Skip to main content
SpringerLink
Log in

The Use and Abuse of -Omes

  • Protocol
  • First Online:
Book cover Bioinformatics for Omics Data

Part of the book series: Methods in Molecular Biology ((MIMB,volume 719))

Abstract

The diverse fields of Omics research share a common logical structure combining a cataloging effort for a particular class of molecules or interactions, the underlying -ome, and a quantitative aspect attempting to record spatiotemporal patterns of concentration, expression, or variation. Consequently, these fields also share a common set of difficulties and limitations. In spite of the great success stories of Omics projects over the last decade, much remains to be understood not only at the technological, but also at the conceptual level. Here, we focus on the dark corners of Omics research, where the problems, limitations, conceptual difficulties, and lack of knowledge are hidden.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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

  1. Lederberg, J., McCray, A. T. (2001) “ome sweet” omics – a genealogical treasury of words. The Scientist 15(7), 7–8.

    Google Scholar 

  2. Somel, M., Creely, H., Franz, H., et al. (2008) Human and chimpanzee gene expression differences replicated in mice fed different diets. PLoS ONE 3, e1504.

    Article  PubMed  Google Scholar 

  3. Binder, H., Kirsten, T., Löffler, M., Stadler, P. F. (2004) The sensitivity of microarray oligonucleotide probes – variability and the effect of base composition. J Phys Chem 108, 18003–18014.

    Google Scholar 

  4. Binder, H., Preibisch, S. (2008) “Hook” calibration of GeneChipmicroarrays: theory and algorithm. Alg Mol Biol 3, 12.

    Article  Google Scholar 

  5. Tomancak, P., Beaton, A., Weiszmann, R., et al. (2002) Systematic determination of patterns of gene expression during Drosophila embryogenesis. Genome Biol 3, R0088.

    Article  Google Scholar 

  6. Pieper, U., Eswar, N., Webb, B. M., et al. (2009) MODBASE, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res 37, D347–D354.

    Article  PubMed  CAS  Google Scholar 

  7. Ruby, J. G., Jan, C., Player, C., et al. (2006) Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans. Cell 127, 1193–1207.

    Article  PubMed  CAS  Google Scholar 

  8. Gingeras, T. R. (2009) Implications of chimaeric non-co-linear transcripts. Nature 461, 206–211.

    Article  PubMed  CAS  Google Scholar 

  9. Li, H., Wang, J., Ma, X., Sklar, J. (2009) Gene fusions and RNA trans-splicing in normal and neoplastic human cells. Cell Cycle 8, 218–222.

    Article  PubMed  CAS  Google Scholar 

  10. The ENCODE Project Consortium (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816.

    Article  Google Scholar 

  11. Pennisi, E. (2003) A low number wins the genesweep pool. Science 300, 1484.

    Article  PubMed  CAS  Google Scholar 

  12. Prohaska, S. J., Stadler, P. F. (2008) “Genes” Theory Biosci 127, 215–221.

    Article  PubMed  Google Scholar 

  13. Douaud, M., Fève, K., Gerus, M., et al. (2008) Addition of the microchromosome GGA25 to the chicken genome sequence assembly through radiation hybrid and genetic mapping. BMC Genomics 9, 129.

    Article  PubMed  Google Scholar 

  14. Scheibye-Alsing, K., Hoffmann, S., Frankel, A. M., et al. (2009) Sequence assembly. Comp Biol Chem 33, 121–136.

    Article  CAS  Google Scholar 

  15. Richardson, M. K., Crooijmans, R. P., Groenen, M. A. (2007) Sequencing and genomic annotation of the chicken (Gallus gallus) Hox clusters, and mapping of evolutionarily conserved regions. Cytogenet Genome Res 117, 110–119.

    Article  PubMed  CAS  Google Scholar 

  16. Katz, L. A. (2005) Evolution and implications of genome rearrangements in ciliates. J Euk Microbiol 52, 7S–27S.

    Article  Google Scholar 

  17. Duharcourt, S., Lepère, G., Meyer, E. (2009) Developmental genome rearrangements in ciliates: a natural genomic subtraction mediated by non-coding transcripts. Trends Genet 25, 344–350.

    Article  PubMed  CAS  Google Scholar 

  18. Smith, J. J., Antonacci, F., Eichler, E. E., Amemiya, C. T. (2009) Programmed loss of millions of base pairs from a vertebrate genome. Proc Natl Acad Sci USA 106, 11212–11217.

