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Hierarchical Clustering-Based Algorithms and In Silico Techniques for Phylogenetic Analysis of Rhizobia

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Rhizobium Biology and Biotechnology

Part of the book series: Soil Biology ((SOILBIOL,volume 50))

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Abstract

There are several in silico methodologies and tools available to study evolutionary history of rhizobia. Previously, it was very difficult to study and predict evolutionary relationships between two microbial species in a laboratory using wet lab experiments due to the lack of fossil evidences of microorganisms. In previous two decades, emergence and development of bioinformatics tools resolved this problem by replacing wet experiments by in silico experiments. The present communication is a survey on phylogenetic tools and algorithms used by researchers to study phylogeny of rhizobia. Phylogenetic analysis may be considered to be a highly reliable and important bioinformatics tool in biological sciences. The importance of phylogenetic analysis lies in its simple and easy handling of data. The varied applications of phylogenetics in different fields of biology make this analysis an unconditional necessity. This chapter is a survey on the molecular phylogenetic analysis of rhizobia with reference to the contemporary in silico methods used for rhizobia. Evidences and input parameters have also been discussed.

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Authors and Affiliations

  1. Department of Computer Science, Poornima University, Jaipur, India

    Jyoti Lakhani & Ajay Khuteta

  2. Department of Computer Science, Maharaja Ganga Singh University, Bikaner, India

    Jyoti Lakhani

  3. Department of Computer Science, Keen College, Bikaner, India

    Anupama Choudhary

  4. Department of Microbiology, Maharaja Ganga Singh University, Bikaner, India

    Dharmesh Harwani

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  1. Jyoti Lakhani
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  2. Ajay Khuteta
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  3. Anupama Choudhary
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  4. Dharmesh Harwani
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Corresponding author

Correspondence to Dharmesh Harwani .

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Editors and Affiliations

  1. Sankt Augustin, NRW, Germany

    Alexander P. Hansen

  2. Amity Institute of Microbial Technology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India

    Devendra K. Choudhary

  3. National Agricultural Science Fund, Indian Council of Agricultural Research, New Delhi, India

    Pawan Kumar Agrawal

  4. Amity Institute of Microbial Technology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India

    Ajit Varma

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Lakhani, J., Khuteta, A., Choudhary, A., Harwani, D. (2017). Hierarchical Clustering-Based Algorithms and In Silico Techniques for Phylogenetic Analysis of Rhizobia. In: Hansen, A., Choudhary, D., Agrawal, P., Varma, A. (eds) Rhizobium Biology and Biotechnology. Soil Biology, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-64982-5_10

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