Skip to main content

Bioinformatics and In Silico 2D Gel Electrophoresis

  • Chapter
  • First Online:
Bioinformatics and the Cell
  • 2479 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ast G (2004) How did alternative splicing evolve? Nat Rev Genet 5(10):773–782

    Article  CAS  PubMed  Google Scholar 

  • 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):3396–3403

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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):117–126

    Article  CAS  PubMed  Google Scholar 

  • 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):58–62

    Article  CAS  PubMed  Google Scholar 

  • Epstein CB, Butow RA (2000) Microarray technology – enhanced versatility, persistent challenge. Curr Opin Biotechnol 11(1):36–41

    Article  CAS  PubMed  Google Scholar 

  • Gaasterland T, Bekiranov S (2000) Making the most of microarray data [news]. Nat Genet 24(3):204–206

    Article  CAS  PubMed  Google Scholar 

  • Graveley BR (2005) Mutually exclusive splicing of the insect Dscam pre-mRNA directed by competing intronic RNA secondary structures. Cell 123(1):65–73

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.txt

    Article  CAS  PubMed  Google Scholar 

  • Kazan K (2003) Alternative splicing and proteome diversity in plants: the tip of the iceberg has just emerged. Trends Plant Sci 8(10):468–471

    Article  CAS  PubMed  Google Scholar 

  • Kornblihtt AR (2005) Promoter usage and alternative splicing. Curr Opin Cell Biol 17(3):262–268

    Article  CAS  PubMed  Google Scholar 

  • Lee C, Wang Q (2005) Bioinformatics analysis of alternative splicing. Brief Bioinform 6(1):23–33

    Article  CAS  PubMed  Google Scholar 

  • Liebler DC, TBDC L III., fb JRY, Publisher : c (2002) Introduction to proteomics: tools for the new biology. Humana Press, Totowa

    Google Scholar 

  • Lipscombe D (2005) Neuronal proteins custom designed by alternative splicing. Curr Opin Neurobiol 15(3):358–363

    Article  CAS  PubMed  Google Scholar 

  • Madden SL, Galella EA, Zhu J, Bertelsen AH, Beaudry GA (1997) SAGE transcript profiles for p53-dependent growth regulation. Oncogene 15(9):1079–1085

    Article  PubMed  CAS  Google Scholar 

  • 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):508–512

    Article  PubMed  CAS  Google Scholar 

  • Schena M (1996) Genome analysis with gene expression microarrays. BioEssays 18(5):427–431

    Article  PubMed  CAS  Google Scholar 

  • Schena M (2003) Microarray analysis. Wiley-Liss, New York

    Google Scholar 

  • 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):671–684

    Article  CAS  PubMed  Google Scholar 

  • 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):1281–1295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stamm S, Ben-Ari S, Rafalska I, Tang Y, Zhang Z, Toiber D, Thanaraj TA, Soreq H (2005) Function of alternative splicing. Gene 344:1–20

    Article  CAS  PubMed  Google Scholar 

  • Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1995) Serial analysis of gene expression. Science 270(5235):484–487

    Article  PubMed  CAS  Google Scholar 

  • 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):243–251

    Article  PubMed  CAS  Google Scholar 

  • 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):387–388

    Article  CAS  PubMed  Google Scholar 

  • Xia X (2013) DAMBE5: a comprehensive software package for data analysis in molecular biology and evolution. Mol Biol Evol 30:1720–1728

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Xia X (2015) A major controversy in codon-anticodon adaptation resolved by a new codon usage index. Genetics 199:573–579

    Article  CAS  PubMed  Google Scholar 

  • Xia X (2017d) Self-organizing map for characterizing heterogeneous nucleotide and amino acid sequence motifs. Computation 5(4):43

    Article  Google Scholar 

  • 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):1268–1272

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media LLC

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Xia, X. (2018). Bioinformatics and In Silico 2D Gel Electrophoresis. In: Bioinformatics and the Cell. Springer, Cham. https://doi.org/10.1007/978-3-319-90684-3_18

Download citation

Publish with us

Policies and ethics