efforts that successfully characterized scRNA-seq data obtained from diverse cancer cells and elucidated the role of senescent cells in tumor malignancy. We further highlight the functional implications of SA genes expressed specifically in cancer and stromal cell populations in the tumor microenvironment. Translational research leveraging scRNA-seq profiling of SA genes will facilitate the identification of novel expression patterns underlying cancer susceptibility, providing new therapeutic opportunities in the era of precision medicine.

• Genomics & Informatics
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• v.16 no.4
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• pp.32.1-32.7
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• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.