Skip to main content

Role and Applications of Bioinformatics in Improvement of Nutritional Quality and Yield of Crops

  • Chapter
  • First Online:
  • 636 Accesses

Part of the book series: Concepts and Strategies in Plant Sciences ((CSPS))

Abstract

Bioinformatics has the major role to play in decoding of the genomes of plants and animals. Bioinformatics is making progress in each and every field of life sciences, and similarly, the field of crop improvement has also been influenced by it. Bioinformatics allows capturing, managing, analyzing, and integrating the huge amount of metabolomics, genomics, and proteomics data enabling its efficient interpretation by the users. Bioinformatics makes available data and various tools to every individual, company, or industries so as to increase nutritional value and yield of crops. Detection of complex protein–protein interactions, modeling the protein structures, and unraveling the high-resolution genetic and physical network in plants can also be easily accomplished using in silico studies. This book chapter basically reviews the different role and applications of bioinformatics in plant breeding, gene network analysis, and molecular marker-assisted crop improvement techniques.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   169.99
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

Learn about institutional subscriptions

References

  • Atanassov A, Leunissen J, Dimov G, Nenov A, Vassilev D (2014) Application of bioinformatics in plant breeding. Biotechnol Biotechnol Equip 19(sup3):139–152

    Google Scholar 

  • Athar A, Fullgrabe A, George N, Iqbal H, Huerta L et al (2019) Arrayexpress update—from bulk to single-cell expression data. Nucleic Acids Res 47(D1):D711–D715

    Article  CAS  Google Scholar 

  • Ballabh G, Singh UK, Kushwaha B, Deo I, Jaiswal JP, Prasad B (2017) Role of bioinformatics in crop improvement. Type Double Blind Peer Rev Int Res J Publ Glob J Inc [Internet] 17(1)

    Google Scholar 

  • Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF et al (2013) NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41:D991–D995

    Article  CAS  Google Scholar 

  • Basantani MK, Gupta D, Mehrotra R, Mehrotra S, Vaish S, Singh A (2017) An update on bioinformatics resources for plant genomics research. Curr Plant Biol 11–12:33–40. Available from: https://doi.org/10.1016/j.cpb

  • Caspi R, Altman T, Billington R, Dreher K, Foerster H et al (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 42:D459–D471

    Article  CAS  Google Scholar 

  • Chaitieng B, Kaga A, Han OK, Wang YW, Wongkaew S (2002) Mapping a new source of resistance to powdery mildew in mungbean. Plant Breed 121:521–525

    Article  CAS  Google Scholar 

  • Duran Y, Vega PM (2004) Assessment of genetic variation and species relationships in a collection of lens using RAPD and ISSR. Span J Agric Res 2:538–544

    Article  Google Scholar 

  • Humphry ME, Magner CJ, Mcintyr ET, Aitken EABCL, Liu CJ (2003) Identification of major locus conferring resistance to powdery mildew in mungbean by QTL analysis. Genome 46:738–744

    Article  CAS  Google Scholar 

  • Ikeo K, Ishi-I J, Tamura T, Gojobori T, Tateno Y (2003) CIBEX: center for information biology gene EXpression database. CR Biol 326:1079–1082

    Article  CAS  Google Scholar 

  • Jennings PR (1979) Concluding remarks. In: Proceedings of the rice blast workshop. International Rice Research Institute, Manila, Philippines, pp 217–222

    Google Scholar 

  • Joshi SP, Gupta VS, Aggarwal RK, Ranjekar PK, Brar DS (2000) Genetic diversity and phylogenetic relationship as revealed by inter-simple sequence repeat (ISSR) polymorphism in the genus Oryza. Theo Appl Genet 100:1311–1320

    Article  CAS  Google Scholar 

  • Kallio MA, Tuimala JT, Hupponen T, Klemelä P, Gentile M et al (2011) Chipster: user-friendly analysis software for microarray and other high-throughput data. BMC Genom 12:507

    Article  Google Scholar 

  • Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M (2019) New approach for understanding genome variations in KEGG. Nucleic Acids Res 47:D590–D595

    Article  CAS  Google Scholar 

  • Koschmieder A, Zimmermann K, Trißl S, Stoltmann T, Leser U (2012) Tools for managing and analyzing microarray data. Brief Bioinform 13:46–60

    Article  Google Scholar 

  • Kumar A, Tiwari KL, Jadhav SK, Singh M, Datta D (2011) Microsatellite (SSR) markers for ToLCV susceptible and resistant tomato genotype identification and F^ sub 1^ purity. J Plant Sci Res 27:199

    Google Scholar 

  • Martin GB, Brommonschenkel SH, Chunwongse J, Frary A, Ganal MW et al (1993) Map-based cloning of a protein kinase gene conferring disease resistance in tomato. Science 262:1432–1436

