Choong-Hyun Sun

Human nuclear clusterin mediates apoptosis by interacting with Bcl-XL through C-terminal coiled coil domain.

Clusterin (CLU), a glycoprotein, is involved in apoptosis, producing two alternatively spliced isoforms in various cell types. The pro‐apoptotic CLU appears to be a nuclear isoform (nuclear clusterin; nCLU), and the secretory CLU (sCLU) is thought to be anti‐apoptotic. The detailed molecular mechanism of nCLU as a pro‐apoptotic molecule has not yet been clear. In the current study, overexpressed nCLU induced apoptosis in human kidney cells. Biochemical studies revealed that nCLU sequestered Bcl‐XL via a putative BH3 motif in the C‐terminal coiled coil (CC2) domain, releasing Bax, and promoted apoptosis accompanied by activation of caspase‐3 and cytochrome c release. These results suggest a novel mechanism of apoptosis mediated by nCLU as a pro‐apoptotic molecule. J. Cell. Physiol. 227: 1157–1167, 2012.

FiGS: a filter-based gene selection workbench for microarray data.

BackgroundThe selection of genes that discriminate disease classes from microarray data is widely used for the identification of diagnostic biomarkers. Although various gene selection methods are currently available and some of them have shown excellent performance, no single method can retain the best performance for all types of microarray datasets. It is desirable to use a comparative approach to find the best gene selection result after rigorous test of different methodological strategies for a given microarray dataset.ResultsFiGS is a web-based workbench that automatically compares various gene selection procedures and provides the optimal gene selection result for an input microarray dataset. FiGS builds up diverse gene selection procedures by aligning different feature selection techniques and classifiers. In addition to the highly reputed techniques, FiGS diversifies the gene selection procedures by incorporating gene clustering options in the feature selection step and different data pre-processing options in classifier training step. All candidate gene selection procedures are evaluated by the .632+ bootstrap errors and listed with their classification accuracies and selected gene sets. FiGS runs on parallelized computing nodes that capacitate heavy computations. FiGS is freely accessible at http://gexp.kaist.ac.kr/figs.ConclusionFiGS is an web-based application that automates an extensive search for the optimized gene selection analysis for a microarray dataset in a parallel computing environment. FiGS will provide both an efficient and comprehensive means of acquiring optimal gene sets that discriminate disease states from microarray datasets.

COFECO: composite function annotation enriched by protein complex data

COFECO is a web-based tool for a composite annotation of protein complexes, KEGG pathways and Gene Ontology (GO) terms within a class of genes and their orthologs under study. Widely used functional enrichment tools using GO and KEGG pathways create large list of annotations that make it difficult to derive consolidated information and often include over-generalized terms. The interrelationship of annotation terms can be more clearly delineated by integrating the information of physically interacting proteins with biological pathways and GO terms. COFECO has the following advanced characteristics: (i) The composite annotation sets of correlated functions and cellular processes for a given gene set can be identified in a more comprehensive and specified way by the employment of protein complex data together with GO and KEGG pathways as annotation resources. (ii) Orthology based integrative annotations among different species complement the defective annotations in an individual genome and provide the information of evolutionary conserved correlations. (iii) A term filtering feature enables users to collect the specified annotations enriched with selected function terms. (iv) A cross-comparison of annotation results between two different datasets is possible. In addition, COFECO provides a web-based GO hierarchical viewer and KEGG pathway viewer where the enrichment results can be summarized and further explored. COFECO is freely accessible at http://piech.kaist.ac.kr/cofeco.

Enhancement of TREK1 channel surface expression by protein–protein interaction with β-COP

TREK1 belongs to a family of two-pore-domain K(+) (K(2P)) channels and produce background currents that regulate cell excitability. In the present study, we identified a vesicle transport protein, beta-COP, as an interacting partner by yeast two-hybrid screening of a human brain cDNA library with N-terminal region of TREK1 (TREK1-N) as bait. Several in vitro and in vivo binding assays confirmed the protein-protein interaction between TREK1 and beta-COP. We also found that beta-COP was associated with TREK1 in native condition at the PC3 cells. When RFP-beta-COP was co-transfected with GFP-TREK1 into COS-7 cells, both proteins were found localized to the plasma membrane. In addition, the channel activity and surface expression of GFP-TREK1 increased dramatically by co-transfection with RFP-beta-COP. Surface expression of the TREK1 channel was also clearly reduced with the addition of beta-COP-specific shRNA. Collectively, these data suggest that beta-COP plays a critical role in the forward transport of TREK1 channel to the plasma membrane.

A Model of Problem Solving Environment for Integrated Bioinformatics Solution on Grid by Using Condor

To solve the real-world bioinformatics problems on grid, the integration of various analysis tools is necessary in addition to the implementation of basic tools. Workflow based problem solving environment on grid can be the efficient solution for this type of software development. Here we propose a model of simple problem solving environment that enables component based workflow design of integrated bioinformatics applications on Grid environment by using Condor functionalities.

