DNA칩 데이터 분석을 위한 유전자발연 통합분석 프로그램의 개발

Program Development of Integrated Expression Profile Analysis System for DNA Chip Data Analysis

  • 양영렬 (한국생명공학연구원 유전체 연구센터 바이오인포메틱스 Lab.) ;
  • 허철구 (한국생명공학연구원 유전체 연구센터 바이오인포메틱스 Lab.)
  • 발행 : 2001.08.01

초록

DNA칩의 유전자 발현 데이터의 통합적 분석을 위하여 매트랩을 기반으로 한 통합분석 프로그램을 구축하였다. 이 프로그램은 유전자 발현 분석을 위해 일반적으로 많이 쓰는 방법인 Hierarchical clustering(HC), K-means, Self-organizing map(SOM), Principal component analysis(PCA)를 지원하며, 이외에 Fuzzy c-means방법과 최근에 발표된 Singular value decomposition(SVD) 분석 방법도 지원하고 있다. 통합분석프로그램의 성능을 알아보기 위하여 효모의 포자형성(sporulation)과 정의 유전자발현 데이터를 사용하였으며, 각 분석 방법에 따른 분석 결과를 제시하였으며, 이 프로그램이 유전자 발현데이타의 통합적인 분석을 위해 효과적으로 사용될 수 있음을 제시하였다.

A program for integrated gene expression profile analysis such as hierarchical clustering, K-means, fuzzy c-means, self-organizing map(SOM), principal component analysis(PCA), and singular value decomposition(SVD) was made for DNA chip data anlysis by using Matlab. It also contained the normalization method of gene expression input data. The integrated data anlysis program could be effectively used in DNA chip data analysis and help researchers to get more comprehensive analysis view on gene expression data of their own.

키워드

참고문헌

  1. Special supplement to Nature Genetics v.21 The chipping forecast
  2. Trends Pharmacol Sci v.22 no.7 Proteomics-post-genomic cartography to understand gene function Naaby-Hansen S;Waterfield MD;Cramer R.
  3. International Medical Informatics Association Yearbook Luscombe N M;Greenbaum D;Gerstein M.
  4. Functional Genomics v.2 no.5 Level-by level inference from large-scale gene expression data Somogyi R.
  5. Bioinformatics v.16 no.11 Biochemical systems anlaysis of genome-wide expression data Voit E.O.;Radivoyevitch T.
  6. Bulletin of Mathematical Biology v.62 Modeling transcriptional control in gene networks-methods,recent results and future directions Paul S.;Douglas A.B.;John H.B.
  7. Papers on microarray data analysis
  8. Stanford Microarray Database
  9. Microarray Biochip Technology Mark Schena
  10. CHAOS v.11 no.1 Extracting information from cDNA arrays Herzel H;Beule D.;Kielbas S.;Korbel J.
  11. Nature Reviews Genetics v.2 Computational analysis of microarray data Quackenbush J.
  12. ACM Computing Surveys v.31 no.3 Data clustering:A review Jain A.K.;Murty M<.N.;Flynn P.J.
  13. Genome biology v.1 no.2 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns Hastie T.;Thbshirani R.;et.al
  14. PNAS v.97 no.5 Fundamental patterns underlying gene expression profiles:Simplicity from complexity Neal S.H.Madhusmita M;et.al
  15. PNAS v.98 no.4 Dynamic modeling of gene expression data Neal S.H Amos M;et.al
  16. Bioinformatics v.16 no.10 Support vector machine classification and validation of cancer tissue samples using microarray expression data Terrence S.F.;Nello C.;et.al
  17. Databases and other software tools for Gene Expression
  18. Matlab
  19. ESIT 2000 v.14 no.15 Gene expression data mining for functional genomics Guthke R;Hahn D.;et.al
  20. Science v.282 no.5389 The transcriptional program of sporulation in budding yearst Chu S;DeRisi J;Eisen M;et.alMulholland J;Botstein D;Brown PO;Herskowitz I.
  21. Self-organizing maps($2^{nd}$ edition) Kohonen,T.
  22. Theoretical Aspects of Pattern Analysis In:New Approaches for the Generation and Analysis of Microbial Fingerprints A.van Ooyen;L.Dijkshoorn(ed.);K.J.Towner(ed.);M.Struelens(ed.)
  23. Applied Multivariate Statistical Analysis($4^{th}$ edition Johnson R.A.;Wichern D.W.
  24. Pacific Symposium on Biocomputing 2000 Principal Component analysis to summerize microarray experiments:Application to sporulation time series Raychaudhuri S.;Stuart J.M.;Altman R.B
  25. Human Molecular Genetics v.8 no.10 Computational methods for the identification of differential and coordinated gene expression Claverie J.M.