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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 36 issue 1 (march 2021) : 25-28,   Doi: 10.18805/BKAP265

Next Generation Sequencing Data Analysis and its Applications in Agriculture

S
Shbana Begum
R
Rahul Banerjee
1ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa-110 012, New Delhi, India.

  • Submitted15-02-2021|

  • Accepted16-06-2021|

  • First Online 26-06-2021|

  • doi 10.18805/BKAP265

Cite article:- Begum Shbana, Banerjee Rahul (2021). Next Generation Sequencing Data Analysis and its Applications in Agriculture. Bhartiya Krishi Anusandhan Patrika. 36(1): 25-28. doi: 10.18805/BKAP265.

ABSTRACT

Next Generation sequencing (NGS) technologies are revolutionizing the acquisition of genomic data at relatively low cost. NGS technologies are rapidly changing approaches to complex genomic studies and generating a vast amount of data. New and more powerful bioinformatics proposals and tools are needed to handle such large biological collections of such huge amounts of data provided by NGS data analysis. In addition, specialized software tools and advanced computational resources are required for data integration. In this article, we are describing here the main computational approach for next generation sequencing data analysis and its role in agriculture for crop improvement.

REFERENCES

  1. Andrews, K.R. and Luikart, G. (2014). Recent novel approaches for population genomics data analysis. Molecular Ecology. 23(7): 1661-1667.
  2. Del Vecchio, F., Mastroiaco, V., Di Marco, A., Compagnoni, C., Capece, D., Zazzeroni, F. and Tessitore, A. (2017). Next-generation sequencing: recent applications to the analysis of colorectal cancer. Journal of Translational Medicine. 15(1): 246.
  3. Esposito, A., Colantuono, C., Ruggieri, V. and Chiusano, M.L. (2016). Bioinformatics for agriculture in the Next-Generation sequencing era. Chemical and Biological Technologies in Agriculture. 3(1): 9.
  4. Henry, R.J. (2012). Next-generation sequencing for understanding and accelerating crop domestication. Briefings in Functional Genomics. 11(1): 51-56.
  5. Sirangelo, T.M. and Calabrò, G. (2020). Next Generation Sequencing Approach and Impact on Bioinformatics: Applications in Agri-Food Field. Journal of Bioinformatics and Systems Biology. 3: 32-44.
  6. Tang, C.Y., Hung, C.L., Zheng, H., Lin, C.Y. and Jiang, H. (2015). Novel computational technologies for next-generation sequencing data analysis and their applications.
  7. Wadapurkar, R.M. and Vyas, R. (2018). Computational analysis of next generation sequencing data and its applications in clinical oncology. Informatics in Medicine Unlocked. 11: 75-82. 
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Bhartiya Krishi Anusandhan Patrika
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    Research Article

    volume 36 issue 1 (march 2021) : 25-28,   Doi: 10.18805/BKAP265

    Next Generation Sequencing Data Analysis and its Applications in Agriculture

    S
    Shbana Begum
    R
    Rahul Banerjee
    1ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa-110 012, New Delhi, India.

    • Submitted15-02-2021|

    • Accepted16-06-2021|

    • First Online 26-06-2021|

    • doi 10.18805/BKAP265

    Cite article:- Begum Shbana, Banerjee Rahul (2021). Next Generation Sequencing Data Analysis and its Applications in Agriculture. Bhartiya Krishi Anusandhan Patrika. 36(1): 25-28. doi: 10.18805/BKAP265.

    ABSTRACT

    Next Generation sequencing (NGS) technologies are revolutionizing the acquisition of genomic data at relatively low cost. NGS technologies are rapidly changing approaches to complex genomic studies and generating a vast amount of data. New and more powerful bioinformatics proposals and tools are needed to handle such large biological collections of such huge amounts of data provided by NGS data analysis. In addition, specialized software tools and advanced computational resources are required for data integration. In this article, we are describing here the main computational approach for next generation sequencing data analysis and its role in agriculture for crop improvement.

    REFERENCES

    1. Andrews, K.R. and Luikart, G. (2014). Recent novel approaches for population genomics data analysis. Molecular Ecology. 23(7): 1661-1667.
    2. Del Vecchio, F., Mastroiaco, V., Di Marco, A., Compagnoni, C., Capece, D., Zazzeroni, F. and Tessitore, A. (2017). Next-generation sequencing: recent applications to the analysis of colorectal cancer. Journal of Translational Medicine. 15(1): 246.
    3. Esposito, A., Colantuono, C., Ruggieri, V. and Chiusano, M.L. (2016). Bioinformatics for agriculture in the Next-Generation sequencing era. Chemical and Biological Technologies in Agriculture. 3(1): 9.
    4. Henry, R.J. (2012). Next-generation sequencing for understanding and accelerating crop domestication. Briefings in Functional Genomics. 11(1): 51-56.
    5. Sirangelo, T.M. and Calabrò, G. (2020). Next Generation Sequencing Approach and Impact on Bioinformatics: Applications in Agri-Food Field. Journal of Bioinformatics and Systems Biology. 3: 32-44.
    6. Tang, C.Y., Hung, C.L., Zheng, H., Lin, C.Y. and Jiang, H. (2015). Novel computational technologies for next-generation sequencing data analysis and their applications.
    7. Wadapurkar, R.M. and Vyas, R. (2018). Computational analysis of next generation sequencing data and its applications in clinical oncology. Informatics in Medicine Unlocked. 11: 75-82. 
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