Genome Analysis of Species of Agricultural Interest

  • Chapter
  • First Online:
Advances in Modeling Agricultural Systems

Abstract

In recent years, the role ofbioinformatics in supporting structural and functional genomics and the analysis of the molecules that are expressed in a cell has become fundamental for data management, interpretation, and modeling. This interdisciplinary research area provides methods that aim not only to detect and to extract information from a massive quantity of data but also to predict the structure and function of biomolecules and to model biological systems of small and medium complexity. Although bioinformatics provides a major support for experimental practice, it mainly plays a complementary role in scientific research. Indeed, bioinformatics methods are typically appropriate for large-scale analyses and cannot be replaced with experimental approaches. Specialized databases, semiautomated analyses, and data mining methods are powerful tools in performing large-scale analyses aiming to (i) obtain comprehensive collections; (ii) manage, classify, and explore the data as a whole; and (iii) derive novel features, properties, and relationships. Such methods are thus suitable for providing novel views and supporting in-depth understanding of biological system behavior and designing reliable models.

The success of bioinformatics approaches is directly dependent on the efficiency of data integration and on the value-added information that it produces. This is, in turn, determined by the diversity of data sources and by the quality of the annotation they are endowed with. To fulfill these requirements, we designed the computational platform ISOLA, in the framework of the International Solanaceae Genomics Project. ISOLA is an Italian genomics resource dedicated to the Solanaceae family and was conceived to collect data produced by ‘omics' technologies. Its main features and tools are presented and discussed as an example of how to convert experimental data into biological information that in turn is the basis for modeling biological systems.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 103.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 129.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
GBP 129.99
Price includes VAT (United Kingdom)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Cullis, C.A.: Plant genomics and proteomics. Hoboken, NJ: John Wiley and Sons, pp. 214 (2004).

    Google Scholar 

  2. Heslop-Harrison, J.S., Murata, M., Ogura, Y., Schwarzacher, T., Motoyoshi, F: Polymorphisms and genomic organization of repetitive DNA from centromeric regions of Arabidopsis thaliana chromosomes. Plant Cell 11: 31–42 (2000).

    Article  Google Scholar 

  3. Britten, R.J., Kohne, D.E.: Repeated sequences in DNA . Science 161: 529–540 (1968).

    Article  Google Scholar 

  4. Zwick, M.S., Hanson, R.E., McKnight, T.D., Islam-Faridi, M.N., Stelly, D.M., et al.: A rapid procedure for the isolation of Cot1 DNA from plants. Genome 40: 138–142 (1997).

    Article  Google Scholar 

  5. EPSO: European plant science: A field of opportunities. Journal of Experimental Botany 56: 1699–1709 (2005).

    Article  Google Scholar 

  6. Iovene, M., Barone, A.., Frusciante, L.., Monti, L.., Carputo, D.: Selection for aneuploid Solanum commersonii-S. tuberosum hybrids combining low wild genome content and resistance traits. Theoretical and Applied Genetics 119: 1139–1146 (2004).

    Article  Google Scholar 

  7. Barone, A., Frusciante L.: Molecular marker-assisted selection for resistance to pathogens in tomato. In: Marker-assisted selection: Current status and future perspectives in crops, livestock, forestry and fish. E.P. Guimaraes, J. Ruane, B.D. Scherf, A. Sonnino, J.D. Dargie (eds), FAO, pp. 151–164 (2007).

    Google Scholar 

  8. Barone, A.: Molecular marker-assisted selection for potato breeding. American Journal of Potato Research 81: 111–117 (2004).

    Article  Google Scholar 

  9. Ruane, J., Sonnino A: Marker-assisted selection as a tool for genetic improvement of crops, livestock, forestry and fish in develo** countries: On overview of the issues. In: Marker-assisted selection: Current status and future perspectives in crops, livestock, forestry and fish. E.P. Guimaraes, J. Ruane, B.D. Scherf, A. Sonnino, J.D. Dargie (eds), FAO, pp. 4–13 (2007).

    Google Scholar 

  10. Varsheney, R.K, Graner, A., Sorrells, M.E: Genomics-assisted breeding for crop improvement. Trends in Plant Science 10(12): 621–630 (2005).

    Article  Google Scholar 

  11. Peleman, J.D., Rouppe van der Voort,J.: Breeding by design. Trends in Plant Science 8: 330–334 (2003).

    Article  Google Scholar 

  12. Wheeler, D.L., Smith-White, B., Chetvernin, V., Resenchuk, S., Dombrowski, S.M., et al.: Plant genome resources at the national center for biotechnology information. Plant Physiology 138: 1280–1288 (2005).

    Article  Google Scholar 

  13. Schneider, M., Bairoch, A., Wu, C.H., Apweiler, R.: Plant protein annotation in the UniProt Knowledgebase. Plant Physiology 138: 59–66 (2005).

    Article  Google Scholar 

  14. Deshpande, N., Addess, K.J., Bluhm, W.F., Merino-Ott, J.C., Townsend-Merino, W., et al.: The RCSB Protein Data Bank: A redesigned query system and relational database based on the mmCIF schema. Nucleic Acids Research 33: D233–D237 (2005).