    Article  PubMed  CAS  Google Scholar 

  19. Schena, M., Shalon, D., Davis, R. W., Brown, P. O. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470.

    Article  PubMed  CAS  Google Scholar 

  20. Maeda, N., Kasukawa, T., Oyama, R., et al. (2006) Transcript annotation in FANTOM3: Mouse gene catalog based on physical cDNAs. PLoS Genetics 2, e62.

    Google Scholar 

  21. Drosophila 12 Genomes Consortium (2007) Evolution of genes and genomes on the Drosophila phylogeny. Nature 450, 203–218.

    Google Scholar 

  22. Hiller, M., Findeiß, S., Lein, S., et al. (2009) Conserved introns reveal novel transcripts in Drosophila melanogaster. Genome Res 19, 1289–1300.

    Article  PubMed  CAS  Google Scholar 

  23. Kapranov, P., Cheng, J., Dike, S., et al. (2007) RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 316, 1484–1488.

    Article  PubMed  CAS  Google Scholar 

  24. Dinger, M. E., Amaral, P. P., Mercer, T. R., Mattick, J. S. (2009) Pervasive transcription of the eukaryotic genome: functional indices and conceptual implications. Brief Funct Genomic Proteomic 8, 407–423.

    Article  PubMed  CAS  Google Scholar 

  25. Frise, E., Hammonds, A. S., Celniker, S. E. (2010) Systematic image-driven analysis of the spatial Drosophila embryonic expression landscape. Mol Syst Biol 6, 345.

    Article  PubMed  Google Scholar 

  26. Kumar, S., Jayaraman, K., Panchanathan, S., Gurunathan, R., Marti-Subirana, A., Newfeld, S. J. (2002) BEST: a novel computational approach for comparing gene expression patterns from early stages of Drosophila melanogaster development. Genetics 162, 2037–2047.

    PubMed  CAS  Google Scholar 

  27. Ye, J., Chen, J., Janardan, R., Kumar, S. (2008) Developmental stage annotation of Drosophila gene expression pattern images via an entire solution path for LDA. ACM Trans Knowl Discov Data 2, 1–21.

    Article  Google Scholar 

  28. Heffel, A., Stadler, P. F., Prohaska, S. J., Kauer, G., Kuska, J.-P. Process flow for classification and clustering of fruit fly gene expression patterns. In: Proceedings of the 15th IEEE International Conference on Image Processing, ICIP 2008. IEEE, 2008 721–724.

    Google Scholar 

  29. Aebersold, R., Mann, M. (2003) Mass ­spectrometry-based proteomics. Nature 422, 198–207.

    Article  PubMed  CAS  Google Scholar 

  30. Johnson, R. S., Davis, M. T., Taylor, J. A., Patterson, S. D. (2005) Informatics for protein identification by mass spectrometry. Methods 35, 223–236.

    Article  PubMed  CAS  Google Scholar 

  31. Liu, J., Bell, A. W., Bergeron, J. J. M., et al. (2007) Methods for peptide identification by spectral comparison. Proteome Sci 5, 3.

    Article  PubMed  Google Scholar 

  32. Lam, H., Deutsch, E. W., Eddes, J. S., Eng, J. K., Stein, S. E., Aebersold, R. (2008) Building consensus spectral libraries for peptide identification in proteomics. Nat Methods 5, 873–875.

    Article  PubMed  CAS  Google Scholar 

  33. Garbis, S., Lubec, G., Fountoulakis, M. (2005) Limitations of current proteomics technologies. J Chromatography A 1077, 1–18.

    Article  CAS  Google Scholar 

  34. Klein, C., Aivaliotis, M., Olsen, J. V., et al. (2007) The low molecular weight proteome of Halobacterium salinarum. J Proteome Res 6, 1510–1518.

    Article  PubMed  CAS  Google Scholar 

  35. Reinders, J., Sickmann, A. (2007) Modificomics: posttranslational modifications beyond protein phosphorylation and glycosylation. Biomol Eng 24, 169–177.

    Article  PubMed  CAS  Google Scholar 

  36. Fu, Y., Jia, W., Lu, Z., et al. (2009) Efficient discovery of abundant post-translational modifications and spectral pairs using peptide mass and retention time differences. BMC Bioinformatics 10 (Suppl 1), S50.

    Google Scholar 

  37. Schubert, W., Bonnekoh, B., Pommer, A. J., et al. (1270–1278) Analyzing proteome topology and function by automated multidimensional fluorescence microscopy. Nat Biotechnol 24, 2006.

    Google Scholar 

  38. Scalbert, A., Brennan, L., Fiehn, O., et al. (2009) Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics 5, 435–458.

