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RAN-aCGH: R GUI Tools for Analysis and Visualization of an Array-CGH Experiment.

Genomics & Informatics.2007;5(3):137-139.

RAN-aCGH is an R GUI tool for the analysis and visualization of array comparative genomic hybridization (array-CGH) experiments. The tool consists of data-loading, preprocessing for missing data, several methods for statistical identification of DNA copy number aberration, and visualization of the copy number change. RAN-aCGH requires a single input format, provides various visualizations, and allows the addition of a new statistical method, all in a user-friendly graphic user interface (GUI).
Comparative Genomic Hybridization ; DNA

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GSnet: An Integrated Tool for Gene Set Analysis and Visualization.

Yoon Jeong CHOI ; Hyun Goo WOO ; Ungsik YU

Genomics & Informatics.2007;5(3):133-136.

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.
Gene Ontology ; Microarray Analysis ; Protein Interaction Maps

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arrayImpute: Software for Exploratory Analysis and Imputation of Missing Values for Microarray Data.

Eun Kyung LEE ; Dankyu YOON ; Taesung PARK

Genomics & Informatics.2007;5(3):129-132.

arrayImpute is a software for exploratory analysis of missing data and imputation of missing values in microarray data. It also provides a comparative analysis of the imputed values obtained from various imputation methods. Thus, it allows the users to choose an appropriate imputation method for microarray data. It is built on R and provides a user-friendly graphical interface. Therefore, the users can easily use arrayImpute to explore, estimate missing data, and compare imputation methods for further analysis.

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PathTalk: Interpretation of Microarray Gene-Expression Clusters in Association with Biological Pathways.

Tae Su CHUNG ; Hee Joon CHUNG ; Ju Han KIM

Genomics & Informatics.2007;5(3):124-128.

Microarray technology enables us to measure the expression of tens of thousands of genes simultaneously under various experimental conditions. Clustering analysis is one of the most successful methods for analyzing microarray data using the assumption that co-expressed genes may be co-regulated. It is important to extract meaningful clusters from a long unordered list of clusters and to evaluate the functional homogeneity and heterogeneity of clusters. Many quality measures for clustering results have been suggested in different conditions. In the present study, we consider biological pathways as a collection of biological knowledge and used them as a reference for measuring the quality of clustering results and functional homogeneities. PathTalk visualizes and evaluates functional relationships between gene clusters and biological pathways.
Cluster Analysis ; Multigene Family ; Population Characteristics ; Transcutaneous Electric Nerve Stimulation

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Effect of Normalization on Detection of Differentially-Expressed Genes with Moderate Effects.

Seoae CHO ; Eunjee LEE ; Youngchul KIM ; Taesung PARK

Genomics & Informatics.2007;5(3):118-123.

The current existing literature offers little guidance on how to decide which method to use to analyze one-channel microarray measurements when dealing with large, grouped samples. Most previous methods have focused on two-channel data;therefore they can not be easily applied to one-channel microarray data. Thus, a more reliable method is required to determine an appropriate combination of individual basic processing steps for a given dataset in order to improve the validity of onechannel expression data analysis. We address key issues in evaluating the effectiveness of basic statistical processing steps of microarray data that can affect the final outcome of gene expression analysis without focusingon the intrinsic data underlying biological interpretation.
Analysis of Variance ; Dataset ; Gene Expression ; Statistics as Topic

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Fluorescence Quenching Causes Systematic Dye Bias in Microarray Experiments Using Cyanine Dye.

Hosang JEON ; Sangdun CHOI

Genomics & Informatics.2007;5(3):113-117.

The development of microarray technology has facilitated the understanding of gene expression profiles. Despite its convenience, the cause of dye-bias that confounds data interpretation in dual-color DNA microarray experiments is not well known. In order to economize time and money, it is necessary to identify the cause of dye bias, since designing dye-swaps to reduce the dye-specific bias tends to be very expensive. Hence, we sought to determine the reliable cause of systematic dye bias after treating murine macrophage RAW 264.7 cells with 2-keto-3-deoxyoctonate (KDO), interferon-beta (IFN-beta), and 8-bromoadenosine (8-BR). To find the cause of systematic dye bias from the point of view of fluorescence quenching, we examined the correlation between systematic dye bias and the proportion of each nucleotide in mRNA and oligonucleotide probe sequence. Cy3-dye bias was highly correlated with the proportion of adenines. Our results support the fact that systematic dye bias is affected by fluorescence quenching of each feature. In addition, we also found that the strength of fluorescence quenching is based on not only dye-dye interactions but also dye-nucleotide interactions as well.
Bias (Epidemiology)* ; Fluorescence* ; Gene Expression ; Interferon-beta ; Macrophages ; Oligonucleotide Array Sequence Analysis ; RNA, Messenger ; Transcriptome

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Statistical Analysis of Gene Expression in Innate Immune Responses: Dynamic Interactions between MicroRNA and Signaling Molecules.

Vincent PIRAS ; Kumar SELVARAJOO ; Naoki FUJIKAWA ; Sangdun CHOI ; Masaru TOMITA ; Alessandro GIULIANI ; Masa TSUCHIYA

Genomics & Informatics.2007;5(3):107-112.