    Article  Google Scholar 

  15. Ostell, J.: The NCBI Handbook: The Entrez Search and Retrieval System. Part 3, section 15 (2003).

    Google Scholar 

  16. Safran, M., Chalifa-Caspi, V., Shmueli, O., Olender, T., Lapidot, M., et al.: Human Gene-Centric Databases at the Weizmann Institute of Science: GeneCards, UDB, CroW 21 and HORDE . Nucleic Acids Research 31: 142–146 (2003).

    Article  Google Scholar 

  17. Hubbard, T. J. P., Aken, B.L., Beal, K., Ballester, B., Caccamo, M., et al.: Ensembl 2007. Nucleic Acids Research 35: D610–D617 (2007).

    Article  Google Scholar 

  18. Stein, L.D., Mungall, C., Shu, S., Caudy, M., Mangone, M., et al.: The generic genome browser: A building block for a model organism system database. Genome Research 12(10): 1599–1610 (2002).

    Article  Google Scholar 

  19. Kuhn, R.M., Karolchik, D., Zweig, A.S., Trumbower, H., Thomas, D.J., et al.: The UCSC Genome Browser database: Update 2007 Nucleic Acids Research. 35: D668–D673 (2007).

    Article  Google Scholar 

  20. Yamazaki, Y., Jaiswal, P.: Biological ontologies in rice databases. An introduction to the activities in Gramene and Oryzabase. Plant Cell Physiology. 46: 63–68 (2005).

    Article  Google Scholar 

  21. D 'Agostino, N., Aversano, M., Frusciante, L., Chiusano, M.L.: TomatEST database: In silico exploitation of EST data to explore expression patterns in tomato species. Nucleic Acids Research 35: D901–D905 (2007).

    Article  Google Scholar 

  22. The Wellcome Trust. Sharing Data from Large-Scale Biological Research Projects: A System of Tripartite Responsibility. Fort Lauderdale, FL: Wellcome Trust (2003).

    Google Scholar 

  23. Noel, J.P., Austin, M.B., Bomati, E.K: Structure-function relationships in plant phenylpropanoid biosynthesis. Current Opinion in Plant Biology 8: 249–253 (2005).

    Article  Google Scholar 

  24. Claverie, J.M.: Computational methods for the identification of genes in vertebrate genomic sequences. Human Molecular Genetics 6: 1735–1744 (1997).

    Article  Google Scholar 

  25. Stormo, G.D.: Gene-finding approaches for eukaryotes. Genome Research 10(4): 394–397 (2000).

    Article  Google Scholar 

  26. Davuluri, R.V., Zhang, M.Q.: Computer software to find genes in plant genomic DNA . Methods in Molecular Biology 236: 87–108 (2003).

    Google Scholar 

  27. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. Journal of Molecular Biology 215(3): 403–410 (1990).

    Google Scholar 

  28. Yao, H., Guo, L., Fu, Y., Borsuk, L.A., Wen, T.J., et al.: Evaluation of five ab initio gene prediction programs for the discovery of maize genes. Plant Molecular Biology 57(3): 445–460 (2005).

    Article  Google Scholar 

  29. Zhang, M.Q.: Computational prediction of eukaryotic protein-coding genes. Nature Reviews Genetics. 3: 698–709 (2002).

    Article  Google Scholar 

  30. Schlueter, S.D., Dong, Q., Brendel, V.: GeneSeqer@PlantGDB: Gene structure prediction in plant genomes. Nucleic Acids Research. 31: 3597–3600 (2003).

    Article  Google Scholar 

  31. Seki, M., Naruska, M., Kamiya, A., Ishida, J., Satou, M., et al.: Functional annotation of a full-length Arabidopsis cDNA collection. Science 296: 141–145 (2002).

    Article  Google Scholar 

  32. Alexandrov, N.N., Troukhan, M.E., Brover, V.V., Tatarinova, T., Flavell, R.B.,et al.: Features of Arabidopsis genes and genome discovered using full-length cDNAs. Plant Molecular Biology 60(1): 69–85 (2006).

    Article  Google Scholar 

  33. Adams M.D., Kelley, J.M., Gocayne, J.D., Dubnick, M., Polymeropoulos, M.H., et al.: Complementary DNA sequencing: Expressed sequence tags and human genome project. Science 252: 1651–1656 (1991).

    Article  Google Scholar 

  34. Zhu, W., Schlueter, S.D., Brendel, V.: Refined annotation of the Arabidopsis genome by complete expressed sequence tag map**. Plant Physiology. 132: 469–484 (2003).

    Article  Google Scholar 

  35. D ’Agostino, N., Traini, A., Frusciante, L., Chiusano, M.L.: Gene models from ESTs (GeneModelEST): An application on the Solanum lycopersicum genome . BMC Bioinformatics 8(Suppl 1): S9 (2007).

    Article  Google Scholar 

  36. Lewin, B.: Genes VIII. Upper Saddle River, NJ: Prentice Hall (2003).

    Google Scholar 

  37. Jiang, N., Bao, Z., Zhang, X., Eddy, S.R., Wessler, S.R.: Pack-MULE transposable elements mediate gene evolution in plants. Nature 431: 569–573 (2004).

