• Journal of Ginseng Research
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• v.38 no.4
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• pp.278-288
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• 2014

Background: Panax ginseng Meyer is a traditional medicinal plant famous for its strong therapeutic effects and serves as an important herbal medicine. To understand and manipulate genes involved in secondary metabolic pathways including ginsenosides, transcriptome profiling of P. ginseng is essential. Methods: RNA-seq analysis of adventitious roots of two P. ginseng cultivars, Chunpoong (CP) and Cheongsun (CS), was performed using the Illumina HiSeq platform. After transcripts were assembled, expression profiling was performed. Results: Assemblies were generated from ~85 million and ~77 million high-quality reads from CP and CS cultivars, respectively. A total of 35,527 and 27,716 transcripts were obtained from the CP and CS assemblies, respectively. Annotation of the transcriptomes showed that approximately 90% of the transcripts had significant matches in public databases.We identified several candidate genes involved in ginsenoside biosynthesis. In addition, a large number of transcripts (17%) with different gene ontology designations were uniquely detected in adventitious roots compared to normal ginseng roots. Conclusion: This study will provide a comprehensive insight into the transcriptome of ginseng adventitious roots, and a way for successful transcriptome analysis and profiling of resource plants with less genomic information. The transcriptome profiling data generated in this study are available in our newly created adventitious root transcriptome database (http://im-crop.snu.ac.kr/transdb/index.php) for public use.

• BMB Reports
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• v.48 no.7
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• pp.388-394
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• 2015

The brain is an organ that consists of various cell types. As our knowledge of the structure and function of the brain progresses, cell type-specific research is gaining importance. Together with advances in sequencing technology and bioinformatics, cell type-specific transcriptome studies are providing important insights into brain cell function. In this review, we discuss 3 different cell type-specific transcriptome analyses i.e., Laser Capture Microdissection (LCM), Translating Ribosome Affinity Purification (TRAP)/RiboTag, and single cell RNA-Seq, that are widely used in the field of neuroscience. [BMB Reports 2015; 48(7): 388-394]

• Childhood Kidney Diseases
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• v.15 no.1
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• pp.38-48
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• 2011

Purpose: Focal segmental glomerulosclerosis (FSGS) is the most common glomerulopathy causing pediatric renal failure. Since specific treatment targeting the etiology and pathophysiology of primary FSGS is yet elusive, the authors explored the pathophysiology of FSGS by transcriptome analysis of the disease using an animal model. Methods: FGS/kist strain, a mouse model of primary FSGS, and RFM/kist strain, as control and the parent strain of FGS/kist, were used. Kidney tissues were harvested and isolated renal cortex was used to extract mRNA, which was run on AB 1700 mouse microarray chip after reverse transcription to get the transcriptome profile. Results: Sixty two genes were differentially expressed in FGS/kist kidney tissue compared to the control. Those genes were related to cell cycle/cell death, immune reaction, and lipid metabolism/vasculopathy, and the key molecules of their networks were TNF, IL-6/4, IFN${\gamma}$, TP53, and PPAR${\gamma}$. Conclusion: This study confirmed that renal cell death, immune system activation with subsequent fibrosis, and lipid metabolism-related early vasculopathy were involved in the pathophysiology of FSGS. In addition, the relevance of methodology used in this study, namely transcriptome profiling, and Korean animal model of FGS/kist was validated. Further study would reveal novel pathophysiology of FSGS for new therapeutic targets.

• BMB Reports
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• v.55 no.3
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• pp.113-124
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• 2022

Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at single-molecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 ㎛ resolution. Unfortunately, neither imaging-based technology nor capture-based method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization.

• 한국생물공학회:학술대회논문집
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• 2003.10a
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• pp.601-602
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• 2003

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.246-246
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• 2022

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.132-132
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• 2023

• 한국약용작물학회:학술대회논문집
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• 2012.05a
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• pp.341-342
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• 2012

• Genomics & Informatics
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• v.17 no.1
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• pp.8.1-8.10
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• 2019

Alveolar type II cells constitute a small fraction of the total lung cell mass. However, they play an important role in many cellular processes including trans-differentiation into type I cells as well as repair of lung injury in response to toxic chemicals and respiratory pathogens. Transcription factors are the regulatory proteins dynamically modulating DNA structure and gene expression. Transcription factor profiling in microarray datasets revealed that several members of AP1, ATF, $NF-{\kappa}B$, and C/EBP families involved in diverse responses were expressed in mouse lung type II cells. A transcriptional factor signature consisting of Cebpa, Srebf1, Stat3, Klf5, and Elf3 was identified in lung type II cells, Sox9+ pluripotent lung stem cells as well as in mouse lung development. Identification of the transcription factor profile in mouse lung type II cells will serve as a useful resource and facilitate the integrated analysis of signal transduction pathways and specific gene targets in a variety of physiological conditions.

• Genomics & Informatics
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• v.10 no.1
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• pp.1-8
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• 2012

Recently, the technologies of DNA sequence variation and gene expression profiling have been used widely as approaches in the expertise of genome biology and genetics. The application to genome study has been particularly developed with the introduction of the nextgeneration DNA sequencer (NGS) Roche/454 and Illumina/ Solexa systems, along with bioinformation analysis technologies of whole-genome $de$ $novo$ assembly, expression profiling, DNA variation discovery, and genotyping. Both massive whole-genome shotgun paired-end sequencing and mate paired-end sequencing data are important steps for constructing $de$ $novo$ assembly of novel genome sequencing data. It is necessary to have DNA sequence information from a multiplatform NGS with at least $2{\times}$ and $30{\times}$ depth sequence of genome coverage using Roche/454 and Illumina/Solexa, respectively, for effective an way of de novo assembly. Massive shortlength reading data from the Illumina/Solexa system is enough to discover DNA variation, resulting in reducing the cost of DNA sequencing. Whole-genome expression profile data are useful to approach genome system biology with quantification of expressed RNAs from a wholegenome transcriptome, depending on the tissue samples. The hybrid mRNA sequences from Rohce/454 and Illumina/Solexa are more powerful to find novel genes through $de$ $novo$ assembly in any whole-genome sequenced species. The $20{\times}$ and $50{\times}$ coverage of the estimated transcriptome sequences using Roche/454 and Illumina/Solexa, respectively, is effective to create novel expressed reference sequences. However, only an average $30{\times}$ coverage of a transcriptome with short read sequences of Illumina/Solexa is enough to check expression quantification, compared to the reference expressed sequence tag sequence.