• Asian Pacific Journal of Cancer Prevention
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• 제15권6호
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• pp.2433-2438
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• 2014

Cancer is the leading cause of the death, accounts for about 13% of all annual deaths worldwide. Many different fields of science are collaborating together studying cancer to improve our knowledge of this lethal disease, and find better solutions for diagnosis and treatment. Proteomics is one of the most recent and rapidly growing areas in molecular biology that helps understanding cancer from an omics data analysis point of view. The human proteome project was officially initiated in 2008. Proteomics enables the scientists to interrogate a variety of biospecimens for their protein contents and measure the concentrations of these proteins. Current necessary equipment and technologies for cancer proteomics are mass spectrometry, protein microarrays, nanotechnology and bioinformatics. In this paper, we provide a brief review on proteomics and its application in cancer research. After a brief introduction including its definition, we summarize the history of major previous work conducted by researchers, followed by an overview on the role of proteomics in cancer studies. We also provide a list of different utilities in cancer proteomics and investigate their advantages and shortcomings from theoretical and practical angles. Finally, we explore some of the main challenges and conclude the paper with future directions in this field.

• Genomics & Informatics
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• 제1권2호
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• pp.87-93
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• 2003

DNA microarray is currently the most prominent tool for investigating large-scale gene expression data. Different algorithms for measuring gene expression levels from scanned images of microarray experiments may significantly impact the following steps of functional genomic analyses. $Affymetrix^{(R)}$ recently introduced high-density microarrays and new statistical algorithms in Microarray Suit (MAS) version 5.0$^{(R)}$. Very high correlations (0.92 - 0.97) between the new algorithms and the old algorithms (MAS 4.0) across several species and conditions were reported. We found that the column-wise array correlations had a tendency to be much higher than the row-wise gene correlations, which may be much more meaningful in the following higher-order data analyses including clustering and pattern analyses. In this paper, not only the detailed comparison of the two sets of algorithms is illustrated, but the impact of the introducing new algorithms on the further clustering analysis of microarray data and of possible pitfalls in mixing the old and the new algorithms were also described.

• Genomics & Informatics
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• 제5권2호
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• pp.83-87
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• 2007

The analysis of DNA microarray data is a rapidly evolving area of bioinformatics, and various types of microarray are emerging as some of the most exciting technologies for use in biological and clinical research. In recent years, microarray technology has been utilized in various applications such as the profiling of mRNAs, assessment of DNA copy number, genotyping, and detection of methylated sequences. However, the analysis of these heterogeneous microarray platform experiments does not need to be performed separately. Rather, these platforms can be co-analyzed in combination, for cross-validation. There are a number of separate laboratory information management systems (LIMS) that individually address some of the needs for each platform. However, to our knowledge there are no unified LIMS systems capable of organizing all of the information regarding multi-platform microarray experiments, while additionally integrating this information with tools to perform the analysis. In order to address these requirements, we developed a web-based LIMS system that provides an integrated framework for storing and analyzing microarray information generated by the various platforms. This system enables an easy integration of modules that transform, analyze and/or visualize multi-platform microarray data.

• BMB Reports
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• 제46권9호
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• pp.439-447
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• 2013

Rice is a monocot gramineous crop, and one of the most important staple foods. Rice is considered a model species for most gramineous crops. Extensive research on rice has provided critical guidance for other crops, such as maize and wheat. In recent years, climate change and exacerbated soil degradation have resulted in a variety of abiotic stresses, such as greenhouse effects, lower temperatures, drought, floods, soil salinization and heavy metal pollution. As such, there is an extremely high demand for additional research, in order to address these negative factors. Studies have shown that the alternative splicing of many genes in rice is affected by stress conditions, suggesting that manipulation of the alternative splicing of specific genes may be an effective approach for rice to adapt to abiotic stress. With the advancement of microarrays, and more recently, next generation sequencing technology, several studies have shown that more than half of the genes in the rice genome undergo alternative splicing. This mini-review summarizes the latest progress in the research of splicing and alternative splicing in rice, compared to splicing in humans. Furthermore, we discuss how additional studies may change the landscape of investigation of rice functional genomics and genetically improved rice.

• Molecular & Cellular Toxicology
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• 제1권2호
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• pp.92-98
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• 2005

DNA microarray analysis of gene expression in toxicogenomics typically requires relatively large amounts of total RNA. This limits the use of DNA microarray when the sample available is small. To confront this limitation, different methods of linear RNA amplification that generate antisense RNA (aRNA) have been optimized for microarray use. The target preparation strategy using amplified RNA in DNA microarray protocol can be divided into direct-incorporation labeling which resulted in cDNA targets (Cy-dye labeled cDNA from aRNA) and indirect-labeling which resulted in cRNA targets (i.e. Cy-dye labeled aRNA), respectively. However, despite the common use of amplified targets (cDNA or cRNA) from aRNAs, no systemic assessment for the use of amplified targets and bias in terms of hybridization performance has been reported. In this investigation, we have compared the hybridization performance of cRNA targets with cDNA targets from aRNA on a 10 K cDNA microarrays. Under optimized hybridization conditions, we found that 43% of outliers from cDNA technique and 86% from the outlier genes were reproducibly detected by both targets hybridization onto cDNA microarray. This suggests that the cRNA labeling method may have a reduced capacity for detecting the differential gene expression when compared to the cDNA target preparation. However, further validation of this discordant result should be pursued to determine which techniques possesses better accuracy in identifying truly differential genes.