    Article  CAS  Google Scholar 

  • Maurer M, Molidor R, Sturn A, Hartler J, Hackl H et al (2005) MARS: microarray analysis, retrieval, and storage system. BMC Bioinformatics 6:101

    Article  Google Scholar 

  • Mochida K, Shinozaki K (2010) Genomics and bioinformatics resources for crop improvement. Plant Cell Physiol 51:497–523

    Article  CAS  Google Scholar 

  • Mueller LA, Zhang P, Rhee SY (2003) AraCyc: a biochemical pathway database for arabidopsis. Plant Physiol 132:453–460

    Article  CAS  Google Scholar 

  • Naithani S, Preece J, D’Eustachio P, Gupta P, Amarasinghe V et al (2017) Plant reactome: a resource for plant pathways and comparative analysis. Nucleic Acids Res 45(D1):D1029–D1039

    Article  CAS  Google Scholar 

  • Ogawa T, Yamamoto T, Khush GS, Mew TW, Kaku H (1988) Near-isogenic lines as international differentials for resistance to bacterial blight of rice. Rice Genet Newsl 5:106–107

    Google Scholar 

  • Ogawa T, Yamamoto T, Khush GS, Mew TW (1991) Breeding of near-isogenic lines of rice with single genes for resistance to bacterial blight pathogen (Xanthomonas campestris pv oryzae). Jpn J Breed 41:523–529

    Article  Google Scholar 

  • Paine JA, Shipton CA, Chaggar S, Howells RM, Kennedy MJ, Vernon G et al (2005) Improving the nutritional value of golden rice through increased pro-vitamin A content. Nat Biotechnol 23:482–487

    Article  CAS  Google Scholar 

  • Salomonis N, Hanspers K, Zambon AC, Vranizan K, Lawlor SC et al (2007) GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics 8:217

    Article  Google Scholar 

  • Schlötterer (2004) The evolution of molecular markers—just a matter of fashion? Nature Rev Genet 5:63

    Google Scholar 

  • Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504

    Article  CAS  Google Scholar 

  • Sholihin, Hautea DM (2002) Molecular mapping of drought resistance in mungbean (Vigna radiata): 2: QTL linked to drought resistance. J Biotechnol Pertanian 7:55–61

    Google Scholar 

  • Souframanien J, Manjaya JG, Krishna TG, Pawar SE (2003) Random amplified polymorphic DNA analyses of cytoplasmic male-sterile and male fertile pigeonpea [Cajanus cajan (L.) Millsp.]. Euphytica 129:293–299

    Article  CAS  Google Scholar 

  • Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102:15545–15550

    Article  CAS  Google Scholar 

  • Sucaet Y, Deva T (2011) Evolution and applications of plant pathway resources and databases. Brief Bioinform 12:530–544

    Article  Google Scholar 

  • Tien Lea D, Duc Chua H, Quynh Lea N (2016) Improving nutritional quality of plant proteins through genetic engineering. Curr Genomics 17:220–229

    Article  Google Scholar 

  • Vallon-Christersson J, Nordborg N, Svensson M, Häkkine J (2009) BASE—2nd generation software for microarray data management and analysis. BMC Bioinformatics 10:330

    Article  Google Scholar 

  • Vassilev D, Leunissen J, Atanassov A, Nenov A, Dimov G (2005) Application of bioinformatics in plant breeding. Biotechnol Biotechnol Equip 19:139–152

    Article  Google Scholar 

  • Vilanova S, Cañizares J, Pascual L, Blanca JM, Díez MJ, Prohens J et al (2012) Application of genomic tools in plant breeding. Curr Genomics 13:179–195

    Article  Google Scholar 

  • Yoshimura S, Yoshimura A, Saito A, Kishimoto N, Kawase M et al (1992) RFLP analysis of introgressed chromosomal segments in three near-isogenic lines of rice for bacterial blight resistance genes, Xa-1, Xa-3 andXa-4. Jpn J Genet 67:29–37

    Article  CAS  Google Scholar 

  • Young ND, Danesh D, Menancio-Hautea D, Kumar L (1993) Mapping oligogenic resistance to powdery mildew in mungbean with RFLPs. Theo Appl Genet 87:243–249

    Article  CAS  Google Scholar 

  • Zhang P (2005) MetaCyc and AraCyc. Metabolic pathway databases for plant research. Plant Physiol 138:27–37

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors are thankful to DBT-BIF facility, Centre for Bioinformatics, Maharshi Dayanand University for providing the necessary resources for successful compilation of this book chapter.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anil K. Chhillar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Dangi, M., Jakhar, R., Deswal, S., Chhillar, A.K. (2019). Role and Applications of Bioinformatics in Improvement of Nutritional Quality and Yield of Crops. In: Jaiwal, P., Chhillar, A., Chaudhary, D., Jaiwal, R. (eds) Nutritional Quality Improvement in Plants. Concepts and Strategies in Plant Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-95354-0_16

Download citation

Publish with us

Policies and ethics