PPDB: A Tool for Investigation of Plants Physiology Based on Gene Ontology

  • Ajay Shiv SharmaEmail author
  • Hari Om Gupta
  • Rajendra Prasad
Original Research Article


Representing the way forward, from functional genomics and its ontology to functional understanding and physiological model, in a computationally tractable fashion is one of the ongoing challenges faced by computational biology. To tackle the standpoint, we herein feature the applications of contemporary database management to the development of PPDB, a searching and browsing tool for the Plants Physiology Database that is based upon the mining of a large amount of gene ontology data currently available. The working principles and search options associated with the PPDB are publicly available and freely accessible online ( through a user-friendly environment generated by means of Drupal-6.24. By knowing that genes are expressed in temporally and spatially characteristic patterns and that their functionally distinct products often reside in specific cellular compartments and may be part of one or more multicomponent complexes, this sort of work is intended to be relevant for investigating the functional relationships of gene products at a system level and, thus, helps us approach to the full physiology.


Biology Bioinformatics Database Data mining Functional genomics Gene ontology Gene products Physiology Plant 



We acknowledge the gene ontology consortium and open-source community as the source and inspiration for Plants Physiology Database. The authors also acknowledge AmiGO for the sourced term images on PPDB web site. The authors are thankful to Indian Institute of Technology, Roorkee, for providing the facilities to carry out this work. The authors also recognize contributions made by Dr. Petar M. Mitrasinovic. Ajay Shiv Sharma thanks to All India Council for Technical Education, Ministry of Human Resource Development, Government of India, for providing financial assistance.


