• Archives of Pharmacal Research
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• v.19 no.6
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• pp.450-455
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• 1996

The genomic DNA was prepared from trout liver which was treated with 3-methycholanthrene, and cloned into lambda EMBL3 at BamHl site. The genomic library was constructed via infections of these recombinant phages into E. coli K802, and screened by the most $5^I$-portion of trout CYP1A1 cDNA. After the screening of $10^9$ clones of the amplified library, 12 positive clones were isolated, and subjected to further screenings. The results of southern blot hybridization of genomic DNA prepared from the positive clone showed the presence of a single gene of CYP1A1, and 3.5 Kb PstI fragment that hybridizes with the most $5^I$-region DNA of CYP1A1 cDNA. The restriction map of PstI fragment was determined by the restriction digestion with various enzymes. The nucleotide sequence of the upstream genomic DNA of CYPIAI was determined by DNA sequencing of exonuclease III unidirectionally deleted PstI fragment DNA using $[^{35}/S]$dATP. This paper presented the upstream genomic DNA of CYP1A1 contained a part of coding region which was about 351 base pairs (from ATG to PstI site at 3563).

• Journal of Microbiology
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• v.43 no.5
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• pp.411-416
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• 2005

The high-throughput sequencing of microbial genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable research attention in recent years. This paper addresses the development of a predictive model, known as the dependence decomposition weight matrix model (DDWMM), which was designed to detect the core promoter region, including the -10 region and the transcription start sites (TSSs), in prokaryotic genomic DNA sequences. This is an issue of some importance with regard to genome annotation efforts. Our predictive model captures the most significant dependencies between positions (allowing for non­adjacent as well as adjacent dependencies) via the maximal dependence decomposition (MDD) procedure, which iteratively decomposes data sets into subsets, based on the significant dependence between positions in the promoter region to be modeled. Such dependencies may be intimately related to biological and structural concerns, since promoter elements are present in a variety of combinations, which are separated by various distances. In this respect, the DDWMM may prove to be appropriate with regard to the detection of core promoter regions and TSSs in long microbial genomic contigs. In order to demonstrate the effectiveness of our predictive model, we applied 10-fold cross-validation experiments on the 607 experimentally-verified promoter sequences, which evidenced good performance in terms of sensitivity.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.12
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• pp.1691-1695
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• 2006

The identification method that inflects real time ultrasound (RUT) and the potential application of marker assisted selection (MAS) for improvement of a cow population of Hanwoo (Korean Native cattle) was studied. The averages of RUT longissimus muscle area, RUT fat thickness, and RUT marbling score scanned at the 13th rib were 55.78 $cm^2$, 3.70 mm and 3.83 scores, respectively. We investigated the effects of the two SNPs (Kpn2 I and Msp I) in the leptin gene on carcass traits for Hanwoo cows by using ultrasound measurements. Genotype CC of the Kpn2 I had a significantly higher effect on back fat thickness (4.23 mm) and longissimus muscle area (57.57 $cm^2$) than genotype TT (3.14 mm, 53.93 $cm^2$, respectively, p<0.05). Genotype AA of the Msp I had a significantly higher effect only on marbling score (5.37) than genotype AB (3.57, p<0.05) and BB (3.37, p<0.05). Significant effects of SNPs in the leptin gene were found for the ultrasound measures of body composition in live cattle.

• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• Animal Bioscience
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• v.37 no.4
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• pp.555-566
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• 2024

Objective: This study aimed to assess the genetic parameters and accuracy of genomic predictions for twenty-four linear body conformation traits and overall conformation scores in Korean Holstein dairy cows. Methods: A dataset of 2,206 Korean Holsteins was collected, and genotyping was performed using the Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The traits investigated included body traits (stature, height at front end, chest width, body depth, angularity, body condition score, and locomotion), rump traits (rump angle, rump width, and loin strength), feet and leg traits (rear leg set, rear leg rear view, foot angle, heel depth, and bone quality), udder traits (udder depth, udder texture, udder support, fore udder attachment, front teat placement, front teat length, rear udder height, rear udder width, and rear teat placement), and overall conformation score. Accuracy of genomic predictions was assessed using the single-trait animal model genomic best linear unbiased prediction method implemented in the ASReml-SA v4.2 software. Results: Heritability estimates ranged from 0.10 to 0.50 for body traits, 0.21 to 0.35 for rump traits, 0.13 to 0.29 for feet and leg traits, and 0.05 to 0.46 for udder traits. Rump traits exhibited the highest average heritability (0.29), while feet and leg traits had the lowest estimates (0.21). Accuracy of genomic predictions varied among the twenty-four linear body conformation traits, ranging from 0.26 to 0.49. The heritability and prediction accuracy of genomic estimated breeding value (GEBV) for the overall conformation score were 0.45 and 0.46, respectively. The GEBVs for body conformation traits in Korean Holstein cows had low accuracy, falling below the 50% threshold. Conclusion: The limited response to selection for body conformation traits in Korean Holsteins may be attributed to both the low heritability of these traits and the lower accuracy estimates for GEBVs. Further research is needed to enhance the accuracy of GEBVs and improve the selection response for these traits.

