Assessment and integration of publicly available SAGE, cDNA microarray, and oligonucleotide microarray expression data for global coexpression analyses

Genomics

Volumn 86, Issue 4, 2005, Pages 476-488

  Griffith, Obi L ^a   Pleasance, Erin D ^a   Fulton, Debra L ^b   Oveisi, Mehrdad ^a   Ester, Martin ^b   Siddiqui, Asim S ^a   Jones, Steven J M ^a  

^a BRITISH COLUMBIA CANCER AGENCY   (Canada)

^b SIMON FRASER UNIVERSITY   (Canada)

Author keywords

cDNA microarray; Coexpression; Gene expression; Gene expression profiling; Gene Ontology; Microarray analysis; Oligonucleotide microarray; Serial analysis of gene expression

Indexed keywords

COMPLEMENTARY DNA;

ARTICLE; DNA MICROARRAY; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE IDENTIFICATION; GENOME ANALYSIS; HUMAN; MICROARRAY ANALYSIS; PRIORITY JOURNAL; RELIABILITY; SERIAL ANALYSIS OF GENE EXPRESSION;

GENE EXPRESSION PROFILING; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; STATISTICS;

HOMO SAPIENS;

EID: 33644501814     PISSN: 08887543     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ygeno.2005.06.009     Document Type: Article

References (45)

