Dynamic covariation between gene expression and proteome characteristics

BMC Bioinformatics

Volumn 6, Issue , 2005, Pages

  Taghavi Azar Sharabiani, Mansour ^a   Siermala, Markku ^a   Lehtinen, Tommi O ^a   Vihinen, Mauno ^a,b  

^a UNIVERSITY OF TAMPERE   (Finland)

^b TAMPERE UNIVERSITY HOSPITAL   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

BIOSYNTHESIS; CELLS; CYTOLOGY; GENES; PROTEINS; T-CELLS; TIME SERIES ANALYSIS; YEAST;

AMINO ACID COMPOSITIONS; BIOINFORMATICS METHODS; DROSOPHILA MELANOGASTER; EXTRACELLULAR SIGNALS; GENE EXPRESSION PATTERNS; GENE EXPRESSION PROFILES; MICROARRAY DATA SETS; SACCHAROMYCES CEREVISIAE CELLS;

GENE EXPRESSION;

AMINO ACID; CD28 ANTIGEN; CD3 ANTIGEN; DNA; PHOSPHOPROTEIN PHOSPHATASE; PROTEIN KINASE; PROTEIN SERINE THREONINE KINASE; PROTEIN TYROSINE KINASE; PROTEOME; TRANSCRIPTOME;

AMINO ACID COMPOSITION; ARTICLE; B LYMPHOCYTE ACTIVATION; B LYMPHOCYTE DIFFERENTIATION; BIOINFORMATICS; CELL CYCLE; CELL SURVIVAL; CELL TYPE; CLUSTER ANALYSIS; CORRELATION ANALYSIS; DNA MICROARRAY; DROSOPHILA MELANOGASTER; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMAN; MATHEMATICAL COMPUTING; NONHUMAN; PHYSICAL CHEMISTRY; PROTEIN EXPRESSION; PROTEIN FUNCTION; PROTEIN LOCALIZATION; PROTEIN STRUCTURE; PROTEIN SYNTHESIS; PROTEOMICS; SACCHAROMYCES CEREVISIAE; SEQUENCE ANALYSIS; SIGNAL TRANSDUCTION; SPECIES DIFFERENCE; STIMULUS RESPONSE; T LYMPHOCYTE ACTIVATION; TIME SERIES ANALYSIS;

DROSOPHILA MELANOGASTER; SACCHAROMYCES CEREVISIAE;

EID: 25444447353     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-6-215     Document Type: Article

References (61)