    Article  PubMed  CAS  Google Scholar 

  39. Weber, M., Davies, J. J., Wittig, D., et al. (2005) Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 37, 853–862.

    Article  PubMed  CAS  Google Scholar 

  40. Fraga, M. F., Esteller, M. (2002) DNA methylation: a profile of methods and applications. Biotechniques 33, 632–649.

    PubMed  CAS  Google Scholar 

  41. Prohaska, S. J., Stadler, P. F., Krakauer, D. C. (2010) Innovation in gene regulation: the case of chromatin computation. J Theor Biol 265, 27–44.

    Google Scholar 

  42. Andersson, R., Enroth, S., Rada-Iglesias, A., Wadelius, C., Komorowski, J. (2009) Nucleosomes are well positioned in exons and carry characteristic histone modifications. Genome Res 19, 1732–1741.

    Article  PubMed  CAS  Google Scholar 

  43. Nahkuri, S., Taft, R. J., Mattick, J. S. (2009) Nucleosomes are preferentially positioned at exons in somatic and sperm cells. Cell Cycle 8, 3420–3424.

    Article  PubMed  CAS  Google Scholar 

  44. Schwartz, S., Meshorer, E., Ast, G. (2009) Chromatin organization marks exon–intron structure. Nat Struct Mol Biol 16, 990–995.

    Article  PubMed  CAS  Google Scholar 

  45. Tilgner, H., Nikolaou, C., Althammer, S., et al. (2009) Nucleosome positioning as a determinant of exon recognition. Nat Struct Mol Biol 16, 996–1001.

    Article  PubMed  CAS  Google Scholar 

  46. Apte, A., Meitei, N. S. (2010) Bioinformatics in glycomics: glycan characterization with mass spectrometric data using SimGlycan. Methods Mol Biol 600, 269–281.

    Article  PubMed  CAS  Google Scholar 

  47. Zaia, J. (2008) Mass spectrometry and the emerging field of glycomics. Chem Biol 15.

    Google Scholar 

  48. Hüttenhofer, A., Brosius, J., Bachellerie, J. P. (2002) RNomics: identification and function of small, non-messenger RNAs. Curr Opin Chem Biol 6, 835–843.

    Article  PubMed  Google Scholar 

  49. Kiemer, L., Cesareni, G. (2007) Comparative interactomics: comparing apples and pears? Trends Biotech 25, 448–454.

    Article  CAS  Google Scholar 

  50. Brückner, A., Polge, C., Lentze, N., Auerbach, D., Schlattner, U. (2009) Yeast two-hybrid, a powerful tool for systems biology. Int J Mol Sci 10, 2763–2788.

    Article  PubMed  Google Scholar 

  51. Heck, A. J. (2008) Native mass spectrometry: a bridge between interactomics and structural biology. Nat Methods 5, 927–933.

    Article  PubMed  CAS  Google Scholar 

  52. Wong, E., Wei, C. L. (2009) ChIP’ing the mammalian genome: technical advances and insights into functional elements. Genome Med 1, 89.

    Google Scholar 

  53. Yoder, S. J., Enkemann, S. A. (2009) ChIP-on-Chip analysis methods for Affymetrix tiling arrays. Methods Mol Biol 523, 367–381.

    Article  PubMed  CAS  Google Scholar 

  54. Park, P. J. (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 10, 669–680.

    Article  PubMed  CAS  Google Scholar 

  55. Licatalosi, D., Mele, A., Fak, J. J., et al. (2008) HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456, 464–469.

    Article  PubMed  CAS  Google Scholar 

  56. Khalil, A. M., Guttman, M., Huarte, M., Garber, M., et al. (2009) Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proc Natl Acad Sci USA 106, 11675–11680.

    Article  Google Scholar 

  57. Brunel, C., Romby, P. (2000) Probing RNA structure and RNA-ligand complexes with chemical probes. Methods Enzymol 318, 3–21.

    Article  PubMed  CAS  Google Scholar 

  58. Mückstein, U., Tafer, H., Hackermüller, J., Bernhard, S. B., Stadler, P. F., Hofacker, I. L. (2006) Thermodynamics of RNA-RNA binding. Bioinformatics 22, 1177–1182.

    Article  PubMed  Google Scholar 

  59. Busch, A., Richter, A., Backofen, R. (2008) IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions. Bioinformatics 24, 2849–2856.