  1. 1.
    Banihashemi K (2009) Iranian human genome project: overview of a research process among Iranian ethnicities. Indian J Hum Genet 15:88–92CrossRefGoogle Scholar
  2. 2.
    Katzman S, Capra JA, Haussler D, Pollard KS (2011) Ongoing GC-biased evolution is widespread in the human genome and enriched near recombination hot spots. Genome Biol Evol 3:614–626CrossRefGoogle Scholar
  3. 3.
    Sharma AS, Gupta HO, Prasad R, Mitrasinovic PM (2013) Microarray database bioinformatics usher functional genomics to unveil biological knowledge underlying physiology. In: International conference on recent trends in computing, 4–5 Oct 2013. ICRTC 2013, SRM University, NCR Campus, Ghaziabad, India, pp 218–224Google Scholar
  4. 4.
    Lewis SE (2005) Gene ontology: looking backwards and forwards. Genome Biol 6(1):103CrossRefGoogle Scholar
  5. 5.
    Blake JA, Corradi J, Eppig JT, Hill DP, Richardson JE, Ringwald M (2001) Creating the gene ontology resource: design and implementation. Genome Res 11:1425–1433CrossRefGoogle Scholar
  6. 6.
    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29CrossRefGoogle Scholar
  7. 7.
    Wang JZ, Du Z, Yu PS, Chen CF (2007) An efficient online tool to search Top-N genes with similar biological functions in gene ontology database. In: BIBM 2007 international conference on bioinformatics and biomedicine, 2–4 Nov 2007. IEEE, Fremont, CA, USA, pp 406–411Google Scholar
  8. 8.
    McCarthy FM, Mahony TJ, Parcells MS, Burgess SC (2009) Understanding animal viruses using the Gene Ontology. Trends Microbiol 17:328–335CrossRefGoogle Scholar
  9. 9.
    Jiang W, Li X, Rao S, Wang L, Du L, Li C, Wu C, Wang H, Wang Y, Yang B (2008) Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements. BMC Syst Biol 2:72CrossRefGoogle Scholar
  10. 10.
    Buza JJ, Burgess SC (2008) Different signaling pathways expressed by chicken naive CD4+ T cells, CD4+ lymphocytes activated with staphylococcal enterotoxin B, and those malignantly transformed by Marek’s disease virus. J Proteome Res 7:2380–2387CrossRefGoogle Scholar
  11. 11.
    Kumar S, Buza JJ, Burgess SC (2009) Genotype-dependent tumor regression in Marek’s disease mediated at the level of tumor immunity. Cancer Microenviron 2:23–31CrossRefGoogle Scholar
  12. 12.
    Klein G (2002) Perspectives in studies of human tumor viruses. Front Biosci 7:d268–d274CrossRefGoogle Scholar
  13. 13.
    Klouche M, Carruba G, Castagnetta L, Rose-John S (2004) Virokines in the pathogenesis of cancer: focus on human herpesvirus 8. Ann Ny Acad Sci 1028:329–339PubMedGoogle Scholar
  14. 14.
    Smith SA, Kotwal GJ (2001) Virokines: novel immunomodulatory agents. Expert Opin Biol Ther 1:343–357CrossRefGoogle Scholar
  15. 15.
    Torto-Alalibo T, Collmer C, Gwinn-Giglio M (2009) The plant-associated Microbe Gene Ontology (PAMGO) consortium: community development of new Gene Ontology terms describing biological processes involved in microbe-host interactions. BMC Microbiol 9:S1CrossRefGoogle Scholar
  16. 16.
    Bassingthwaighte JB (2000) Strategies for the physiome project. Ann Biomed Eng 28:1043–1058CrossRefGoogle Scholar
  17. 17.
    Crampin EJ, Halstead M, Hunter P, Nielsen P, Noble D, Smith N, Tawhai M (2004) Computational physiology and the physiome project. Exp Physiol 89:1–26CrossRefGoogle Scholar
  18. 18.
    Meier S, Gehring C (2008) A guide to the integrated application of on-line data mining tools for the inference of gene functions at the systems level. Biotechnol J 3:1375–1387CrossRefGoogle Scholar
  19. 19.
    Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C et al (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32:D258–D261CrossRefGoogle Scholar
  20. 20.
    Diehl AD, Lee JA, Scheuermann RH, Blake JA (2007) Ontology development for biological systems: immunology. Bioinformatics 23:913–915CrossRefGoogle Scholar
  21. 21.
    Sharma AS, Gupta HO, Mitrasinovic PM (2012) From ontology-based gene function to physiological model. Curr Bioinform 7:436–446CrossRefGoogle Scholar
  22. 22.
    Tari L, Baral C, Dasgupta P (2005) Understanding the global properties of functionally-related gene networks using the Gene Ontology. In: Pacific symposium on biocomputing 2005. 4–8 Jan 2005, Fairmont Orchid, Big Island of Hawaii. PSB, Stanford, CA, USA, pp 209–220Google Scholar
  23. 23.
    Thomas PD, Mi H, Lewis S (2007) Ontology annotation: mapping genomic regions to biological function. Curr Opin Chem Biol 11:4–11CrossRefGoogle Scholar
  24. 24.
    Mutowo-Meullenet P, Huntley RP, Dimmer EC, Alam-Faruque Y, Sawford T, Martin MJ, O’Donovan C, Apweiler R (2013) Use of Gene Ontology annotation to understand the peroxisome proteome in humans. Database 2013, bas062Google Scholar
  25. 25.
    Jaiswal P, Avraham S, Ilic K, Kellogg EA, McCouch S, Pujar A, Reiser L, Rhee SY, Sachs MM, Schaeffer M et al (2005) Plant ontology (PO): a controlled vocabulary of plant structures and growth stages. Comp Funct Genom 6:388–397CrossRefGoogle Scholar
  26. 26.
    Ficklin SP, Sanderson LA, Cheng CH, Staton ME, Lee T, Cho IH, Jung S, Bett KE, Main D (2011) Tripal: a construction toolkit for online genome databases. Database 2011, bar044Google Scholar
  27. 27.
    Papanicolaou A, Heckel DG (2010) The GMOD Drupal bioinformatic server framework. Bioinformatics 26:3119–3124CrossRefGoogle Scholar
  28. 28.
    Pettifer S, Thorne D, McDermott P, Attwood T, Baran J, Bryne JC, Hupponen T, Mowbray D, Vriend G (2009) An active registry for bioinformatics web services. Bioinformatics 25:2090–2091CrossRefGoogle Scholar
  29. 29.
    Krupp M, Marquardt JU, Sahin U, Galle PR, Castle J, Teufel A (2012) RNA-Seq Atlas–a reference database for gene expression profiling in normal tissue by next-generation sequencing. Bioinformatics 28:1184–1185CrossRefGoogle Scholar
  30. 30.
    Smedley D, Schofield P, Chen CK, Aidinis V, Ainali C, Bard J, Balling R, Birney E, Blake A, Bongcam-Rudloff E et al. (2010) Finding and sharing: new approaches to registries of databases and services for the biomedical sciences. Database 2010, baq014CrossRefGoogle Scholar
  31. 31.
    Vanhee P, Reumers J, Stricher F, Baeten L, Serrano L, Schymkowitz J, Rousseau F (2010) PepX: a structural database of non-redundant protein-peptide complexes. Nucleic Acids Res 38:D545–D551CrossRefGoogle Scholar
  32. 32.
    Chakrabarti S, Cox E, Frank E, Güting RH, Han J, Jiang X, Kamber M, Lightstone SS, Nadeau TP, Neapolitan RE et al (2008) Data Mining: Know It All: Know It All. Morgan Kaufmann, Burlington, MA, USAGoogle Scholar
  33. 33.
    Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S (2009) AmiGO: online access to ontology and annotation data. Bioinformatics 25:288–289CrossRefGoogle Scholar
  34. 34.
    Renfro DP, McIntosh BK, Venkatraman A, Siegele DA, Hu JC (2012) GONUTS: the gene ontology normal usage tracking system. Nucleic Acids Res 40:D1262–D1269CrossRefGoogle Scholar
  35. 35.
    Binns D, Dimmer E, Huntley R, Barrell D, O’Donovan C, Apweiler R (2009) QuickGO: a web-based tool for Gene Ontology searching. Bioinformatics 25:3045–3046CrossRefGoogle Scholar
  36. 36.
    McIntosh BK, Renfro DP, Knapp GS, Lairikyengbam CR, Liles NM, Niu L, Supak AM, Venkatraman A, Zweifel AE, Siegele DA et al (2012) EcoliWiki: a wiki-based community resource for Escherichia coli. Nucleic Acids Res 40:D1270–D1277CrossRefGoogle Scholar

Copyright information

© International Association of Scientists in the Interdisciplinary Areas and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Ajay Shiv Sharma
    • 1
    Email author
  • Hari Om Gupta
    • 1
  • Rajendra Prasad
    • 1
  1. 1.Department of Electrical EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia

Personalised recommendations