5월 Bioinformatics 저널에 발표된 국내 연구자 논문
1. 서울대 장병탁 박사(Bioinformatics 2005 21(9):2116-2117)
CrossChip: a system supporting comparative analysis of different generations of Affymetrix arrays
Sek Won Kong 1,2,*, , Kyu-Baek Hwang 3, , Richard D. Kim 4, Byoung-Tak Zhang 3, Steven A. Greenberg 5,6, Isaac S. Kohane 4,6 and Peter J. Park 4,6
1Bauer Center for Genomics Research, Harvard University Cambridge, MA, USA
2Molecular Medicine, Beth Israel Deaconess Medical Center Boston, MA, USA
3School of Computer Science and Engineering, Seoul National University Korea
4Harvard-Partners Center for Genetics and Genomics Boston, MA, USA
5Department of Neurology, Brigham and Women's Hospital Boston, MA, USA
6Children's Hospital Informatics Program Boston, MA, USA
Summary: To increase compatibility between different generations of Affymetrix GeneChip arrays, we propose a method of filtering probes based on their sequences. Our method is implemented as a web-based service for downloading necessary materials for converting the raw data files (*.CEL) for comparative analysis. The user can specify the appropriate level of filtering by setting the criteria for the minimum overlap length between probe sequences and the minimum number of usable probe pairs per probe set. Our website supports a within-species comparison for human and mouse GeneChip arrays.
2. KAIST 이도헌 박사(Bioinformatics 2005 21(9):1927-1934)
Detecting clusters of different geometrical shapes in microarray gene expression data
Dae-Won Kim 1, Kwang H. Lee 1,2 and Doheon Lee 1,*
1Department of BioSystems and Advanced Information Technology Research Center, Korea Advanced Institute of Science and Technology 373?1 Guseong-dong, Yuseong-gu, Daejeon, 305?701, Korea
2Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology 373?1 Guseong-dong, Yuseong-gu, Daejeon, 305?701, Korea
Motivation: Clustering has been used as a popular technique for finding groups of genes that show similar expression patterns under multiple experimental conditions. Many clustering methods have been proposed for clustering gene-expression data, including the hierarchical clustering, k-means clustering and self-organizing map (SOM). However, the conventional methods are limited to identify different shapes of clusters because they use a fixed distance norm when calculating the distance between genes. The fixed distance norm imposes a fixed geometrical shape on the clusters regardless of the actual data distribution. Thus, different distance norms are required for handling the different shapes of clusters.
Results: We present the Gustafson?Kessel (GK) clustering method for microarray gene-expression data. To detect clusters of different shapes in a dataset, we use an adaptive distance norm that is calculated by a fuzzy covariance matrix (F) of each cluster in which the eigenstructure of F is used as an indicator of the shape of the cluster. Moreover, the GK method is less prone to falling into local minima than the k-means and SOM because it makes decisions through the use of membership degrees of a gene to clusters. The algorithmic procedure is accomplished by the alternating optimization technique, which iteratively improves a sequence of sets of clusters until no further improvement is possible. To test the performance of the GK method, we applied the GK method and well-known conventional methods to three recently published yeast datasets, and compared the performance of each method using the Saccharomyces Genome Database annotations. The clustering results of the GK method are more significantly relevant to the biological annotations than those of the other methods, demonstrating its effectiveness and potential for clustering gene-expression data.
Availability: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request.
Supplementary information: Supplementary data are available at http://dragon.kaist.ac.kr/gk
3. 국가유전체정보센터 박종화 박사(Bioinformatics 2005 21(10):2541-2543)
PSIbase: a database of Protein Structural Interactome map (PSIMAP)
Sungsam Gong 1, Giseok Yoon 2, Insoo Jang 3, Dan Bolser 4, Panos Dafas 5, Michael Schroeder 6, Hansol Choi 1, Yoobok Cho 2, Kyungsook Han 7, Sunghoon Lee 3, Hwanho Choi 1, Michael Lappe 8, Liisa Holm 9, Sangsoo Kim 3, Donghoon Oh 2 and Jonghwa Bhak 1,2,3,10,*
1Biomatics Lab, Department of BioSystems, KAIST Daejeon, Korea
2OITEK Daejeon, Korea
3NGIC, KRIBB Daejeon, Korea
4MRC-DUNN Cambridge, UK
5City University London, UK
6Biotechnologisches Zentrum TU Dresden, Germany
7Inha University Incheon, Korea
8Max Planck Institute for Molecular Genetics Berlin, Germany
9Helsinki University Finland
10BiO Centre, KAIST Daejeon, Korea
Summary: Protein Structural Interactome map (PSIMAP) is a global interaction map that describes domain?domain and protein?protein interaction information for known Protein Data Bank structures. It calculates the Euclidean distance to determine interactions between possible pairs of structural domains in proteins. PSIbase is a database and file server for protein structural interaction information calculated by the PSIMAP algorithm. PSIbase also provides an easy-to-use protein domain assignment module, interaction navigation and visual tools. Users can retrieve possible interaction partners of their proteins of interests if a significant homology assignment is made with their query sequences.
