• Genomics & Informatics
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• v.13 no.4
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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.

• BMB Reports
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• v.50 no.4
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• pp.201-207
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• 2017

Alternations in usage of polyadenylation sites during transcription termination yield transcript isoforms from a gene. Recent findings of transcriptome-wide alternative polyadenylation (APA) as a molecular response to changes in biology position APA not only as a molecular event of early transcriptional termination but also as a cellular regulatory step affecting various biological pathways. With the development of high-throughput profiling technologies at a single nucleotide level and their applications targeted to the 3'-end of mRNAs, dynamics in the landscape of mRNA 3'-end is measureable at a global scale. In this review, methods and technologies that have been adopted to study APA events are discussed. In addition, various bioinformatics algorithms for APA isoform analysis using publicly available RNA-seq datasets are introduced.

• 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.

• Genomics & Informatics
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• v.16 no.4
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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.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.7
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• pp.1062-1071
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• 2018

The misuse of anabolic hormones or illegal drugs is a ubiquitous problem in animal husbandry and in food safety. The ban on growth promotants in food producing animals in the European Union is well controlled. However, application regimens that are difficult to detect persist, including newly designed anabolic drugs and complex hormone cocktails. Therefore identification of molecular endogenous biomarkers which are based on the physiological response after the illicit treatment has become a focus of detection methods. The analysis of the 'transcriptome' has been shown to have promise to discover the misuse of anabolic drugs, by indirect detection of their pharmacological action in organs or selected tissues. Various studies have measured gene expression changes after illegal drug or hormone application. So-called transcriptomic biomarkers were quantified at the mRNA and/or microRNA level by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) technology or by more modern 'omics' and high throughput technologies including RNA-sequencing (RNA-Seq). With the addition of advanced bioinformatical approaches such as hierarchical clustering analysis or dynamic principal components analysis, a valid 'biomarker signature' can be established to discriminate between treated and untreated individuals. It has been shown in numerous animal and cell culture studies, that identification of treated animals is possible via our transcriptional biomarker approach. The high throughput sequencing approach is also capable of discovering new biomarker candidates and, in combination with quantitative RT-qPCR, validation and confirmation of biomarkers has been possible. These results from animal production and food safety studies demonstrate that analysis of the transcriptome has high potential as a new screening method using transcriptional 'biomarker signatures' based on the physiological response triggered by illegal substances.

• Journal of Microbiology and Biotechnology
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• v.28 no.4
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• pp.652-662
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• 2018

Diarrhea is a global disease with a high morbidity and mortality rate in children. In this study, 25 fecal samples were collected from children under 5 years old. Seven samples had been taken from healthy children without diarrhea and marked as the healthy control group; eight samples had been sampled from children with diarrhea caused by dyspepsia and defined as the non-infectious group; and ten samples had been taken from children with diarrhea induced by intestinal infections and identified as the infectious group. We detected the microbial communities of samples by using high-throughput sequencing of 16S rRNA genes. The proportion of aerobic and facultative anaerobic microbes in samples of the infectious group was much higher than in the non-infectious group. In addition, the relative abundance of Enterococcus in the healthy control group was significantly higher than in the non-infectious group and infectious group. This can be used as a potential diagnostic biomarker for diarrhea.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.3
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• pp.309-315
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• 2013

MicroRNAs are a class of endogenous small RNAs that play important roles in post-transcriptional gene regulation by directing degradation of mRNAs or facilitating repression of target gene translation. In this study, three small RNA cDNA libraries from the mammary gland tissues of Laoshan dairy goats (Capra hircus) were constructed and sequenced, individually. Through Solexa high-throughput sequencing and bioinformatics analysis, we obtained 50 presumptive novel miRNAs candidates, and 55,448 putative target genes were predicted. GO annotations and KEGG pathway analyses showed the majority of target genes were involved in various biological processes and metabolic pathways. Our results discovered more information about the regulation network between miRNAs and mRNAs and paved a foundation for the molecular genetics of mammary gland development in goats.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.12
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• pp.1816-1825
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• 2019

Objective: We tried to analyze allele-specific expression in the pig neocortex using bioinformatic analysis of high-throughput sequencing results from the parental genomes and offspring transcriptomes from reciprocal crosses between Korean Native and Landrace pigs. Methods: We carried out sequencing of parental genomes and offspring transcriptomes using next generation sequencing. We subsequently carried out genome scale identification of single nucleotide polymorphisms (SNPs) in two different ways using either individual genome mapping or joint genome mapping of the same breed parents that were used for the reciprocal crosses. Using parent-specific SNPs, allele-specifically expressed genes were analyzed. Results: Because of the low genome coverage (${\sim}4{\times}$) of the sequencing results, most SNPs were non-informative for parental lineage determination of the expressed alleles in the offspring and were thus excluded from our analysis. Consequently, 436 SNPs covering 336 genes were applicable to measure the imbalanced expression of paternal alleles in the offspring. By calculating the read ratios of parental alleles in the offspring, we identified seven genes showing allele-biased expression (p<0.05) including three previously reported and four newly identified genes in this study. Conclusion: The newly identified allele-specifically expressing genes in the neocortex of pigs should contribute to improving our knowledge on genomic imprinting in pigs. To our knowledge, this is the first study of allelic imbalance using high throughput analysis of both parental genomes and offspring transcriptomes of the reciprocal cross in outbred animals. Our study also showed the effect of the number of informative animals on the genome level investigation of allele-specific expression using RNA-seq analysis in livestock species.

• Genomics & Informatics
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• v.15 no.1
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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.