Bioinformatics Approaches toward Plant Breeding Programs

Main Article Content

Marina A. Ibrahim
Marina A. Shehata
Nancy N. Nasry
Mariam S. Fayez
Sabah K. Bishay
Mariam M. Aziz
Nardeen R. Ratib
Nessma K. Ahmed
Shimaa M. Ali
E. Ismail
Galal A. R. El-Sherbieny
Haitham M. A. Elsayed

Abstract

Plants are comprised of interrelated traits, where a change in one trait may cause a change in another, or in a combination of traits. Bioinformatics tenancies were distinguished by wide accessibility of computers to different aspects of genomes. Nucleic acid sequences and information from a wide range of genomes become possible through genomics. Genomics made this information accessible to further analysis and experimentation. Therefore, development of computerized models for quantitative traits used bioinformatics techniques can reduce the time and cost in creating new plant variety, and can significantly improve breeding efficiency by constructing reliable predictive estimates and identifying selectable genotypes by greatly accelerating the progress in both fundamental plant science and applied breeding research. Moreover, it clarify the function of key genes and the interaction of responsible genes. Thus, a variety software’s and web-based tools have been developed to help with these issues. So, this article highlights the functional information and tools for genome annotation, gene ontology and gene network by stating the art regarding genome assembly. In addition, we show how phenotypic data yield new trait-trait correlations by linking phenotypic data to genomic data together.

Keywords:
Genome annotation, gene ontology, gene network, genome assembly, correlation

Article Details

How to Cite
Ibrahim, M. A., Shehata, M. A., Nasry, N. N., Fayez, M. S., Bishay, S. K., Aziz, M. M., Ratib, N. R., Ahmed, N. K., Ali, S. M., Ismail, E., El-Sherbieny, G. A. R., & Elsayed, H. M. A. (2021). Bioinformatics Approaches toward Plant Breeding Programs. Asian Journal of Research and Review in Agriculture, 3(3), 5-14. Retrieved from https://globalpresshub.com/index.php/AJRRA/article/view/1100
Section
Review Papers

References

Urazaliev KR. Bioinformation Technologies in Plant Breeding. UDC. 2019;57:51-76; 57.02:001.57.

Barh D, Zambare V, Azevedo V. Omics: Applications in Biomedical, Agricultural, and Environmental Sciences. 2013;CRC Press.

Van Emon JM. The Omics Revolution in Agricultural Research. J Agr Food Chem. 2016;64(1):36-44.

Usadel B, Fernie AR. The Plant Transcriptome from Integrating Observations to Models. Front Plant Sci. 2013;4:48.

Barh D, Khan MS, Davies E. Plant Omics: The Omics of Plant Science. Springer; 2015.

Gürel F, Öztürk NZ, Uçarlı C. Transcriptomic Responses of Barley (Hordeum vulgare L.) to Drought and Salinity. In Plant Omics: Trends and Applications; 2016.

Hakeem KR, Tombuloğlu H, Tombuloğlu G. Plant Omics: Trends and Applications; 2016.

Fiorani F, Schurr U. Future Scenarios for Plant Phenotyping. Annu. Rev. Plant Biol. 2013;64:267–291.

Pound MP, Atkinson JA, Townsend AJ et al. Deep Machine Learning Provides State of The Art Performance in Image-Based Plant Phenotyping. Gigascience. 2017;6(10):1–10.

Coppens F, Wujts N, Inze D, Dhont S. Unlocking the Potential of Plant Phenotyping Data through Integration and Data Driven Approaches. Curr. Opin. Syst. Biol. 2017;4:58–63.

Thampi SM. Introduction to Bioinformatics. arXiv preprint arXiv:0911. 2009; 4230.

Shariatipour Nikwan, Bahram Heidari. Application of Bioinformatics in Plant Breeding Programmes. BAOJ Bioinfo. 2017;1(2):1-8.

Al-Khayri JM, Jain SM, Johnson DV. Advances in Plant Breeding Strategies: Breeding, Biotechnology and Molecular Tools. Springer International Publishing; 2015.

