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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Genomics & Informatics
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Journal DOI :
Korea Genome Organization
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Volume & Issues
Volume 4, Issue 4 - Dec 2006
Volume 4, Issue 3 - Sep 2006
Volume 4, Issue 2 - Jun 2006
Volume 4, Issue 1 - Mar 2006
Selecting the target year
Sample Size and Power Estimation in Case-Control Genetic Association Studies
Ahn Chul ;
Genomics & Informatics, volume 4, issue 2, 2006, Pages 51~56
In planning a genetic association study, it is necessary to determine the number of samples to be collected for the study in order to achieve sufficient power to detect the hypothesized effect. The case-control design is increasingly used for genetic association studies due to the simplicity of its design. We review the methods for the sample size and power calculations in case-control genetic association studies between a marker locus and a disease phenotype.
Single and Dual Ligand Effects on Gene Expression Changes in Mouse Macrophage Cells
Choi Sang-Dun ; Seo Jeong-Sun ;
Genomics & Informatics, volume 4, issue 2, 2006, Pages 57~64
We identified differentially expressed genes in RAW264.7 cells in response to single and double ligand treatments (LPS,
, 2MA, LPS plus
, and LPS plus 2MA). The majority of the regulated transcripts responded additively to dual ligand treatment. However, a significant fraction responded in a non-additive fashion. Several cytokines showing non-additive transcriptional responses to dual ligand treatment also showed non-additive protein production/secretion responses in separately performed experiments. Many of the genes with non-additive responses to LPS plus 2MA showed enhanced responses and encoded pro-inflammatory proteins. LPS plus
appeared to induce both non-additive enhancement and non-additive attenuation of gene expression. The affected genes were associated with a variety of biological functions. These experiments reveal both dependent and independent regulatory pathways and point out the specific nature of the regulatory interactions.
Theoretical Peptide Mass Distribution in the Non-Redundant Protein Database of the NCBI
Lim Da-Jeong ; Oh Hee-Seok ; Kim Hee-Bal ;
Genomics & Informatics, volume 4, issue 2, 2006, Pages 65~70
Peptide mass mapping is the matching of experimentally generated peptides masses with the predicted masses of digested proteins contained in a database. To identify proteins by matching their constituent fragment masses to the theoretical peptide masses generated from a protein database, the peptide mass fingerprinting technique is used for the protein identification. Thus, it is important to know the theoretical mass distribution of the database. However, few researches have reported the peptide mass distribution of a database. We analyzed the peptide mass distribution of non-redundant protein sequence database in the NCBI after digestion with 15 different types of enzymes. In order to characterize the peptide mass distribution with different digestion enzymes, a power law distribution (Zipfs law) was applied to the distribution. After constructing simulated digestion of a protein database, rank-frequency plot of peptide fragments was applied to generalize a Zipfs law curve for all enzymes. As a result, our data appear to fit Zipfs law with statistically significant parameter values.
Prediction of Exposure to 1763MHz Radiofrequency Radiation Using Support Vector Machine Algorithm in Jurkat Cell Model System
Huang Tai-Qin ; Lee Min-Su ; Bae Young-Joo ; Park Hyun-Seok ; Park Woong-Yang ; Seo Jeong-Sun ;
Genomics & Informatics, volume 4, issue 2, 2006, Pages 71~76
We have investigated biological responses to radiofrequency (RF) radiation in in vitro and in vivo models. By measuring the levels of heat shock proteins as well as the activation of mitogen activated protein kinases (MAPKs), we could not detect any differences upon RF exposure. In this study, we used more sensitive method to find the molecular responses to RF radiation. Jurkat, human T-Iymphocyte cells were exposed to 1763 MHz RF radiation at an average specific absorption rate (SAR) of 10 W/kg for one hour and harvested immediately (R0) or after five hours (R5). From the profiles of 30,000 genes, we selected 68 differentially expressed genes among sham (S), R0 and R5 groups using a random-variance F-test. Especially 45 annotated genes were related to metabolism, apoptosis or transcription regulation. Based on support vector machine (SVM) algorithm, we designed prediction model using 68 genes to discriminate three groups. Our prediction model could predict the target class of 19 among 20 examples exactly (95% accuracy). From these data, we could select the 68 biomarkers to predict the RF radiation exposure with high accuracy, which might need to be validated in in vivo models.
Identification of Novel Genes with Proapoptotic Activity
Kang Eun-Ju ; Kim Jeong-Min ; Kim Na-Young ; Park Kyung-Mi ; Park Seong-Min ; Kim Nam-Soon ; Yoo Hyang-Sook ; Yeom Young-Il ; Kim Soo-Jung ;
Genomics & Informatics, volume 4, issue 2, 2006, Pages 77~79
In order to identify novel proapoptotic genes, we screened approximately 1,000 hypothetical genes whose functions are completely unknown. After these genes were transiently expressed in HeLa cells, their nuclei images were captured using automated high-speed fluorescence microscope, through which the ratio of apoptotic nuclei was estimated. We selected genes that induce greater than 3-fold increase in apoptotic nuclei compared to that of the vector control. The candidate proapoptotic genes were sequenced and their effects on cell death were further confirmed by the additional assay, DNA fragmentation ELISA. Finally, we were able to identify 4 full-length hypo-thetical genes with proapoptotic activity.
Construction of Chromosome-Specific BAC Libraries from the Filamentous Ascomycete Ashbya gossypii
Choi Sang-Dun ;
Genomics & Informatics, volume 4, issue 2, 2006, Pages 80~86
It is clear that the construction of large insert DNA libraries is important for map-based gene cloning, the assembly of physical maps, and simple screening for specific genomic sequences. The bacterial artificial chromosome (BAC) system is likely to be an important tool for map-based cloning of genes since BAC libraries can be constructed simply and analyzed more efficiently than yeast artificial chromosome (YAC) libraries. BACs have significantly expanded the size of fragments from eukaryotic genomes that can be cloned in Escherichia coli as plasmid molecules. To facilitate the isolation of molecular-biologically important genes in Ashbya gossypii, we constructed Ashbya chromosome-specific BAC libraries using pBeloBAC11 and pBACwich vectors with an average insert size of 100 kb, which is equivalent to 19.8X genomic coverage. pBACwich was developed to streamline map-based cloning by providing a tool to integrate large DNA fragments into specific sites in chromosomes. These chromosome-specific libraries have provided a useful tool for the further characterization of the Ashbya genome including positional cloning and genome sequencing.
Improved Algorithms for the Identification of Yeast Proteins and Significant Transcription Factor and Motif Analysis
Lee Seung-Won ; Hong Seong-Eui ; Lee Kyoo-Yeol ; Choi Do-Il ; Chung Hae-Young ; Hur Cheol-Goo ;
Genomics & Informatics, volume 4, issue 2, 2006, Pages 87~93
With the rapid development of MS technologiesy, the demands for a more sophisticated MS interpretation algorithm haves grown as well. We have developed a new protein fingerprinting method using a binomial distribution, (fBIND). With the fBIND, we improved the performance accuracy of protein fingerprinting up to the maximum 49% (more than MOWSE) and 2% than(at a previous binomial distribution approach studied by of Wool et al.) as compared to the established algorithms. Moreover, we also suggest a the statistical approach to define the significance of transcription factors and motifs in the identified proteins based on the Gene Ontology (GO). Abbreviations: fBIND, fingerprinting using binomial distribution; GO, Gene Ontology; MS, Mass Spectrometry; PMF, peptide mass fingerprinting; nr, nonredundant; SGD, Saccharomyces Genome Database