• Journal of Microbiology and Biotechnology
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• v.32 no.2
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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.

• 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.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.37-40
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• 2011

선택 스플라이싱 (alternative splicing)은 mRNA (messenger RNA)의 전구체인 pre-mRNA가 mRNA로 전사될 때 pre-mRNA의 엑손 영역들 (exons)이 여러 가지 유형 (pattern)으로 다시 연결되는 과정을 말한다. 선택 스플라이싱에 의해 하나의 유전자로부터 서로 다른 mRNA가 만들어 지고 서로 다른 이소형의 단백질 (protein isoforms)이 생성된다. 현재까지 알려진 선택 스플라이싱의 유형은 약 7가지 종류가 있으며, 유전자의 돌연변이 및 질병과 밀접한 연관성을 가지고 있는 것으로 알려져 있다. 본 연구에서는 차세대 시퀀싱 (Next Generation Sequencing : NGS) 기술로 생성된 RNA-Seq 데이터로부터 각 유전자 영역에 대한 선택 스플라이싱 유형을 분류/추출하는 새로운 알고리즘을 제안한다. 제안된 알고리즘에서는 RNA-Seq 데이터를 DNA 시퀀스와 mRNA 트랜스크립트 시퀀스에 동시 매핑하고, 각 엑손 영역에 정렬된 RNA-Seq 데이터의 커버리지 정보 및 엑손의 접합 (junction) 정보를 이용하여 발현된 트랜스크립트 (transcript)의 종류와 양을 측정한다. 알고리즘의 유효성을 보이기 위하여 시뮬레이션 데이터를 이용한 인간 유전자 영역에서의 선택 스플라이싱 유형 추출 실험을 수행하였으며, 검증된 선택 스플라이싱 DB와 비교, 검증하였다.

• Korean Journal of Microbiology
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• v.35 no.2
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• pp.121-127
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• 1999

An effective computer program for assembling fragments in DNA sequencing has been developed. The program, called SeqEditor (Sequence Editor), is usable on the pcrsonal computer systems of MS-Widows which is the mosl popular operating system in Korea. It c'm recd several sequence file formats such as GenBak, FASTA, and ASCII. In the SeqEditor program, a dynamic programming algorihm is applied to compute the maximalscoring overlapping alignment between each pjlr of fragments. A novel feature of the program is that SeqEdilor implemnents interaclive operation with a graphical user interface. The performance lests of the prograln 011 fragmen1 data from 16s and 18s rDNA sequencing pi-ojects produced saiisIactory results. This program may be useful to a person who has work of time with large-scale DNA sequencing projects.

• Biomedical Science Letters
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• v.28 no.1
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• pp.42-49
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• 2022

Tetrodotoxin (TTX) is a natural neurotoxin found in several species of puffer fish belonging to Tetraodon fugu genus and has been reported to affect processes such as proliferation, metastasis and invasion of various cancer cells. However, it was not revealed which genes were influenced by these reactions. In this experiment, it was examined in human SW620 colorectal cancer cells. The proliferation of SW620 cells was significantly reduced when treated with 0, 1, 10 and 100 μM TTX for 48 h. It was confirmed using Annexin V-propidium iodide staining that some apoptosis was induced. Differentially expressed genes (DEGs) affecting cell proliferation through RNA sequencing (RNA-seq) were selected. The expression change of DEGs was confirmed by conducting quantitative real-time polymerase chain reaction (qRT-PCR). As a result, the mRNA expression of FOS and WDR48 genes was found to be increased in the 100 μM TTX treatment group compared to the control group. On the other hand, the mRNA expression of ALKBH7, NDUFA13, RIPPLY3 and SELENOM genes was found to be reduced, and in the case of the ALKBH7 gene was identified to show significant differences. This experiment suggests that TTX can be used as an important fundamental data to elucidate the mechanism that inhibits the proliferation of SW620 cells.