Skuse GR, Du C. Bioinformatics Tools for Plant Genomics. Int J Plant Genomics; 2008.

Anthony M. Bolger, Hendrik Poorter, Kathryn Dumschott, Marie E. Bolger, Daniel Arend, Sonia Osorio, Heidrun Gundlach, Klaus F.X. Mayer, Matthias Lange, Uwe Scholz, Bjorn Usadel. Computational Aspects Underlying Genome to Phenome Analysis in Plants. The Plant Journal. 2019;97:182–198.

Millet EJ, Welcker C, Kruijer W et al. Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios. Plant Physiol. 2016;172:749–764.

Malchikov PN, Vyushkov AA, Myasnikova MG. Formation of Models of Durum Wheat Varieties for the Middle Volga Region. Samara: Samar. Scientific center of RAS; 2009.

Cooper M, Podlich DW, Luo L. Modeling QTL Effects and MAS in Plant Breeding. In Genomics-Assisted Crop Improvement” (R. K. Varshney and R. Tuberosa, Eds.). 2007;57–95. Springer, Dordrecht, the Netherlands.

Li Xin, Chengsong Zhu, Jiankang Wang, Jianming Yu. Computer Simulation in Plant Breeding. Advances in Agronomy. 2012; 116(3):219-264.

Johnson R. Marker-Assisted Selection. Plant Breed. Rev. 2004;24(1):293–309.

Mackay TFC. The Genetic Architecture of Quantitative Traits. Annu. Rev. Genet. 2001;35:303–339.

Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114–2120.

Bolger ME, Arsova B, Usadel B. Plant genome and transcriptome annotations: from misconceptions to simple solutions. Brief. Bioinform. 2019; 19(3):437–449.

Avni R, Nave M, Barad O et al. Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. Science. 2017;357(6346): 93–97.

Luo MC, Gu YQ, Puiu D et al. Genome sequence of the progenitor of the wheat D genome Aegilops tauschii. Nature. 2017; 551:498–502.

Hackl T, Hedrich R, Schultz J, Forster F. Proovread: large scale high-accuracy PacBio correction through iterative short read consensus. Bioinformatics. 2014; 30:3004–3011.

Au KF, Underwood JG, Lee JG, Wong WH. Improving Pac-Bio long read accuracy by short read alignment. PLoS ONE. 2012; 7:e46679.

Zhang R, Calixto CPG, Marquez Y et al. A high quality Arabidopsis transcriptome for accurate transcript-level analysis of alternative splicing. Nucleic Acids Res. 2017;45(9):5061–5073.

Ezer D, Jung JH, Lan H et al. The evening complex coordinates environmental and endogenous signals in Arabidopsis. Nat. Plants. 2017;3:17087.

Zhong S, Fei Z, Chen YR et al. Single-base resolution methylomes of tomato fruit development reveal epigenome modifications associated with ripening. Nat. Biotechnol. 2013;31:154–159.

James GV, Patel V, Nordstrom KJ, Klasen JR, Salome PA, Weigel D, Schneeberger K. User guide for mapping-by-sequencing in Arabidopsis. Genome Biol. 2013; 14(6):R61.

Klap C, Yeshayahou E, Bolger AM, Arazi T, Gupta SK, Shabtai S, Usadel B, Salts Y, Barg R. Tomato facultative parthenocarpy results from SlAGAMOUS-LIKE 6 loss of function. Plant Biotechnol. J. 2017; 15(5):634–647.

Thoen MP, Davila Olivas NH, Kloth KJ et al. Genetic architecture of plant stress resistance: multi-trait genome-wide association mapping. New Phytol. 2017; 213(3):1346–1362.

Varshney RK, Nayak SN, May GD, Jackson SA. Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnol. 2009;27:522–530.

Lin T, Zhu G, Zhang J et al. Genomic analyses provide insights into the history of tomato breeding. Nat. Genet. 2014; 46:1220–12266.

Bhat JA, Ali S, Salgotra RK et al. Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Front. Genet. 2016;7:221.

