• KOGO NEWS
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• v.5 no.4
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• pp.9-16
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• 2005

Bioinformatics is a rapidly emerging field of biomedical research. A flood of large-scale genomic expression data transforms the challenges m biomedical research into ones in bioinformatics. Clinical informatics has long developed technologies to imp개ve biomedical research by integrating experimental and clinical information systems. Biomedical informatics, powered by high throughput techniques, genomic-scale databases and advanced clinical information system, is likely to transform our biomedical understanding forever much the same way that biochemistry did to biology a generation ago. The emergence of healthcare and biomedical informatics revolutionizing both bioinformatics and clinical informatics will eventually change the current practice of medicine, including diagnostics, therapeutics and prognostics.

• International Journal of Oral Biology
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• v.41 no.3
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• pp.149-154
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• 2016

The purpose of this study was to develop Streptococcus sobrinus-specific qPCR primers based on the nucleotide sequence of the RNA polymerase ${\beta}-subunit$ gene (rpoB). The specificity of the primers was determined by conventional polymerase chain reaction (PCR) with 12 strains of S. sobrinus and 50 strains (50 species) of non-S. sobrinus bacteria. The sensitivity of the primers was determined by quantitative real-time PCR (qPCR) with serial dilutions of the purified genomic DNAs (40 ng to 4 fg) of S. sobrinus ATCC $33478^T$. The specificity data showed that the S. sobrinus-specific qPCR primers (RTSsob-F4/RTSsob-R4) detected only the genomic DNAs of S. sobrinus strains with a detection limit of up to 4 fg of S. sobrinus genomic DNA. Our results suggest that the RTSsob-F4/RTSsob-R4 primers are useful in detecting S. sobrinus with high sensitivity and specificity for epidemiological studies of dental caries..

• Genomics & Informatics
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• v.18 no.1
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• pp.3.1-3.8
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• 2020

Patient-derived xenograft (PDX) mouse models are frequently used to test the drug efficacy in diverse types of cancer. They are known to recapitulate the patient characteristics faithfully, but a systematic survey with a large number of cases is yet missing in lung cancer. Here we report the comparison of genomic characters between mouse and patient tumor tissues in lung cancer based on exome sequencing data. We established PDX mouse models for 132 lung cancer patients and performed whole exome sequencing for trio samples of tumor-normal-xenograft tissues. Then we computed the somatic mutations and copy number variations, which were used to compare the PDX and patient tumor tissues. Genomic and histological conclusions for validity of PDX models agreed in most cases, but we observed eight (~7%) discordant cases. We further examined the changes in mutations and copy number alterations in PDX model production and passage processes, which highlighted the clonal evolution in PDX mouse models. Our study shows that the genomic characterization plays complementary roles to the histological examination in cancer studies utilizing PDX mouse models.

• Journal of Periodontal and Implant Science
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• v.32 no.2
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• pp.269-280
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• 2002

The purpose of this study is to develop species-specific DNA probes and polymerase chain reaction (PCR) primers for detection and identification of Prevotella nigrescens (P. nigrescens) 9336. This study procedure includes (1) whole-genomic DNA extraction of P. nigrescens 9336 (2) construction of the genomic DNA library, (3) screening of strain-specific DNA probe by reverse Dot Hybridization method, (4) confirmation of strain-specific DNA probe by Southern blot analysis, (5) determination of nucleotide sequences of strain-specific DNA probe. Thirty-five restriction fragments of P. nigrescens 9336 genomic DNA digested with the Hind III were obtained. Reverse dot hybridization and Southern blot analysis data showed that three of them, Pn10, Pn23, and Pn35, could be P. nigrescens 9336-specific DNA probes. These data indicated that these DNA probes could be useful in detection and identification of the P. nigrescens 9336.

• Journal of Microbiology and Biotechnology
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• v.20 no.1
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• pp.187-192
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• 2010

In order to monitor the possibility of horizontal gene transfer between transgenic rice and microorganisms in a paddy rice field, the gene flow from a bifunctional fusion (TPSP) rice containing trehalose-6-phosphate synthase and phosphatase to microorganisms in soils was investigated. The soil samples collected from the paddy rice field during June 2004 to March 2006 were investigated by multiplex PCR, Southern hybridization, and amplified fragment length polymorphism (AFLP). The TPSP gene from soil genomic DNAs was not detected by PCR. Soil genomic DNAs did not show homologies on the Southern blotting data, indicating that gene transfer did not occur during the last two years in the paddy rice field. In addition, the AFLP band patterns produced by soil genomic DNAs from both transgenic and non-transgenic rice fields appeared similar to each other when analyzed by the NTSYSpc program. Thus, these data suggest that transgenic rice does not give a significant impact on the communities of soil microorganisms, although long-term observation may be needed.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.847-859
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• 2011

cDNA microarray-based comparative genomic hybridization(CGH) data includes low-intensity spots and thus a statistical strategy is needed to detect subtle differences between different cancer classes. In this study, genes displaying a high frequency of alteration in one of the different classes were selected among the pre-selected genes that show relatively large variations between genes compared to total variations. Utilizing copy-number changes of the selected genes, this study suggests a statistical approach to predict patients' classes with increased performance by pre-classifying patients with similar genetic alteration scores. Two-stage logistic regression model(TLRM) was suggested to pre-classify homogeneous patients and predict patients' classes for cancer prediction; a decision tree(DT) was combined with logistic regression on the set of informative genes. TLRM was constructed in cDNA microarray-based CGH data from the Cancer Metastasis Research Center(CMRC) at Yonsei University; it predicted the patients' clinical diagnoses with perfect matches (except for one patient among the high-risk and low-risk classified patients where the performance of predictions is critical due to the high sensitivity and specificity requirements for clinical treatments. Accuracy validated by leave-one-out cross-validation(LOOCV) was 83.3% while other classification methods of CART and DT performed as comparisons showed worse performances than TLRM.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.4
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• pp.1591-1597
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• 2015

Background: Korea has experienced rapid economic development in a very short period of time. A mixture of traditional and modern risk factors coexists and the rapid change in non-genetic factors interacts with genetic constituents. With consideration of these unique aspects of Korean society, a large-scale genomic cohort study-the Health Examinees (HEXA) Study-has been conducted to investigate epidemiologic characteristics, genomic features, and gene-environment interactions of major chronic diseases including cancer in the Korean population. Materials and Methods: Following a standardized study protocol, the subjects were prospectively recruited from 38 health examination centers and training hospitals throughout the country. An interview-based questionnaire survey was conducted to collect information on socio-demographic characteristics, medical history, medication usage, family history, lifestyle factors, diet, physical activity, and reproductive factors for women. Various biological specimens (i.e., plasma, serum, buffy coat, blood cells, genomic DNA, and urine) were collected for biorepository according to the standardized protocol. Skilled medical staff also performed physical examinations. Results: Between 2004 and 2013, a total of 167,169 subjects aged 40-69 years were recruited for the HEXA study. Participants are being followed up utilizing active and passive methods. The first wave of active follow-up began in 2012 and it will be continued until 2015. The principal purpose of passive follow-up is based on data linkages with the National Death Certificate, the National Cancer Registry, and the National Health Insurance Claim data. Conclusions: The HEXA study will render an opportunity to investigate biomarkers of early health index and the chronological changes associated with chronic diseases.

• BMB Reports
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• v.37 no.6
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• pp.648-656
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• 2004

This paper deals with the development of a predictive probabilistic model, a composite dependency-reflecting model (CDRM), which was designed to detect core promoter regions and transcription start sites (TSS) in vertebrate genomic DNA sequences, an issue of some importance for genome annotation. The model actually represents a combination of first-, second-, third- and much higher order or long-range dependencies obtained using the expanded maximal dependency decomposition (EMDD) procedure, which iteratively decomposes data sets into subsets on the basis of dependency degree and patterns inherent in the target promoter region to be modeled. In addition, decomposed subsets are modeled by using a first-order Markov model, allowing the predictive model to reflect dependency between adjacent positions explicitly. In this way, the CDRM allows for potentially complex dependencies between positions in the core promoter region. Such complex dependencies may be closely related to the biological and structural contexts since promoter elements are present in various combinations separated by various distances in the sequence. Thus, CDRM may be appropriate for recognizing core promoter regions and TSSs in vertebrate genomic contig. To demonstrate the effectiveness of our algorithm, we tested it using standardized data and real core promoters, and compared it with some current representative promoter-finding algorithms. The developed algorithm showed better accuracy in terms of specificity and sensitivity than the promoter-finding ones used in performance comparison.

• Smart Media Journal
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• v.2 no.4
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• pp.34-40
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• 2013

Rapid sequencing of individual genomes with next generation sequencer opens new horizon to biology and personalized medicine. The analyzed sequences help to check several genomic abnormality, genomic expression, epigenomic phenotypes, gene annotation after assembly of their reads. Several trials integrating genomic information and clinical data will assist disease diagnostics and clinical treatments. To have a large step towards individualized medicine, development of smart interface linking specialized sequence data to the public is necessary.

• Molecules and Cells
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• v.39 no.2
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• pp.77-86
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• 2016

Cancer is a heterogeneous disease caused by diverse genomic alterations in oncogenes and tumor suppressor genes. Despite recent advances in high-throughput sequencing technologies and development of targeted therapies, novel cancer drug development is limited due to the high attrition rate from clinical studies. Patient-derived xenografts (PDX), which are established by the transfer of patient tumors into immunodeficient mice, serve as a platform for co-clinical trials by enabling the integration of clinical data, genomic profiles, and drug responsiveness data to determine precisely targeted therapies. PDX models retain many of the key characteristics of patients' tumors including histology, genomic signature, cellular heterogeneity, and drug responsiveness. These models can also be applied to the development of biomarkers for drug responsiveness and personalized drug selection. This review summarizes our current knowledge of this field, including methodologic aspects, applications in drug development, challenges and limitations, and utilization for precision cancer medicine.