• Molecules and Cells
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• v.40 no.10
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• pp.714-730
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• 2017

Pre-mRNA splicing further increases protein diversity acquired through evolution. The underlying driving forces for this phenomenon are unknown, especially in terms of gene expression. A rice alternatively spliced transcript detection microarray (ASDM) and RNA sequencing (RNA-Seq) were applied to differentiate the transcriptome of 4 representative organs of Oryza sativa L. cv. Ilmi: leaves, roots, 1-cm-stage panicles and young seeds at 21 days after pollination. Comparison of data obtained by microarray and RNA-Seq showed a bell-shaped distribution and a co-lineation for highly expressed genes. Transcripts were classified according to the degree of organ enrichment using a coefficient value (CV, the ratio of the standard deviation to the mean values): highly variable (CVI), variable (CVII), and constitutive (CVIII) groups. A higher index of the portion of loci with alternatively spliced transcripts in a group (IAST) value was observed for the constitutive group. Genes of the highly variable group showed the characteristics of the examined organs, and alternatively spliced transcripts tended to exhibit the same organ specificity or less organ preferences, with avoidance of 'organ distinctness'. In addition, within a locus, a tendency of higher expression was found for transcripts with a longer coding sequence (CDS), and a spliced intron was the most commonly found type of alternative splicing for an extended CDS. Thus, pre-mRNA splicing might have evolved to retain maximum functionality in terms of organ preference and multiplicity.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.3997-4003
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• 2014

Background: Infection with certain human papillomavirus (HPV) genotypes is the most important risk factor related with cervical cancer. The objective of the present study was to investigate the prevalence of HPV infection, the distribution of HPV genotypes and HPV E6/E7 oncogene mRNA expression in Turkish women with different cervical cytological findings in Mersin province, Southern Turkey. Materials and Methods: A total of 476 cytological samples belonging to women with normal and abnormal cervical Pap smears were enrolled in the study. For the detection and genotyping assay, a PCR/direct cycle sequencing approach was used. E6/E7 mRNA expression of HPV-16, 18, 31, 33, and 45 was determined by type-specific real-time NASBA assay (NucliSENS EasyQ$^{(R)}$HPV v1.1). Results: Of the 476 samples, 106 (22.3%) were found to be positive for HPV DNA by PCR. The presence of HPV was significantly more common (p<0.001) in HSIL (6/8, 75%) when compared with LSIL (6/14, 42.9%), ASC-US (22/74, 29.7%) and normal cytology (72/380, 18.9%). The most prevalent genotypes were, in descending order of frequency, HPV genotype 66 (22.6%), 16 (20.8%), 6 (14.2%), 31 (11.3%), 53 (5.7%), and 83 (4.7%). HPV E6/E7 oncogene mRNA positivity (12/476, 2.5%) was lower than DNA positivity (38/476, 7.9%). Conclusions: Our data present a wide distribution of HPV genotypes in the analyzed population. HPV genotypes 66, 16, 6, 31, 53 and 83 were the predominant types and most of them were potential carcinogenic types. Because of the differences between HPV E6/E7 mRNA and DNA positivity, further studies are required to test the role of mRNA testing in the triage of women with abnormal cervical cytology or follow up of HPV DNA positive and cytology negative. These epidemiological data will be important to determine the future impact of vaccination on HPV infected women in our region.

• Genomics & Informatics
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• v.17 no.4
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• pp.45.1-45.3
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• 2019

To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.

• Genomics & Informatics
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• v.19 no.2
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• pp.17.1-17.13
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• 2021

Breast cancer is one of the leading causes of cancer in women all over the world and accounts for ~25% of newly observed cancers in women. Epigenetic modifications influence differential expression of genes through non-coding RNA and play a crucial role in cancer regulation. In the present study, epigenetic regulation of gene expression by in-silico analysis of histone modifications using chromatin immunoprecipitation sequencing (ChIP-Seq) has been carried out. Histone modification data of H3K4me3 from one normal-like and four breast cancer cell lines were used to predict miRNA expression at the promoter level. Predicted miRNA promoters (based on ChIP-Seq) were used as a probe to identify gene targets. Five triple-negative breast cancer (TNBC)-specific miRNAs (miR153-1, miR4767, miR4487, miR6720, and miR-LET7I) were identified and corresponding 13 gene targets were predicted. Eight miRNA promoter peaks were predicted to be differentially expressed in at least three breast cancer cell lines (miR4512, miR6791, miR330, miR3180-3, miR6080, miR5787, miR6733, and miR3613). A total of 44 gene targets were identified based on the 3'-untranslated regions of downregulated mRNA genes that contain putative binding targets to these eight miRNAs. These include 17 and 15 genes in luminal-A type and TNBC respectively, that have been reported to be associated with breast cancer regulation. Of the remaining 12 genes, seven (A4GALT, C2ORF74, HRCT1, ZC4H2, ZNF512, ZNF655, and ZNF608) show similar relative expression profiles in large patient samples and other breast cancer cell lines thereby giving insight into predicted role of H3K4me3 mediated gene regulation via the miRNA-mRNA axis.