    Article  Google Scholar 

  38. Morgante, M.: Plant genome organisation and diversity: The year of the junk! Current Opinion in Biotechnology 17(2): 168–173 (2006).

    Article  Google Scholar 

  39. Morgante, M., De Paoli, E., Radovic, S.: Transposable elements and the plant pan-genomes. Current Opinion in Plant Biology 10(2): 149–155 (2007).

    Article  Google Scholar 

  40. Audic, S., Claverie, J.M.: The significance of digital gene expression profiles. Genome Research 7(10): 986–995 (1997).

    Google Scholar 

  41. Ewing, R.M., Ben Kahla, A., Poirot, O., Lopez, F., Audic, S., et al.: Large-scale statistical analyses of rice ESTs reveal correlated patterns of gene expression. Genome Research 9: 950–959 (1999).

    Article  Google Scholar 

  42. Wu, X., Walker, M.G., Luo, J., Wei, L.: GBA server: EST-based digital gene expression profiling. Nucleic Acids Research 33 (Web Server issue): W673–W676 (2005).

    Article  Google Scholar 

  43. M égy, K., Audic, S., Claverie, J.M.: Heart-specific genes revealed by expressed sequence tag (EST) sampling. Genome Biology 3(12): RESEARCH0074 (2003).

    Google Scholar 

  44. Burke, J., Davison, D., Hide, W.: d2_cluster: A validated method for clustering EST and full-length cDNA sequences. Genome Research 9: 1135–1142 (1999).

    Article  Google Scholar 

  45. Pertea, G., Huang, X., Liang, F., Antonescu, V., Sultana, R., et al.: TIGR Gene Indices clustering tools (TGICL): A software system for fast clustering of large EST datasets. Bioinformatics 19: 651–652 (2003).

    Article  Google Scholar 

  46. Kalyanaraman, A., Aluru, S., Kothari, S., Brendel, V.: Efficient clustering of large EST data sets on parallel computers. Nucleic Acids Research 31: 2963–2974 (2003).

    Article  Google Scholar 

  47. Hotz-Wagenblatt, A., Hankeln, T., Ernst, P., Glatting, K.H., Schmidt, E.R., et al.: ESTAnnotator: A tool for high throughput EST annotation. Nucleic Acids Research 31: 3716–3719 (2003).

    Article  Google Scholar 

  48. D 'Agostino, N., Aversano, M., Chiusano, M.L.: ParPEST: A pipeline for EST data analysis based on parallel computing. BMC Bioinformatics 6 (Suppl 4): S9 (2005).

    Article  Google Scholar 

  49. Van Helden, J.: Regulatory sequence analysis tools. Nucleic Acids Research 31: 3593–3596 (2003).

    Article  Google Scholar 

  50. Noble, D.: The music of life. Oxford: Oxford University Press (2006).

    Google Scholar 

  51. Ge, H., Walhout, A.J., Vidal, M.: Integrating ‘omic' information: A bridge between genomics and systems biology. Trends in Genetics. 19(10): 551–560 (2003).

    Article  Google Scholar 

  52. Chong, L., Ray, L.B.: Whole-istic Biology. Science 295(1): 1661 (2002).

    Article  Google Scholar 

  53. Strömbäck, L., Hall, D., Lambrix, P.: A review of standards for data exchange within systems biology. Proteomics 7(6): 857–867 (2007).

    Article  Google Scholar 

  54. Lei, H., Duan, Y.: Improved sampling methods for molecular simulation. Current Opinion in Structural Biology 17(2): 187–191 (2007).

    Article  Google Scholar 

  55. Mueller, L.A., Tanksley, S.D., Giovannoni, J.J., van Eck, J., Stack, S., et al.: The Tomato Sequencing Project, the first cornerstone of the International Solanaceae Project (SOL). Comparative and Functional Genomics 6: 153–158 (2005).

    Article  Google Scholar 

  56. Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K.F., Itoh, M., et al.: From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Research 34: D354–D357 (2006).

    Article  Google Scholar 

  57. Fei, Z., Tang, X., Alba R., Giovannoni, J.: Tomato Expression Database (TED): A suite of data presentation and analysis tools. Nucleic Acids Research 34: D766–D770 (2006).

    Article  Google Scholar 

Download references

Acknowledgments

We wish to thank Prof. Gerardo Toraldo for useful discussions and constant support. This is the contribution DISSPAPA book 3. Part of the presented work is supported by the Agronanotech Project (Ministry of Agriculture, Italy) and by the PRIN 2006 (Ministry of Scientific Research, Italy) and is in the frame of the EU-SOL Project (European Community).

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Chiusano, M.L., D’Agostino, N., Barone, A., Carputo, D., Frusciante, L. (2009). Genome Analysis of Species of Agricultural Interest. In: Advances in Modeling Agricultural Systems. Springer Optimization and Its Applications, vol 25. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-75181-8_18

Download citation

Publish with us

Policies and ethics

Navigation