MicroRNAs (miRNAs) are known to negatively control protein-coding genes by binding to messenger RNA (mRNA) in the cytoplasm. In innate immunity, the role of miRNA gene silencing is largely unknown. In this study, we performed microarray-based experiments using lipopolysaccharide (LPS)-stimulated macrophages derived from wild-type, MyD88 knockout (KO), TRIF KO, and MyD88/TRIF double KO mice. We employed a statistical approach to determine the importance of the commonality and specificity of miRNA binding sites among groups of temporally co-regulated genes. We demonstrate that both commonality and specificity are irrelevant to define a priori groups of co-downregulated genes. In addition, analyzing the various experimental conditions, we suggest that miRNA regulation may not only be a late-phase process (after transcription) but can also occur even early (1h) after stimulation in knockout conditions. This further indicates the existence of dynamic interactions between miRNA and signaling molecules/ transcription factor regulation; this is another proof for the need of shifting from a 'hard-wired' paradigm of gene regulation to a dynamical one in which the gene co-regulation is established on a case-by-case basis.
Animals ; Binding Sites ; Cytoplasm ; Gene Expression* ; Gene Silencing ; Immunity, Innate* ; Macrophages ; Mice ; MicroRNAs* ; RNA, Messenger ; Sensitivity and Specificity ; Transcription Factors

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Prediction of the Exposure to 1763MHz Radiofrequency Radiation Based on Gene Expression Patterns.

Min Su LEE ; Tai Qin HUANG ; Jeong Sun SEO ; Woong Yang PARK

Genomics & Informatics.2007;5(3):102-106.

Radiofrequency (RF) radiation at the frequency of mobile phones has been not reported to induce cellular responses in in vitro and in vivo models. We exposed HEI-OC1, conditionally-immortalized mouse auditory cells, to RF radiation to characterize cellular responses to 1763 MHz RF radiation. While we could not detect any differences upon RF exposure, whole-genome expression profiling might provide the most sensitive method to find the molecular responses to RF radiation. HEI-OC1 cells were exposed to 1763 MHz RF radiation at an average specific absorption rate (SAR) of 20 W/kg for 24 hr and harvested after 5 hr of recovery (R5), alongside sham-exposed samples (S5). From the whole-genome profiles of mouse neurons, we selected 9 differentially-expressed genes between the S5 and R5 groups using information gain-based recursive feature elimination procedure. Based on support vector machine (SVM), we designed a prediction model using the 9 genes to discriminate the two groups. Our prediction model could predict the target class without any error. From these results, we developed a prediction model using biomarkers to determine the RF radiation exposure in mouse auditory cells with perfect accuracy, which may need validation in in vivo RF-exposure models.
Absorption ; Animals ; Cellular Phone ; Gene Expression* ; Mice ; Neurons ; Support Vector Machine ; Biomarkers

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An Application of the Clustering Threshold Gradient Descent Regularization Method for Selecting Genes in Predicting the Survival Time of Lung Carcinomas.

Seungyeoun LEE ; Youngchul KIM

Genomics & Informatics.2007;5(3):95-101.

In this paper, we consider the variable selection methods in the Cox model when a large number of gene expression levels are involved with survival time. Deciding which genes are associated with survival time has been a challenging problem because of the large number of genes and relatively small sample size (n << p). Several methods for variable selection have been proposed in the Cox model. Among those, we consider least absolute shrinkage and selection operator (LASSO), threshold gradient descent regularization (TGDR), and two different clustering threshold gradient descent regularization (CTGDR)- the K-means CTGDR and the hierarchical CTGDR - and compare these four methods in an application of lung cancer data. Comparison of the four methods shows that the two CTGDR methods yield more compact gene selection than TGDR, while LASSO selects the smallest number of genes. When these methods are evaluated by the approach of Ma and Huang (2007), none of the methods shows satisfactory performance in separating the two risk groups using the log-rank statistic based on the risk scores calculated from the selected genes. However, when the risk scores are calculated from the genes that are significant in the Cox model, the performance of the log-rank statistics shows that the two risk groups are well separated. Especially, the TGDR method has the largest log-rank statistic, and the K-means CTGDR method and the LASSO method show similar performance, but the hierarchical CTGDR method has the smallest log-rank statistic.
Cluster Analysis* ; Gene Expression ; Lung Neoplasms ; Lung* ; Sample Size

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Characteristics in Molecular Vibrational Frequency Patterns between Agonists and Antagonists of Histamine Receptors.

S June OH

Genomics & Informatics.2012;10(2):128-132. doi:10.5808/GI.2012.10.2.128

To learn the differences between the structure-activity relationship and molecular vibration-activity relationship in the ligand-receptor interaction of the histamine receptor, 47 ligands of the histamine receptor were analyzed by structural similarity and molecular vibrational frequency patterns. The radial tree that was produced by clustering analysis of molecular vibrational frequency patterns shows its potential for the functional classification of histamine receptor ligands.
Histamine ; Ligands ; Receptors, Histamine ; Structure-Activity Relationship

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