1. M. Schena, D. Shalon, R.W. Davis, and P.O. Brown Quantitative monitoring of gene expression patterns with a complementary DNA microarray Science 270 1995 467 470
2. D.J. Lockhart Expression monitoring by hybridization to high-density oligonucleotide arrays Nat. Biotechnol. 14 1996 1675 1680
3. V.E. Velculescu, L. Zhang, B. Vogelstein, and K.W. Kinzler Serial analysis of gene expression Science 270 1995 484 487
4. J.M. Stuart, E. Segal, D. Koller, and S.K. Kim A gene-coexpression network for global discovery of conserved genetic modules Science 302 2003 249 255
5. S. Li A map of the interactome network of the metazoan C. elegans Science 303 2004 540 543
6. P. Kemmeren Protein interaction verification and functional annotation by integrated analysis of genome-scale data Mol. Cell 9 2002 1133 1143
7. A.J. Walhout Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline Curr. Biol. 12 2002 1952 1958
8. E. Segal Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data Nat. Genet. 34 2003 166 176
9. E.J. Yeoh Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling Cancer Cell 1 2002 133 143
10. A. Nimgaonkar Reproducibility of gene expression across generations of Affymetrix microarrays BMC Bioinformat. 4 2003 27
11. P.K. Tan Evaluation of gene expression measurements from commercial microarray platforms Nucleic Acids Res. 31 2003 5676 5684
12. S. Blackshaw MicroSAGE is highly representative and reproducible but reveals major differences in gene expression among samples obtained from similar tissues Genome Biol. 4 2003 R17
13. L. Huminiecki, A.T. Lloyd, and K.H. Wolfe Congruence of tissue expression profiles from Gene Expression Atlas, SAGEmap and TissueInfo databases BMC Genom. 4 2003 31
14. V. Detours, J.E. Dumont, H. Bersini, and C. Maenhaut Integration and cross-validation of high-throughput gene expression data: comparing heterogeneous data sets FEBS Lett. 546 2003 98 102
15. A.K. Jarvinen Are data from different gene expression microarray platforms comparable? Genomics 83 2004 1164 1168
16. C.A. Iacobuzio-Donahue Highly expressed genes in pancreatic ductal adenocarcinomas: a comprehensive characterization and comparison of the transcription profiles obtained from three major technologies Cancer Res. 63 2003 8614 8622
17. + cells Exp. Mol. Med. 35 2003 460 466
18. A.T. Rogojina, W.E. Orr, B.K. Song, and E.E. Geisert Jr. Comparing the use of Affymetrix to spotted oligonucleotide microarrays using two retinal pigment epithelium cell lines Mol. Vision 9 2003 482 496
19. M. Ishii Direct comparison of GeneChip and SAGE on the quantitative accuracy in transcript profiling analysis Genomics 68 2000 136 143
20. S.J. Evans Evaluation of Affymetrix Gene Chip sensitivity in rat hippocampal tissue using SAGE analysis: serial analysis of gene expression Eur. J. Neurosci. 16 2002 409 413
21. J. Li, M. Pankratz, and J.A. Johnson Differential gene expression patterns revealed by oligonucleotide versus long cDNA arrays Toxicol. Sci. 69 2002 383 390
22. W.P. Kuo, T.K. Jenssen, A.J. Butte, L. Ohno-Machado, and I.S. Kohane Analysis of matched mRNA measurements from two different microarray technologies Bioinformatics 18 2002 405 412
23. N. Mah A comparison of oligonucleotide and cDNA-based microarray systems Physiol. Genom. 16 2004 361 370
24. J. Lu, A. Lal, B. Merriman, S. Nelson, and G. Riggins A comparison of gene expression profiles produced by SAGE, long SAGE, and oligonucleotide chips Genomics 84 2004 631 636
25. J.K. Lee Comparing cDNA and oligonucleotide array data: concordance of gene expression across platforms for the NCI-60 cancer cells Genome Biol. 4 2003 R82
26. D.J. Allocco, I.S. Kohane, and A.J. Butte Quantifying the relationship between co-expression, co-regulation and gene function BMC Bioinformat. 5 2004 18
27. S.K. Kim A gene expression map for Caenorhabditis elegans Science 293 2001 2087 2092
28. S.A. Jelinsky, P. Estep, G.M. Church, and L.D. Samson Regulatory networks revealed by transcriptional profiling of damaged Saccharomyces cerevisiae cells: Rpn4 links base excision repair with proteasomes Mol. Cell. Biol. 20 2000 8157 8167
29. J. Quackenbush Microarrays-Guilt by association Science 302 2003 240 241
30. E.J. Williams, and D.J. Bowles Coexpression of neighboring genes in the genome of Arabidopsis thaliana Genome Res. 14 2004 1060 1067
31. B.H. Mecham Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements Nucleic Acids Res. 32 2004 e74
32. D.T. Ross Systematic variation in gene expression patterns in human cancer cell lines Nat. Genet. 24 2000 227 235
33. M. Nacht Molecular characteristics of non-small cell lung cancer Proc. Natl. Acad. Sci. USA 98 2001 15203 15208
34. D.A. Porter A SAGE (serial analysis of gene expression) view of breast tumor progression Cancer Res. 61 2001 5697 5702
35. H.K. Lee, A.K. Hsu, J. Sajdak, J. Qin, and P. Pavlidis Coexpression analysis of human genes across many microarray data sets Genome Res. 14 2004 1085 1094
36. M. Ashburner Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium Nat. Genet. 25 2000 25 29
37. L.J. Jensen, J. Lagarde, C. von Mering, and P. Bork ArrayProspector: a Web resource of functional associations inferred from microarray expression data Nucleic Acids Res. 32 2004 W445 W448
38. K. Yeung, M. Medvedovic, and R. Bumgarner From co-expression to co-regulation: how many microarray experiments do we need? Genome Biol. 5 2004 R48
39. P.M. Haverty, L.L. Hsiao, S.R. Gullans, U. Hansen, and Z. Weng Limited agreement among three global gene expression methods highlights the requirement for non-global validation Bioinformatics 20 2004 3431 3441
40. K.D. Pruitt, K.S. Katz, H. Sicotte, and D.R. Maglott Introducing RefSeq and LocusLink: curated human genome resources at the NCBI Trends Genet. 16 2000 44 47
41. Mammalian Gene Collection Program Team Generation and initial analysis of more than 15,000 full-length human and mouse cDNA sequences Proc. Natl. Acad. Sci. USA 99 2002 16899 16903
42. G.S. Davidson, B.N. Wylie, and K.W. Boyack Cluster Stability and the Use of Noise in Interpretation of Clustering 2001 IEEE Comput. Soc Los Alamitos, CA 23
43. M.J. de Hoon, S. Imoto, J. Nolan, and S. Miyano Open source clustering software Bioinformatics 20 2004 1453 1454
44. R. Ihaka, and R. Gentleman A language for data analysis and graphics J. Comput. Graphical Stat. 5 1996 299
45. A. Bairoch The Universal Protein Resource (UniProt) Nucleic Acids Res. 33 2005 D154 D159