• Molecular & Cellular Toxicology
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• 제5권4호
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• pp.335-345
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• 2009

A major source of paeoniflorin (PF) which was from the Paeonia lactiflora root, has been used as a herbal medicine in East Asia for its antiallergic, antiinflammatory, and immunoregulatory effects. However, only few details are known about the mechanism of apoptosis induced by this compound. The present study was undertaken to further elucidate the molecular mechanism of apoptosis and the changes of gene expression elicited by PF using DNA microarrays and computational gene-expression analysis tools in human leukemia U937 cells. A comparative global transcription analysis between treatment with PF and anisomycin (AM) that induces apoptosis in U937 cells revealed that c-Jun-$NH_2$-kinase (JNK) pathway related genes were less expressed in PF-treated cells. Elucidation of the mechanisms by which PF conducts its anti-cancer activities through comparative analysis of the gene expression is necessary to provide a solid foundation for its use as a promising agent in prevention and treatment strategies.

• 한국식물생명공학회:학술대회논문집
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• 한국식물생명공학회 2002년도 춘계학술대회
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• pp.19-25
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• 2002

Biotechnology in the 21st century will be driven by three emerging technologies: genomics, high-throughput biology, and bioinformatics. These technologies are complementary to one another. A large number of economically important crops are currently subjected to whole genome sequencing. Functional genomics for determining the functions of the genes comprising the given plant genome is under progress by using various means including phenotyping data from transgenic mutants, gene expression profiling data from DNA microarrays, and metabolic profiling data from LC/mass analysis. The aim of plant molecular breeding is shifting from introducing agronomic traits such as herbicide and insect resistance to introducing quality traits such as healthful oils and proteins, which will lead to improved and nutritional food and feed products. Plant molecular breeding is also expected to aim to develop crops for producing human therapeutic and industrial proteins.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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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.

• BMB Reports
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• 제37권5호
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• pp.546-551
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• 2004

The increasing pace of development in molecular biology during the last decade has had a direct effect on mass testing and diagnostic applications, including blood screening. We report the model Microarray that has been developed for Hepatitis B virus (HBV) and Hepatitis D virus (HDV) detection. The specific primer pairs of PCR were designed using the Primer Premier 5.00 program according to the conserved regions of HBV and HDV. PCR fragments were purified and cloned into pMD18-T vectors. The recombinant plasmids were extracted from positive clones and the target gene fragments were sequenced. The DNA microarray was prepared by robotically spotting PCR products onto the surface of glass slides. Sequences were aligned, and the results obtained showed that the products of PCR amplification were the required specific gene fragments of HBV, and HDV. Samples were labeled by Restriction Display PCR (RD-PCR). Gene chip hybridizing signals showed that the specificity and sensitivity required for HBV and HDV detection were satisfied. Using PCR amplified products to construct gene chips for the simultaneous clinical diagnosis of HBV and HDV resulted in a quick, simple, and effective method. We conclude that the DNA microarray assay system might be useful as a diagnostic technique in the clinical laboratory. Further applications of RD-PCR for the sample labeling could speed up microarray multi-virus detection.

• BMB Reports
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• 제37권5호
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• pp.612-617
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• 2004

Autosomal dominant polycystic kidney disease (ADPKD) is a common genetic disorder characterized by the formation of fluid-filled cysts in the kidney and progressive renal failure. Other manifestations of ADPKD include the formation of cysts in other organs (liver, pancreas, and spleen), hypertension, cardiac defects, and cerebral aneurysms. The loss of function of the polycystin -1 and -2 results in the formation of epithelium-lined cysts, a process that depends on initial epithelial proliferation. cDNA microarrays powerfully monitor gene expression and have led to the discoveries of pathways regulating complex biological processes. We undertook to profile the gene expression patterns of epithelial cells derived from the cysts of ADPKD patients using the cDNA microarray technique. Candidate genes that were differently expressed in cyst tissues were identified. 19 genes were up-regulated, and 6 down-regulated. Semi-quantitative RT-PCR results were consistent with the microarray findings. To distinguish between normal and epithelial cells, we used the hierarchical method. The results obtained may provide a molecular basis for understanding the biological meaning of cytogenesis.