• 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.

• Genomics & Informatics
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• v.20 no.3
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• pp.32.1-32.15
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• 2022

Malaria is a life-threatening disease, and Africa is still one of the most affected endemic regions despite years of policy to limit infection and transmission rates. Further, studies into the variable efficacy of the vaccine are needed to provide a better understanding of protective immunity. Thus, the current study is designed to delineate the effect of each dose of vaccine on the transcriptional profiles of subjects to determine its efficacy and understand the molecular mechanisms underlying the protection this vaccine provides. Here, we used gene expression profiles of pre and post-vaccination patients after various doses of RTS,S based on samples collected from the Gene Expression Omnibus datasets. Subsequently, differential gene expression analysis using edgeR revealed the significantly (false discovery rate < 0.005) 158 downregulated and 61 upregulated genes between control vs. controlled human malaria infection samples. Further, enrichment analysis of significant genes delineated the involvement of CCL8, CXCL10, CXCL11, XCR1, CSF3, IFNB1, IFNE, IL12B, IL22, IL6, IL27, etc., genes which found to be upregulated after earlier doses but downregulated after the 3rd dose in cytokine-chemokine pathways. Notably, we identified 13 cytokine genes whose expression significantly varied during three doses. Eventually, these findings give insight into the dual role of cytokine responses in malaria pathogenesis. The variations in their expression patterns after various doses of vaccination are linked to the protection as it decreases the severe inflammatory effects in malaria patients. This study will be helpful in designing a better vaccine against malaria and understanding the functions of cytokine response as well.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.45-52
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• 2000

Genomic approach produces massive amount of data within a short time period, New high-throughput automatic sequencers can generate over a million nucleotide sequence information overnight. A typical DNA chip experiment produces tens of thousands expression information, not to mention the tens of megabyte image files, These data must be handled automatically by computer and stored in electronic database, Thus there is a need for systematic approach of data collection, processing, and analysis. DNA sequence information is translated into amino acid sequence and is analyzed for key motif related to its biological and/or biochemical function. Functional genomics will play a significant role in identifying novel drug targets and diagnostic markers for serious diseases. As an enabling technology for functional genomics, bioinformatics is in great need worldwide, In Korea, a new functional genomics project has been recently launched and it focuses on identi☞ing genes associated with cancers prevalent in Korea, namely gastric and hepatic cancers, This involves gene discovery by high throughput sequencing of cancer cDNA libraries, gene expression profiling by DNA microarray and proteomics, and SNP profiling in Korea patient population, Our bioinformatics team will support all these activities by collecting, processing and analyzing these data.

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.3
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• pp.159-163
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• 2000

The genome sequencing project has generated and will contitute to generate enormous amounts of sequence data. Since the first complete genome sequence of bacterium Haemophilus in fluenzae was published in 1995, the complete genome sequences of 2 eukaryotic and about 22 prokaryotic organisms have detemined. Given this everincreasing amounts of sequence information, new strategies are necessary to efficiently pursue the phase of the geome 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 technology was developed to efficienfly identify the differential expression pattern of indepondent biogical samples. DNA chip provides a new tool for genome expreesion analysis that may revolutionize revolutionize many aspects of human kife including mew surg discovery and human disease diagnostics.

• Journal of KIISE
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• v.43 no.1
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• pp.54-60
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• 2016

Brain gene expression information is closely related to the structural and functional characteristics of the brain. Thus, extensive research has been carried out on the relationship between gene expression patterns and the brain's structural organization. In this study, Principal Component Analysis was used to extract features of gene expression patterns, and genes were automatically classified by spatial distribution. Voxels were then clustered with classified specific region expressed genes. Finally, we visualized the clustering results for mouse hippocampal region gene expression with the Allen Brain Atlas. This experiment allowed us to classify the region-specific gene expression of the mouse hippocampal region and provided visualization of clustering results and a brain atlas in an integrated manner. This study has the potential to allow neuroscientists to search for experimental groups of genes more quickly and design an effective test according to the new form of data. It is also expected that it will enable the discovery of a more specific sub-region beyond the current known anatomical regions of the brain.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.280-280
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• 2022