1. Slonim DK: From patterns to pathways: gene expression data analysis comes of age. Nat Genet 2002, 32 Suppl:502-508.
2. Niehrs C, Pollet N: Synexpression groups in eukaryotes. Nature 1999, 402(6761):483-487.
3. Cohen BA, Mitra RD, Hughes JD, Church GM: A computational analysis of whole-genome expression data reveals chromosomal domains of gene expression. Nat Genet 2000, 26(2):183-186.
4. Caron H, van Schaik B, van der Mee M, Baas F, Riggins G, van Sluis P, Hermus MC, van Asperen R, Boon K, Voute PA et al: The human transcriptome map: clustering of highly expressed genes in chromosomal domains. Science 2001, 291(5507):1289-1292.
5. Brazma A, Jonassen I, Vilo J, Ukkonen E: Predicting gene regulatory elements in silico on a genomic scale. Genome Res 1998, 8(11):1202-1215.
6. Roth FP, Hughes JD, Estep PW, Church GM: Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat Biotechnol 1998, 16(10):939-945.
7. Jansen R, Gerstein M: Analysis of the yeast transcriptome with structural and functional categories: characterizing highly expressed proteins. Nucleic Acids Res 2000, 28(6):1481-1488.
8. Ollila J, Vihinen M: Microarray analysis of B-cell stimulation. Vitam Horm 2002, 64:77-99.
9. Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC, Conklin BR: MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 2003, 4(1):R7.
10. Draghici S, Khatri P, Bhavsar P, Shah A, Krawetz SA, Tainsky MA: Onto-Tools, the toolkit of the modern biologist: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Nucleic Acids Res 2003, 31(13):3775-3781.
11. Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M, Narasimhan S, Kane DW, Reinhold WC, Lababidi S et al: GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 2003, 4(4):R28.
12. Ge H, Liu Z, Church GM, Vidal M: Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat Genet 2001, 29(4):482-486.
13. Kemmeren P, van Berkum NL, Vilo J, Bijma T, Donders R, Brazma A, Holstege FC: Protein interaction verification and functional annotation by integrated analysis of genome-scale data. Mol Cell 2002, 9(5):1133-1143.
14. Fukuchi S, Nishikawa K: Protein surface amino acid compositions distinctively differ between thermophilic and mesophilic bacteria. J Mol Biol 2001, 309(4):835-843.
15. Cedano J, Aloy P, Perez-Pons JA, Querol E: Relation between amino acid composition and cellular location of proteins. J Mol Biol 1997, 266(3):594-600.
16. Drawid A, Jansen R, Gerstein M: Genome-wide analysis relating expression level with protein subcellular localization. Trends Genet 2000, 16(10):426-430.
17. Hegyi H, Gerstein M: The relationship between protein structure and function: a comprehensive survey with application to the yeast genome. J Mol Biol 1999, 288(1):147-164.
18. Andrade MA, O'Donoghue SI, Rost B: Adaptation of protein surfaces to subcellular location. J Mol Biol 1998, 276(2):517-525.
19. Tekaia F, Yeramian E, Dujon B: Amino acid composition of genomes, lifestyles of organisms, and evolutionary trends: a global picture with correspondence analysis. Gene 2002, 297(1-2):51-60.
20. Diehn M, Alizadeh AA, Rando OJ, Liu CL, Stankunas K, Botstein D, Crabtree GR, Brown PO: Genomic expression programs and the integration of the CD28 costimulatory signal in T cell activation. Proc Natl Acad Sci U S A 2002, 99(18):11796-11801.
21. Ollila J, Vihinen M: Stimulation-induced gene expression in Ramos B-cells. Genes Immun 2003, 4(5):343-350.
22. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B: Comprehensive identification of cell cycleregulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998, 9(12):3273-3297.
23. Arbeitman MN, Furlong EE, Imam F, Johnson E, Null BH, Baker BS, Krasnow MA, Scott MP, Davis RW, White KP: Gene expression during the life cycle of Drosophila melanogaster. Science 2002, 297(5590):2270-2275.
24. Mustelin T, Tasken K: Positive and negative regulation of T-cell activation through kinases and phosphatases. Biochem J 2003, 371(Pt 1):15-27.
25. Viola A, Schroeder S, Sakakibara Y, Lanzavecchia A: T lymphocyte costimulation mediated by reorganization of membrane microdomains. Science 1999, 283(5402):680-682.
26. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25(1):25-29.
27. Andreeva A, Howorth D, Brenner SE, Hubbard TJ, Chothia C, Murzin AG: SCOP database in 2004: refinements integrate structure and sequence family data. Nucleic Acids Res 2004, 32 Database issue:D226-229.
28. Akashi H, Gojobori T: Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc Natl Acad Sci U S A 2002, 99(6):3695-3700.
29. Greenbaum D, Jansen R, Gerstein M: Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts. Bioinformatics 2002, 18(4):585-596.
30. Anderson L, Seilhamer J: A comparison of selected mRNA and protein abundances in human liver. Electrophoresis 1997, 18(3-4):533-537.
31. Gygi SP, Rochon Y, Franza BR, Aebersold R: Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 1999, 19(3):1720-1730.