Jaiswal P, Usadel B. Plant pathway databases. Methods Mol. Biol. 2016; 1374:71–87.

Lohse M, Nagel A, Herter T et al. Mercator: a fast and simple web server for genome scale functional annotation of plant sequence data. Plant, Cell Environ. 2014; 37:1250–1258.

Conesa A, Gotz S. Blast2GO: A comprehensive suite for functional analysis in plant genomics. Int. J. Plant Genomics. 2008;619832.

Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: An automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007;35:W182–W185.

Van Bel M, Proost S, Van Neste C, Deforce D, Van de Peer Y, Vandepoele K. TRAPID: an efficient online tool for the functional and comparative analysis of de novo RNA-Seq transcriptomes. Genome Biol. 2013;14:R134.

Mascher M, Gundlach H, Himmelbach A, et al. A chromosome conformation capture ordered sequence of the barley genome. Nature. 2017;544:427–433.

Van Buren R, Wai CM, Colle M et al. A near complete, chromosome-scale assembly of the black raspberry (Rubus occidentalis) genome. Gigascience. 2018; 7(8).
Available:https://doi.org/10.1093/gigascience/giy094.

Chuong EB, Elde NC, Feschotte C. Regulatory activities of transposable elements: From conflicts to benefits. Nat. Rev. Genet. 2017;18(2):71–86.

Grebennikova IG, Aleynikov AF, Stepochkin PI, Building A. Model of the Varieties of Spring Triticale On The Basis Of Modern Information Technologies. Computing Technologies. 2016;21(1):53-64.

Cui ML et al. Quantitative Control of Organ Shape by Combinatorial Gene Activity. PLoS Biol. 2010;8(11):

Available:http://dx.doi.org/10.1371/journal.pbio.1000538.=

Wang J, Wolfgang HP. Simulation Modeling in Plant Breeding: Principles and Applications. Agric. Sci. China. 2007;6(8):908-921.

Mcpherson JD. A defining decade in DNA sequencing a revolution in DNA sequencing technology has enabled new insights from thousands of genomes sequenced across taxa. Nat. Methods. 2014;11(10):10.1038/nmeth.3106.

Bassi FM et al. Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Sci. Elsevier. 2016;242:23-36.

Page GP, Coulibaly I. Bioinformatics Tools for Inferring Functional Information from Plant Microarray Data: Tools for the First Steps. Int J Plant Genomics; 2008.

Arnone MI, Davidson EH. The hardwiring of development: organization and function of genomic regulatory systems. Development. 1997;124(10): 1851-1864.

Miklos GL, Rubin GM. The role of the genome project in determining gene function: insights from model organisms. Cell. 1996; 86(4): 521-529.

Barabasi AL, Oltvai ZN. Network biology: Understanding the cell’s functional organization. Nat Rev Genet. 2004;5(2):101-113.

Blazejczyk M, Miron M, Nadon R. FlexArray: A statistical data analysis software for gene expression microarrays. Genome Quebec; 2007.

Garcia-Seco D, Chiapello M, Bracale M, Pesce C, Bagnaresi P, et al. Transcriptome and proteome analysis reveal new insight into proximal and distal responses of wheat to foliar infection by Xanthomonas translucens. Sci Rep. 2017;7.

Drăghici S. Statistics and data analysis for microarrays using R and bioconductor. CRC Press; 2011.

Poersch-Bortolon LB, Pereira JF, Nhani Junior A, Gonzáles HH, Torres GA et al. Gene expression analysis reveals important pathways for drought response in leaves and roots of a wheat cultivar adapted to rainfed cropping in the Cerrado biome. Genet Mol Biol. 2016;39(4):629-645.

Monaco MK, Stein J, Naithani S, Wei S. Dharmawardhana P et al. Gramene 2013: comparative plant genomics resources. Nucleic Acids Res. 2014;42(D1):D1193-1199.

Windram O, Madhou P, McHattie S, Hill C, Hickman R et al. Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis. The Plant Cell Online. 2012;24(9):3530-3557.

Vadez V. Crop simulation models: predicting the future of pulses. 2016;100-102.