Combined Database System for Binary Protein Interaction and Co-complex Association

Protein-protein interaction (PPI) data has been tremendously increased by enhancement in proteomic technologies. Both binary protein interactions and protein complexes have been recognized as valuable sources for protein functional study. Great portion of the binary protein interactions are, however, predicted or inferred ones by computational methods from protein complex data. Validity of these data remains yet in unacceptable status. Hence, it is necessary to combine binary protein interactions and protein complex data for the sake of comprehensiveness while making clear distinction between them in well classified organization. We constructed a web-based database system that combines binary protein interaction and protein complex data from the nine most reliable public databases. Especially, the system maintains validity of data by adequately categorizing PPIs into direct binary interaction, co-complex relationship, and predicted interaction. In addition, we implemented a comprehensive user interface by which they can access relevant information at maximum. For single or group of queried proteins and their homologs, the system reports binary interactions, complexes, protein conservation in complexes, and binary interactions in complexes with their respective evidences. Our system would be a useful resource for identification of functionally correlated candidates in more reliable and comprehensive way. The system is currently available at http://piech.icu.ac.kr/ppi

DynaMod: dynamic functional modularity analysis

A comprehensive analysis of enriched functional categories in differentially expressed genes is important to extract the underlying biological processes of genome-wide expression profiles. Moreover, identification of the network of significant functional modules in these dynamic processes is an interesting challenge. This study introduces DynaMod, a web-based application that identifies significant functional modules reflecting the change of modularity and differential expressions that are correlated with gene expression profiles under different conditions. DynaMod allows the inspection of a wide variety of functional modules such as the biological pathways, transcriptional factor–target gene groups, microRNA–target gene groups, protein complexes and hub networks involved in protein interactome. The statistical significance of dynamic functional modularity is scored based on Z-statistics from the average of mutual information (MI) changes of involved gene pairs under different conditions. Significantly correlated gene pairs among the functional modules are used to generate a correlated network of functional categories. In addition to these main goals, this scoring strategy supports better performance to detect significant genes in microarray analyses, as the scores of correlated genes show the superior characteristics of the significance analysis compared with those of individual genes. DynaMod also offers cross-comparison between different analysis outputs. DynaMod is freely accessible at http://piech.kaist.ac.kr/dynamod.

OntoSNP: Ontology Driven Knowledgebase for SNP

Ontology-based knowledgebase system can provide great benefits for analysis of biological information. Currently, some ontology-driven information systems have been introduced in this filed. In most cases, however, the advantage of ontology which computer science technology can provide is not fully implemented. Even the well-known ontologies such as Gene Ontology (GO) include only limited number of properties for each term. To find meaningful information across complex relation chains among data, indispensable properties of each term should be described and relationship among data can be analyzed by reasoning. Ontology with rules can play a key role in bioinformatics area because, as well as interoperability, it finds new knowledge and checks validity of candidate knowledge automatically using a reasoning engine. In this paper, we propose an ontology-based information system for SNP analysis (OntoSNP) equipped with Web Ontology Language (OWL) and a reasoning engine. The proposed system provides finding of SNP-gene-disease relations, automatic data validity and knowledge conflict checking. OntoSNP is based on well-defined SNP-gene-disease ontology model in OWL and knowledge-finding and validation rules in Semantic Web Rule Language (SWRL).

BioGridPSE: Integrated Solution for Bioinformatics Analysis Using Computing and Data Grid

The bioinformatics field confronts exponential growth of data and increasing problem complexity. Recent progress in computing and data grid are highlighted as an approach to handle these situations. The simplified usage of grid computing resources and the fault-tolerance of large data placement in bioinformatics analysis are the main keys for taking advantage of grid technology. For the purpose of achieving grid computing jobs, we have already provided a model of problem solving environment for integrated bioinformatics solution on grid supporting process manager. Here we have additionally developed BioGrid problem solving environment (BioGridPSE) as an updated version that guarantees both the completion of computational jobs and data placement jobs in the grid and supports flexible process design for bioinformatics analysis, user customized interface generation, and programmable data parsing. The efficiency and usability of BioGridPSE have been tested with high-throughput and complex bioinformatics analysis such as whole genome global alignment

Whole Genome Alignment with BLAST on Grid Environment

BLAST is a widely used sequence alignment tool for comparing the similarity of DNA and protein. However, it is not commonly used in whole genome alignment because of its computational cost. We could solve the computational problem by using fragmentation based alignment on computing grid environment. The BLAST based genome alignment was sensitive in target sequence finding and flexible for the processing of alignment results. Therefore, various procedures could be tested to get more accurate whole genome alignment. The approach is used to align whole human chromosomes and mouse chromosomes. Compared to other genome alignment methods, we could get alignment list matched well with known synteny map data within reasonable time.