32. Nel AE: T-cell activation through the antigen receptor. Part 1: signaling components, signaling pathways, and signal integration at the T-cell antigen receptor synapse. J Allergy Clin Immunol 2002, 109(5):758-770.
33. Nel AE, Slaughter N: T-cell activation through the antigen receptor. Part 2: role of signaling cascades in T-cell differentiation, anergy, immune senescence, and development of immunotherapy. J Allergy Clin Immunol 2002, 109(6):901-915.
34. Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman D, Lockhart DJ et al: A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 1998, 2(1):65-73.
35. Gerstein M, Jansen R: The current excitement in bioinformatics-analysis of whole-genome expression data: how does it relate to protein structure and function? Curr Opin Struct Biol 2000, 10(5):574-584.
36. Ruse CI, Tan FL, Kinter M, Bond M: Intregrated analysis of the human cardiac transcriptome, proteome and phosphoproteome. Proteomics 2004, 4(5):1505-1516.
37. McRedmond JP, Park SD, Reilly DF, Coppinger JA, Maguire PB, Shields DC, Fitzgerald DJ: Integration of proteomics and genomics in platelets: a profile of platelet proteins and platelet-specific genes. Mol Cell Proteomics 2004, 3(2):133-144.
38. Mijalski T, Harder A, Halder T, Kersten M, Horsch M, Strom TM, Liebscher HV, Lottspeich F, de Angelis MH, Beckers J: Identification of coexpressed gene clusters in a comparative analysis of transcriptome and proteome in mouse tissues. Proc Natl Acad Sci U S A 2005, 102(24):8621-8626.
39. Ribeiro JM, Charlab R, Pham VM, Garfield M, Valenzuela JG: An insight into the salivary transcriptome and proteome of the adult female mosquito Culex pipiens quinquefasciatus. Insect Biochem Mol Biol 2004, 34(6):543-563.
40. Knight CG, Kassen R, Hebestreit H, Rainey PB: Global analysis of predicted proteomes: functional adaptation of physical properties. Proc Natl Acad Sci U S A 2004, 101(22):8390-8395.
41. Benga G, Ferdinand W: Amino acid composition of rat and human liver microsomes in normal and pathological conditions. Biosci Rep 1995, 15(2):111-116.
42. Forli L, Pedersen JI, Bjortuft, Vatn M, Kofstad J, Boe J: Serum amino acids in relation to nutritional status, lung function and energy intake in patients with advanced pulmonary disease. Respir Med 2000, 94(9):868-874.
43. The FlyBase database of the Drosophila genome projects and community literature. Nucleic Acids Res 2003, 31(1):172-175.
44. Ikai A: Thermostability and aliphatic index of globular proteins. J Biochem (Tokyo) 1980, 88(6):1895-1898.
45. Fisher HF: A Limiting Law Relating the Size and Shape of Protein Molecules to Their Composition. Proc Natl Acad Sci U S A 1964, 51:1285-1291.
46. Bigelow CC: On the average hydrophobicity of proteins and the relation between it and protein structure. J Theor Biol 1967, 16(2):187-211.
47. Waugh DF: Protein-protein interactions. Adv Protein Chem 1954, 9:325-437.
48. Kyte J, Doolittle RF: A simple method for displaying the hydropathic character of a protein. J Mol Biol 1982, 157(1):105-132.
49. Eisenberg D, Weiss RM, Terwilliger TC: The helical hydrophobic moment: a measure of the amphiphilicity of a helix. Nature 1982, 299(5881):371-374.
50. Janin J: Surface and inside volumes in globular proteins. Nature 1979, 277(5696):491-492.
51. Hopp TP, Woods KR: Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci U S A 1981, 78(6):3824-3828.
52. Baumann G, Frommel C, Sander C: Polarity as a criterion in protein design. Protein Eng 1989, 2(5):329-334.
53. Bachmair A, Finley D, Varshavsky A: In vivo half-life of a protein is a function of its amino-terminal residue. Science 1986, 234(4773):179-186.
54. Vihinen M: Relationship of protein flexibility to thermostability. Protein Eng 1987, 1(6):477-480.
55. Vihinen M, Torkkila E, Riikonen P: Accuracy of protein flexibility predictions. Proteins 1994, 19(2):141-149.
56. Hua S, Sun Z: Support vector machine approach for protein subcellular localization prediction. Bioinformatics 2001, 17(8):721-728.
57. Krogh A, Larsson B, von Heijne G, Sonnhammer EL: Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001, 305(3):567-580.
58. Maglott D, Ostell J, Pruitt KD, Tatusova T: Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res 2005, 33(Database issue):D54-58.
59. Gough J, Karplus K, Hughey R, Chothia C: Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol 2001, 313(4):903-919.
60. McClure MA, Smith C, Elton P: Parameterization studies for the SAM and HMMER methods of hidden Markov model generation. Proc Int Conf Intell Syst Mol Biol 1996, 4:155-164.
61. Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Barrell D, Bateman A, Binns D, Biswas M, Bradley P, Bork P et al: The InterPro Database, 2003 brings increased coverage and new features. Nucleic Acids Res 2003, 31(1):315-318.