Advertisement

Bioinformatics and In Silico 2D Gel Electrophoresis

  • Xuhua Xia
Chapter

Abstract

Proteins can be separated in a 2-D gel based on protein isoelectric point (pI) and molecular weight (MW), and the more abundant proteins will manifest themselves with a larger and darker dots in the gel than less abundant proteins. Because protein pI and MW can be easily calculated, and protein abundance can be approximated by predicted translation efficiency, we can do in silico 2-D gel and compare the separation pattern against that in the empirical 2-D gel. Differences between the two suggest post-translational modifications. The approach of in silico 2-D gel is detailed in this chapter.

References

  1. Ast G (2004) How did alternative splicing evolve? Nat Rev Genet 5(10):773–782CrossRefPubMedGoogle Scholar
  2. Bumann D, Aksu S, Wendland M, Janek K, Zimny-Arndt U, Sabarth N, Meyer TF, Jungblut PR (2002) Proteome analysis of secreted proteins of the gastric pathogen Helicobacter pylori. Infect Immun 70(7):3396–3403CrossRefPubMedPubMedCentralGoogle Scholar
  3. Carroll J, Fearnley IM, Shannon RJ, Hirst J, Walker JE (2003) Analysis of the subunit composition of complex I from bovine heart mitochondria. Mol Cell Proteomics 2(2):117–126CrossRefPubMedGoogle Scholar
  4. Diehn M, Eisen MB, Botstein D, Brown PO (2000) Large-scale identification of secreted and membrane-associated gene products using DNA microarrays. Nat Genet 25(1):58–62CrossRefPubMedGoogle Scholar
  5. Epstein CB, Butow RA (2000) Microarray technology – enhanced versatility, persistent challenge. Curr Opin Biotechnol 11(1):36–41CrossRefPubMedGoogle Scholar
  6. Gaasterland T, Bekiranov S (2000) Making the most of microarray data [news]. Nat Genet 24(3):204–206CrossRefPubMedGoogle Scholar
  7. Graveley BR (2005) Mutually exclusive splicing of the insect Dscam pre-mRNA directed by competing intronic RNA secondary structures. Cell 123(1):65–73CrossRefPubMedPubMedCentralGoogle Scholar
  8. Holstege FC, Jennings EG, Wyrick JJ, Lee TI, Hengartner CJ, Green MR, Golub TR, Lander ES, Young RA (1998) Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95(5):717–728. Transcriptomic data at http://web.wi.mit.edu/young/pub/data/orf_transcriptome.txtCrossRefPubMedGoogle Scholar
  9. Kazan K (2003) Alternative splicing and proteome diversity in plants: the tip of the iceberg has just emerged. Trends Plant Sci 8(10):468–471CrossRefPubMedGoogle Scholar
  10. Kornblihtt AR (2005) Promoter usage and alternative splicing. Curr Opin Cell Biol 17(3):262–268CrossRefPubMedGoogle Scholar
  11. Lee C, Wang Q (2005) Bioinformatics analysis of alternative splicing. Brief Bioinform 6(1):23–33CrossRefPubMedGoogle Scholar
  12. Liebler DC, TBDC L III., fb JRY, Publisher : c (2002) Introduction to proteomics: tools for the new biology. Humana Press, TotowaGoogle Scholar
  13. Lipscombe D (2005) Neuronal proteins custom designed by alternative splicing. Curr Opin Neurobiol 15(3):358–363CrossRefPubMedGoogle Scholar
  14. Madden SL, Galella EA, Zhu J, Bertelsen AH, Beaudry GA (1997) SAGE transcript profiles for p53-dependent growth regulation. Oncogene 15(9):1079–1085PubMedCrossRefGoogle Scholar
  15. Saha S, Sparks AB, Rago C, Akmaev V, Wang CJ, Vogelstein B, Kinzler KW, Velculescu VE (2002) Using the transcriptome to annotate the genome. Nat Biotechnol 20(5):508–512PubMedCrossRefGoogle Scholar
  16. Schena M (1996) Genome analysis with gene expression microarrays. BioEssays 18(5):427–431PubMedCrossRefGoogle Scholar
  17. Schena M (2003) Microarray analysis. Wiley-Liss, New YorkGoogle Scholar
  18. Schmucker D, Clemens JC, Shu H, Worby CA, Xiao J, Muda M, Dixon JE, Zipursky SL (2000) Drosophila Dscam is an axon guidance receptor exhibiting extraordinary molecular diversity. Cell 101(6):671–684CrossRefPubMedGoogle Scholar
  19. Sharp PM, Li WH (1987) The codon adaptation index – a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15(3):1281–1295CrossRefPubMedPubMedCentralGoogle Scholar
  20. Stamm S, Ben-Ari S, Rafalska I, Tang Y, Zhang Z, Toiber D, Thanaraj TA, Soreq H (2005) Function of alternative splicing. Gene 344:1–20CrossRefPubMedGoogle Scholar
  21. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1995) Serial analysis of gene expression. Science 270(5235):484–487PubMedCrossRefGoogle Scholar
  22. Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett DE Jr, Hieter P, Vogelstein B, Kinzler KW (1997) Characterization of the yeast transcriptome. Cell 88(2):243–251PubMedCrossRefGoogle Scholar
  23. Velculescu VE, Madden SL, Zhang L, Lash AE, Yu J, Rago C, Lal A, Wang CJ, Beaudry GA, Ciriello KM et al (1999) Analysis of human transcriptomes. Nat Genet 23(4):387–388CrossRefPubMedGoogle Scholar
  24. Xia X (2013) DAMBE5: a comprehensive software package for data analysis in molecular biology and evolution. Mol Biol Evol 30:1720–1728PubMedPubMedCentralCrossRefGoogle Scholar
  25. Xia X (2015) A major controversy in codon-anticodon adaptation resolved by a new codon usage index. Genetics 199:573–579CrossRefPubMedGoogle Scholar
  26. Xia X (2017d) Self-organizing map for characterizing heterogeneous nucleotide and amino acid sequence motifs. Computation 5(4):43CrossRefGoogle Scholar
  27. Zhang L, Zhou W, Velculescu VE, Kern SE, Hruban RH, Hamilton SR, Vogelstein B, Kinzler KW (1997) Gene expression profiles in normal and cancer cells. Science 276(5316):1268–1272PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  • Xuhua Xia
    • 1
  1. 1.University of Ottawa CAREG and Biology DepartmentOttawaCanada

Personalised recommendations