• Mycobiology
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• v.38 no.1
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• pp.70-73
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• 2010

Temperature-sensitive yeast mutants were used to screen for cell cycle-related genes from Pleurotus eryngii genomic DNA. A mushroom genomic DNA library was established and each gene was screened for the ability to rescue seven Saccharomyces cerevisiae temperature-sensitive strains. Hundreds of yeast transformants were selected at restrictive temperatures over $30^{\circ}C$. Plasmids from the transformants that survived were isolated and transformed back into their host strains. The temperature sensitivity of the resulting transformants was tested from $30^{\circ}C$ to $37^{\circ}C$. Ten DNA fragments from P. eryngii were able to rescue yeast temperature-sensitive strains, and their DNA sequences were determined.

• Journal of Preventive Medicine and Public Health
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• v.36 no.3
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• pp.213-222
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• 2003

Genomic epidemiology is defined as 'an evoking field of inquiring that uses the systematic application of epidemiologic methods are approaches in population-based studies of the impact of human genetic variation on health and disease (Khoury, 1998)'. Most human diseases are caused by the intricate interaction among environmental exposures and genetic susceptibility factors. Susceptibility genes involved in disease pathogenesis are categorized into two groups: high penetrance genes (i.e., BRAC1, RB, etc.) and lour penetranoe genes (i.e., GSTs, Cyps, XRCC1, ets.), and low penetrance susceptibility genes has the higher priority for epidemiological research due to high population attributable risk. In this paper, the summarized results of the association study between single nucleotide polymorphisms (SNPs) and breast cancer in Korea were introduced and the international trends of genomic epidemiology research were reviewed with an emphasis on internee-based case-control and cohort consortium.

• BMB Reports
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• v.57 no.1
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• pp.66-70
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• 2024

Prime editors (PEs), which are CRISPR-Cas9 nickase (H840A)-reverse transcriptase fusion proteins programmed with prime editing guide RNAs (pegRNAs), can not only edit bases but also install transversions, insertions, or deletions without both donor DNA and double-strand breaks at the target DNA. As the demand for in-locus tagging is increasing, to reflect gene expression dynamics influenced by endogenous genomic contexts, we demonstrated that PEs can be used to introduce the hemagglutinin (HA) epitope tag to a target gene locus, enabling molecular and biochemical studies using in-locus tagged plants. To promote genome-wide in-locus tagging, we also implemented a publicly available database that designs pegRNAs for in-locus tagging of all the Arabidopsis genes.

• Proceedings of the Korean Society of Life Science Conference
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• 2006.09a
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• pp.54.2-54.2
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• 2006

• Mycobiology
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• v.42 no.4
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• pp.311-316
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• 2014

Lichen studies, including biodiversity, phylogenetic relationships, and conservation concerns require definitive species identification, however many lichens can be challenging to identify at the species level. Molecular techniques have shown efficacy in discriminating among lichen taxa, however, obtaining genomic DNA from herbarium and fresh lichen thalli by conventional methods has been difficult, because lichens contain high proteins, polysaccharides, and other complex compounds in their cell walls. Here we report a rapid, easy, and inexpensive protocol for extracting PCR-quality DNA from various lichen species. This method involves the following two steps: first, cell breakage using a beadbeater; and second, extraction, isolation, and precipitation of genomic DNA. The procedure requires approximately 10 mg of lichen thalli and can be completed within 20 min. The obtained DNAs were of sufficient quality and quantity to amplify the internal transcribed spacer region from the fungal and algal lichen components, as well as to sequence the amplified products. In addition, 26 different lichen taxa were tested, resulting in successful PCR products. The results of this study validated the experimental protocols, and clearly demonstrated the efficacy and value of our KCl extraction method applied in the fungal and algal samples.