Availability: http://psimap.org and http://psibase.kaist.ac.kr/
Supplementary information: Supplementary material is available at http://psibase.kaist.ac.kr/Doc/supplementary_material.htm
4. 국외 : Bioinformatics 2005 21(10):2514-2516
PLATCOM: a Platform for Computational Comparative Genomics
Kwangmin Choi 1, Yu Ma 2, Jeong-Heyon Choi 1 and Sun Kim 1,3,*
1School of Informatics, Indiana University Bloomington, IN 47404, USA
2Department of Computer Science, Indiana University Bloomington, IN 47404, USA
3Center for Genomics and Bioinformatics, Indiana University Bloomington, IN 47404, USA
Motivation: As more whole genome sequences become available, comparing multiple genomes at the sequence level can provide insight into new biological discovery. However, there are significant challenges for genome comparison. The challenge includes requirement for computational resources owing to the large volume of genome data. More importantly, since the choice of genomes to be compared is entirely subjective, there are too many choices for genome comparison. For these reasons, there is pressing need for bioinformatics systems for comparing multiple genomes where users can choose genomes to be compared freely.
Results: PLATCOM (Platform for Computational Comparative Genomics) is an integrated system for the comparative analysis of multiple genomes. The system is built on several public databases and a suite of genome analysis applications are provided as exemplary genome data mining tools over these internal databases. Researchers are able to visually investigate genomic sequence similarities, conserved gene neighborhoods, conserved metabolic pathways and putative gene fusion events among a set of selected multiple genomes.
Contact: firstname.lastname@example.org; email@example.com
5. 국외: Bioinformatics 2005 21(10):2264-2270
Prediction of protein interdomain linker regions by a hidden Markov model
Kyounghwa Bae 1, Bani K. Mallick 1 and Christine G. Elsik 2,*
1Department of Statistics, Texas A&M University College Station, TX 77843-3143, USA
2Department of Animal Science and Intercollegiate Faculty of Genetics, Texas A&M University College Station, TX 77843-2471, USA
Motivation: Our aim was to predict protein interdomain linker regions using sequence alone, without requiring known homology. Identifying linker regions will delineate domain boundaries, and can be used to computationally dissect proteins into domains prior to clustering them into families. We developed a hidden Markov model of linker/non-linker sequence regions using a linker index derived from amino acid propensity. We employed an efficient Bayesian estimation of the model using Markov Chain Monte Carlo, Gibbs sampling in particular, to simulate parameters from the posteriors. Our model recognizes sequence data to be continuous rather than categorical, and generates a probabilistic output.
Results: We applied our method to a dataset of protein sequences in which domains and interdomain linkers had been delineated using the Pfam-A database. The prediction results are superior to a simpler method that also uses linker index.
Supplementary information: http://racerx00.tamu.edu/kbae
6. 카톨릭의대 정현준 박사(Bioinformatics 2005 21(10):2554-2555)
ArrayCyGHt: a web application for analysis and visualization of array-CGH data
Su Young Kim 1,2, Suk Woo Nam 1,2, Sug Hyung Lee 1,2, Won Sang Park 1,2, Nam Jin Yoo 1,2, Jung Young Lee 1,2 and Yeun-Jun Chung 3,*
1Microdissection Genomics Research Center, The Catholic University of Korea Seoul, 137-701, Republic of Korea
2Department of Pathology, The Catholic University of Korea Seoul, 137-701, Republic of Korea
3Department of Microbiology, College of Medicine, The Catholic University of Korea Seoul, 137-701, Republic of Korea
Summary: ArrayCyGHt is a web-based application tool for analysis and visualization of microarray-comparative genomic hybridization (array-CGH) data. Full process of array-CGH data analysis, from normalization of raw data to the final visualization of copy number gain or loss, can be straightforwardly achieved on this arrayCyGHt system without the use of any further software. ArrayCyGHt, therefore, provides an easy and fast tool for the analysis of copy number aberrations in any kinds of data format.
Availability: ArrayCyGHt can be accessed at http://genomics.catholic.ac.kr/arrayCGH/
Supplementary information: Technical documentation is available. See http://genomics.catholic.ac.kr/arrayCGH/
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