    Article  PubMed  CAS  Google Scholar 

  60. Chitsaz, H., Salari, R., Sahinalp, S., Backofen, R. (2009) A partition function algorithm for interacting nucleic acid strands. Bioinformatics 25, i365–i373.

    Article  PubMed  CAS  Google Scholar 

  61. Huang, F. W. D., Qin, J., Reidys, C. M., Stadler, P. F. (2010) Target prediction and a statistical sampling algorithm for RNA-RNA interaction. Bioinformatics 26, 175–181.

    Article  PubMed  CAS  Google Scholar 

  62. de Leeuw, W. C., Rauwerda, M. J., Jonker, H., Breit, T. M. (2008) Salvaging affymetrix probes after probe-level re-annotation. BMC Res Notes 1, 66.

    Article  PubMed  Google Scholar 

  63. Pearson, H. (2006) Genetics: what is a gene? Nature 441, 398–401.

    Article  PubMed  CAS  Google Scholar 

  64. Pennisi, E. (2007) DNA study forces rethink of what it means to be a gene. Science 316, 1556–1557.

    Article  PubMed  CAS  Google Scholar 

  65. Gerstein, M. B., Bruce, C., Rozowsky, J. S., et al. (2007) What is a gene, post-ENCODE? History and updated definition. Genome Res 17, 669–681.

    Article  PubMed  CAS  Google Scholar 

  66. Gingeras, T. R. (2007) Origin of phenotypes: genes and transcripts. Genome Res 17, 682–690.

    Article  PubMed  CAS  Google Scholar 

  67. Scherrer, K., Jost, J. (2007) The gene and the genon concept: a conceptual and ­information-theoretic analysis of genetic ­storage and expression in the light of modern molecular biology. Theory Biosci 126, 65–113.

    Article  PubMed  CAS  Google Scholar 

  68. Stadler, P. F., Prohaska, S. J., Forst, C. V., Krakauer, D. C. (2009) Defining genes: a computational framework. Theory Biosci 128, 165–170.

    Article  PubMed  Google Scholar 

  69. Dean, A., Harris, S. E. H., Kalajzic, I., Ruan, J. (2009) A systems biology approach to the identification and analysis of transcriptional regulatory networks in osteocytes. BMC Bioinformatics 10 (Suppl 9), S5.

    Google Scholar 

  70. Laubenbacher, R., Hower, V., Jarrah, A., et al. (2009) A systems biology view of cancer. Biochim Biophys Acta 1796, 129–139.

    PubMed  CAS  Google Scholar 

  71. Engl, H. W., Flamm, C., Kügler, P., Lu, J., Müller, S., Schuster, P. (2009) Inverse problems in systems biology. Inverse Problems 25, 123014.

    Article  Google Scholar 

  72. Ciliberti, S., Martin, O. C., Wagner, A. (2007) Innovation and robustness in complex regulatory gene networks. Proc Natl Acad Sci USA 104, 13591–13596.

    Article  PubMed  CAS  Google Scholar 

  73. Missal, K., Cross, M. A., Drasdo, D. (2006) Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment. Bioinformatics 22, 731–738.

    Article  PubMed  CAS  Google Scholar 

  74. Klipp, E., Herwig, R., Kowald, A., Wierling, C., Lehrach, H. Systems Biology in Practice. Concepts, Implementation, and Application. Weinheim, DE: Wiley, 2005.

    Google Scholar 

  75. Marcus, F. Bioinformatics and Systems Biology. Berlin: Springer, 2008.

    Book  Google Scholar 

  76. Eisen, J. A., Coyne, R. S., Wu, M., et al. (2006) Macronuclear genome sequence of the ciliate Tetrahymena thermophila, a model eukaryote. PLoS Biol 4, e286.

    Article  PubMed  Google Scholar 

  77. Prescott, J. D., DuBois, M. L., Prescott, D. M. (1998) Evolution of the scrambled germline gene encoding α-telomere binding protein in three hypotrichous ciliates. Chromosoma 107, 293–303.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany

    Sonja J. Prohaska & Peter F. Stadler

Authors
  1. Sonja J. Prohaska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter F. Stadler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter F. Stadler .

Editor information

Editors and Affiliations

  1. emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, Vienna, 1180, Austria

    Bernd Mayer

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Prohaska, S.J., Stadler, P.F. (2011). The Use and Abuse of -Omes . In: Mayer, B. (eds) Bioinformatics for Omics Data. Methods in Molecular Biology, vol 719. Humana Press. https://doi.org/10.1007/978-1-61779-027-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-61779-027-0_8

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-026-3

  • Online ISBN: 978-1-61779-027-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation