• Korean Journal of Plant Resources
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• v.21 no.3
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• pp.210-215
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• 2008

Gene-expression analysis is increasingly important in biological research, with real-time reverse PCR (RTPCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. However, this technique requires important preliminary work for standardizing and optimizing the many parameters involved in the analysis. Plant stress studies are more and more based on gene expression. The analysis of gene expression requires sensitive and reproducible measurements for specific mRNA sequence. Several genes are regulated in response to abitoic stresses, such as salinity, and their gene products function in stress response and tolerance. The design of the primers and TaqMan probes for real-time PCR assays were carried out using the Primer $Express^{TM}$ software 3.0. The PCR efficiency was estimated through the linear regression of the dilution curve. To understand the expression pattern of various genes under salt stressed condition, we have developed a unique public resource of 9 stress-related genes in poplar. In this study, real-time RT-PCR was used to quantify the transcript level of 10 genes (9 stress-related genes and 1 house keeping gene) that could play a role in adaptation of Populus davidiana. Real-time RT-PCR analyses exhibited different expression ratios of related genes. The data obtained showed that determination of mRNA levels could constitute a new approach to study the stress response of P. davidiana after adaptation during growth in salinity condition.

• Proceedings of the Korean Biophysical Society Conference
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• 2003.06a
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• pp.77-77
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• 2003

Gene expression in a cell is regulated by mutual activations or repressions between genes. Identifying the gene regulation network will be one of the most important research topics in the post genomic era. We propose a linear dynamic model of gene regulation for the yeast cell cycle. A small gene network consisting of about 40 genes is reconstructed from the analysis of micro-array gene expression data of yeast S. cerevisiae published by P. Spellman et al. We show that the network construction is consistent with the result of the hierarchical cluster analysis.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.269-280
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• 2020

Time course gene expression data is a large amount of data observed over time in microarray experiments. This data can also simultaneously identify the level of gene expression. However, the experiment process is complex, resulting in frequent missing values due to various causes. In this paper, we propose a pattern consistency index adaptive nearest neighbors as a method of missing value imputation. This method combines the adaptive nearest neighbors (ANN) method that reflects local characteristics and the pattern consistency index that considers consistent degree for gene expression between observations over time points. We conducted a Monte Carlo simulation study to evaluate the usefulness of proposed the pattern consistency index adaptive nearest neighbors (PANN) method for two yeast time course data.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2006.02a
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• pp.98-107
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• 2006

Chromosomal copy number changes (aneuploidies) are common in human populations. The extra chromosome can affect gene expression by whole-genome level. By gene expression microarray analysis, we want to find aberrant gene expression due to aneuploidies in Klinefelter (+X) and Down syndrome (+21). We have analyzed the inactivation status of X-linked genes in Klinefelter Syndrome (KS) by using X-linked cDNA microarray and cSNP analysis. We analyzed the expression of 190 X-linked genes by cDNA microarray from the lymphocytes of five KS patients and five females (XX) with normal males (XY) controls. cDNA microarray experiments and cSNP analysis showed the differentially expressed genes were similar between KS and XX cases. To analyze the differential gene expressions in Down Syndrome (DS), Amniotic Fluid (AF)cells were collected from 12 pregnancies at $16{\sim}18$ weeks of gestation in DS (n=6) and normal (n=6) subjects. We also analysis AF cells for a DNA microarray system and compared the chip data with two dimensional protein gel analysis of amniotic fluid. Our data may provide the basis for a more systematic identification of biological markers of fetal DS, thus leading to an improved understanding of pathogenesis for fetal DS.

• Korean Journal of Biological Psychiatry
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• v.8 no.2
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• pp.203-207
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• 2001

The genome sequencing project has generated and will continue to generate enormous amounts of sequence data including 5 eukaryotic and about 60 prokaryotic genomes. Given this ever-increasing amounts of sequence information, new strategies are necessary to efficiently pursue the next phase of the genome project-the elucidation of gene expression patterns and gene product function on a whole genome scale. In order to assign functional information to the genome sequence, DNA chip(or gene microarray) technology was developed to efficiently identify the differential expression pattern of independent biological samples. DNA chip provides a new tool for genome expression analysis that may revolutionize many aspects of biotechnology including new drug discovery and disease diagnostics.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.371-378
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• 2010

For sensitive and accurate gene expression analysis, normalization of gene expression data against housekeeping genes is required. There are conventional housekeeping gene (e.g. ACT) that primarily function as an internal control of transcription. In this study, we performed an in silico analysis of 278 rice gene expression samples (GSM) in order to identify the gene that is most consistently expressed. Based on this analysis, we identified novel candidate housekeeping genes that displayed improved stability among the cross experimental conditions. Furthermore four of the most conventional housekeeping genes were included in our 30 other housekeeping genes among the most stable genes. Therefore, these 30 genes can he used to normalize transcription results in gene expression studies on rice at a broad range of experimental conditions.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.305-309
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• 2016

PTEN protein is an important tumour suppressor factor detectable by immunohistochemistry. The goal of the present study was to investigate the prognostic role of PTEN gene expression focusing on length of survival in breast cancer patients. This descriptive-analytical study was conducted on 100 breast cancer cases referred to Sabzevar hospitals in the north east of Iran between 2010 and 2011, followed up to 2015. The PTEN gene expression of tumour tissue samples was determined using specific monoclonal antibodies. The data were analyzed using Chi-square test and Fisher's exact test. Patient length of survival was analyzed after 4 years of follow-up using the Cox regression model. The PTEN gene was expressed in 70 of 100 samples, while being found at a high level in all noncancerous samples. There was an inverse significant relationship between expression of PTEN and tumour stage and grade (p<0.001). In addition, expression of PTEN in invasive ductal tumours was less than in non-invasive tumours. There was also an inverse significant relationship between the likelihood of death and PTEN gene expression (p<0.01). These findings indicate that lack of PTEN gene expression can be sign for a worse prognosis and poor survival in breast cancer.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.59-60
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• 2000

The past few years have seen a dramatic increase in gene expression data on the basis of DNA microarrays or DNA chips. Going beyond a generic view on the genome, microarray data are able to distinguish between gene populations in different tissues of the same organism and in different states of cells belonging to the same tissue. This affords a cell-wide view of the metabolic and regulatory processes under different conditions, building an effective basis for new diagnoses and therapies of diseases. In this talk we present machine learning techniques for effective mining of DNA microarray data. A brief introduction to the research field of machine learning from the computer science and artificial intelligence point of view is followed by a review of recently-developed learning algorithms applied to the analysis of DNA chip gene expression data. Emphasis is put on graphical models, such as Bayesian networks, latent variable models, and generative topographic mapping. Finally, we report on our own results of applying these learning methods to two important problems: the identification of cell cycle-regulated genes and the discovery of cancer classes by gene expression monitoring. The data sets are provided by the competition CAMDA-2000, the Critical Assessment of Techniques for Microarray Data Mining.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1005-1021
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• 2007

Traditional procedures for DNA microarray data analysis are to preprocess and normalize the gene expression data, and then to analyze the normalized data using statistical tests. Drawbacks of the traditional methods are: genuine biological signal may be unwillingly eliminated together with artifacts, the limited number of arrays per gene make statistical tests difficult to use the normality assumption or nonparametric method, and genes are tested independently without consideration of interrelationships among genes. A novel method using average rank in each array is proposed to eliminate such drawbacks. This average rank method monitors differentially regulated genes among genetically different groups and the selected genes are somewhat different from those selected by traditional P-value method. Addition of genes selected by the average rank method to the traditional method will provide better understanding of genetic differences of groups.

• Proceedings of the PSK Conference
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• 2003.10a
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• pp.60-61
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• 2003

Human Genome Project has recently been completed and the information on nucleotide sequences of our whole genome is now available at the public or commercial data banks. Next goals are to identify the functions of each gene and to elucidate the intracellular signal transduction pathways regulating gene expression. We have established a PCR-based bioassay to search for biologically active compounds that can modulate the expression of genes encoding important proteins. (omitted)