• Bioindustry News
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• v.15 no.3
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• pp.34-38
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• 2002

• Bioindustry News
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• v.15 no.3
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• pp.16-19
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• 2002

• ICROS
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• v.11 no.3
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• pp.48-51
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• 2005

• The Korea Genome Conference
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• 2005.09a
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• pp.45-45
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• 2005

• Toxicological Research
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• v.25 no.1
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• pp.9-15
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• 2009

Historically, research in toxicology has utilized non-human mammalian species, particularly rats and mice, to study in vivo the effects of toxic exposure on physiology and behavior. However, ethical considerations and the overwhelming increase in the number of chemicals to be screened has led to a shift away from in vivo work. The decline in in vivo experimentation has been accompanied by an increase in alternative methods for detecting and predicting detrimental effects: in vitro experimentation and in silico modeling. Yet, these new methodologies can not replace the need for in vivo work on animal physiology and behavior. The development of new, non-mammalian model systems shows great promise in restoring our ability to use behavioral endpoints in toxicological testing. Of these systems, the zebrafish, Danio rerio, is the model organism for which we are accumulating enough knowledge in vivo, in vitro, and in silico to enable us to develop a comprehensive, high-throughput toxicology screening system.

• Genomics & Informatics
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• v.15 no.4
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• pp.170-177
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• 2017

HOTAIR is an lncRNA that has been known to have an oncogenic role in different cancers. There is limited knowledge of genetic and epigenetic elements and their interactions for the gene encoding HOTAIR. Therefore, understanding the molecular mechanism and its regulation remains to be challenging. We used different in silico analyses to find genetic and epigenetic elements of HOTAIR gene to gain insight into its regulation. We reported different regulatory elements including canonical promoters, transcription start sites, CpGIs as well as epigenetic marks that are potentially involved in the regulation of HOTAIR gene expression. We identified repeat sequences and single nucleotide polymorphisms that are located within or next to the CpGIs of HOTAIR. Our analyses may help to find potential interactions between genetic and epigenetic elements of HOTAIR gene in the human tissues and show opportunities and limitations for researches on HOTAIR gene in future studies.

• 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.

• Genomics & Informatics
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• v.9 no.4
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• pp.189-193
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• 2011

Simple sequence repeats (SSRs) are ubiquitous short tandem duplications found within eukaryotic genomes. Their length variability and abundance throughout the genome has led them to be widely used as molecular markers for crop-breeding programs, facilitating the use of marker-assisted selection as well as estimation of genetic population structure. Here, we report a software application, "SSR-Primer Generator " for SSR discovery, SSR-primer design, and homology-based search of in silico amplicons from a DNA sequence dataset. On submission of multiple FASTA-format DNA sequences, those analyses are batch processed in a Java runtime environment (JRE) platform, in a pipeline, and the resulting data are visualized in HTML tabular format. This application will be a useful tool for reducing the time and costs associated with the development and application of SSR markers.

• Bulletin of the Korean Chemical Society
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• v.32 no.4
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• pp.1237-1240
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• 2011

In this study, we have developed a ligand-based in-silico prediction model to classify chemical structures into hERG blockers using Bayesian and random forest modeling methods. These models were built based on patch clamp experimental results. The findings presented in this work indicate that Laplacian-modified naive Bayesian classification with diverse selection is useful for predicting hERG inhibitors when a large data set is not obtained.

• CELLMED
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• v.3 no.2
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• pp.12.1-12.4
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• 2013

Identification of target protein is an important procedure in the course of drug discovery. Because of complexity, action mechanisms of herbal medicine are rather obscure, unlike small-molecular drugs. Inverse docking simulation is a reverse use of molecular docking involving multiple target searches for known chemical structure. This methodology can be applied in the field of target fishing and toxicity prediction for herbal compounds as well as known drug molecules. The aim of this review is to introduce a series of in silico works for predicting potential drug targets and side-effects based on inverse docking simulations.