Tiller angle, defined as the angle between the main stem and its side tillers, is one of the main target traits selected inbreeding to achieve the ideal plant type and increase rice yield. Therefore, the discovery and identification of tiller angle-related genes can provide architecture and yield. In the present work, using QTL analysis hence a total of 8 quantitative trait loci (QTLs) were detected based on the phenotype data of tiller angle and tiller crown width in two years. Among them, four QTLs (qTA9, qCW9, qTA9-1, qCW9-1) were overlapped at marker interval RM6235-RM24288 on chromosome 9 with a large effect value regarded as stable major QTL. Twenty tiller angle-related genes were selected from the target region and the relative gene expression levels were checked in five compact type lines, five spreading type lines, and their parental lines. Finally, OsSA URq9 which belongs auxin-responsive SMALL AUXIN UP RNA (SAUR) protein family was selected as a target gene. Overall, this work will help broaden our understanding of the genetic control of tiller angle and tiller crown width, and this study provides both a good theoretical basis and a new genetic resource for the breeding of ideal-type rice.

• BMB Reports
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• v.44 no.5
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• pp.347-351
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• 2011

The testis is major male gonad responsible for spermatogenesis and steroidogenesis. Much knowledge is still remained to be learned about the control of these events. In this study, we performed a comprehensive bioinformatics analysis on 1,196 mouse testis proteins screened from public protein database. Integrated function and pathway analysis were performed through Database for Annotation, Visualization and Integrated Discovery (DAVID) and ingenuity Pathway Analysis (IPA), and significant features were clustered. Protein membrane organization and gene density on chromosomes were analyzed and discussed. The enriched bioinformatics analysis could provide clues and basis to the development of diagnostic markers and therapeutic targets for infertility and male contraception.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1088-1097
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• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

• Korean Journal of Poultry Science
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• v.28 no.2
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• pp.143-153
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• 2001

Using bioinformatic tools for searching the massive genome databases, it is possible to Identify new genes in few minutes for initial discoveries based on evolutionary conservation, domain homology, and tissue expression patterns, followed by further verification and characterization using the bench-top works. The development of high-density two-dimensional arrays has allowed the analysis of the expression of thousands of genes simultaneously in the humans, mice, rats, yeast, and bacteria to elucidate the genes and pathways involved in physiological processes. In addition, rapid and automated protein identification is being achieved by searching protein and nucleotide sequence databases directly with data generated from mass spectrometry. Recently, analysis at the bio-chemical level such as biochemical screening and metabolic profiling (Biochemical genomics) has been introduced as an additional approach for categorical assignment of gene function. To make advantage of recent achievements in computational approaches for facilitated gene discoveries in the avian model, chicken expression sequence tags (ESTs) have been reported and deposited in the international databases. By searching EST databases, a chicken heparanase gene was identified and functionally confirmed by subsequent experiments. Using combination of sub-tractive hybridization assay and Genbank database searches, a chicken heme -binding protein family (cSOUL/HBP) was isolated in the retina and pineal gland of domestic chicken and verified by Northern blot analysis. Microarrays have identified several host genes whose expression levels are elevated following infection of chicken embryo fibroblasts (CEF) with Marek's disease virus (MDV). The ongoing process of chicken genome projects and new discoveries and breakthroughs in genomics and proteomics will no doubt reveal new and exciting information and advances in the avian research.

• Fisheries and Aquatic Sciences
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• v.10 no.3
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• pp.119-126
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• 2007

The analysis of expressed sequence tags (ESTs) is an efficient approach for gene discovery, expression profiling, and the development of resources for functional genomics. To analyze the transcriptome of the abalone Haliotis discus hannai, we conducted EST analysis using seven cDNA libraries made from gill, gut, hepatopancreas, skin, muscle, testis, and ovary. Redundant ESTs were assembled into overlapping contiguous sequences using the assembly program ICAtools. We found that the total 1,393 ESTs formed 135 clusters and 951 singletons, indicating that the overall redundancy of the library was 22%. Of the 1,393 clones, BLAST identified 1,278 clones (91.7%) as known genes; 115 clones (8.3%) did not match any previously described gene. Based on the major functions of their encoded proteins, the identified clones were classified into 16 broad categories. Sequence analysis revealed the presence of micro satellite-containing genes that may be valuable for further gene mapping studies. This study contributes to the identification of numerous EST clones that can be applied to further clarifying the genetics and developmental biology of abalone.