ArrayXPath: Mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics

Nucleic Acids Research

Volumn 32, Issue WEB SERVER ISS., 2004, Pages

  Chung, Hee Joon ^a   Kim, Mingoo ^a   Park, Chan Hee ^a   Kim, Jihoon ^a   Kim, Ju Han ^a  

^a Seoul National University College of Medicine   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DNA;

ARTICLE; AUTOMATION; COMPUTER GRAPHICS; COMPUTER PROGRAM; DNA MICROARRAY; FISHER EXACT TEST; GENE CLUSTER; GENE EXPRESSION PROFILING; GENE MAPPING; GENE PROBE; GENETIC DATABASE; HUMAN COMPUTER INTERACTION; INFORMATION PROCESSING; NUCLEOTIDE SEQUENCE; ONLINE SYSTEM; PRIORITY JOURNAL; STATISTICAL SIGNIFICANCE;

CLUSTER ANALYSIS; COMPUTER GRAPHICS; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; INTERNET; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SIGNAL TRANSDUCTION; SOFTWARE; SYSTEMS INTEGRATION; USER-COMPUTER INTERFACE;

MARTES PENNANTI;

EID: 3242882715     PISSN: 03051048     EISSN: None     Source Type: Journal    
DOI: 10.1093/nar/gkh476     Document Type: Article

References (17)

1. Roy,P.J., Stuart,J.M., Lurd,J. and Kim,S.K. (2002) Chromosomal clustering of muscle-expressed genes in Caenorhabditis elegans. Nature, 418, 975-979.
2. Lercher,M.J., Urrutia,A. and Hurst,L.D. (2002) Clustering of housekeeping genes provides a unified model of gene order in the human genome. Nature Genet., 31, 180-183.
3. Tavazoie,S., Hughes,J.D., Campbell,M.J., Cho,R.J. and Church,G.M. (1999) Systematic determination of genetic network architecture. Nature Genet., 22, 281-285.
4. Segal,E., Shapira,M., Regev,A., Pe'er,D., Botstein,D., Koller,D. and Friedman,N. (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genet., 34, 166-176.
5. Mewes,H.W., Frishman,D., Guldener,U., Mannhaupt,G., Mayer,K., Mokrejs,M., Morgenstern,B., Munsterkotter,M., Rudd,S. and Weil,B. (2002) MIPS: a database for genomes and protein sequences. Nucleic Acids Res., 30, 31-34.
6. Ashburner,M., Ball,C.A., Blake,J.A., Botstein,D., Butler,H., Cherry,J.M., Davis,A.P., Dolinski,K., Dwight,S.S., Eppig,J.T. et al. (2000) Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genet., 25, 25-29.
7. Dennis,G.,Jr, Sherman,B.T., Hosack,D.A., Yang,J., Gao,W., Lane,H.C. and Lempicki,R.A. (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol., 4, R60.
8. Zeeberg,B.R., Feng,W., Wang,G., Wang,M.D., Fojo,A.T., Sunshine,M., Narasimhan,S., Kane,D.W., Reinhold,W.C., Lababidi,S. et al. (2003) GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol., 4, R28.
9. Kanehisa,M., Goto,S., Kawashima,S., Okuno,Y. and Hattori,M. (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res., 32, D277-D280.
10. Dahlquist,K.D., Salomonis,N., Vranizan,K., Lawlor,S.C. and Conklin,B.R. (2002) GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nature Genet., 31, 19-20.
11. Grosu,P., Townsend,J.P., Hartl,D.L. and Cavalieri,D. (2002) Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks. Genome Res., 12, 1121-1126.
12. Shannon,P., Markiel,A., Ozier,O., Baliga,N.S., Wang,J.T., Ramage,D., Amin,N., Schwikowski,B. and Ideker,T. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 13, 2498-2504.
13. Pan,D., Sun,N., Cheung,K.H., Guan,Z., Ma,L., Holford,M., Deng,X. and Zhao,H. (2003) PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis. BMC Bioinformatics, 4, 56.
14. Pressman,R.S. (1997) Software Engineering: A Practitioner's Approach 4th edn. McGraw-Hill, New York, NY, pp. 341-361.
15. Finney,A. and Hucka,M. (2003) Systems biology markup language: Level 2 and beyond. Biochem. Soc. Trans., 31, 1472-1473.
16. Storey,J.D. and Tibshirani,R. (2003) Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA., 100, 9440-9445.
17. Benjamini,Y. and Hochberg,Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B, 57, 289-300.