Chapter 10 Hierarchical Clustering-Based Algorithms and In Silico Techniques for Phylogenetic Analysis of Rhizobia Jyoti Lakhani, Ajay Khuteta, Anupama Choudhary, and Dharmesh Harwani 10.1 Introduction Evolution can be defined as the development of a species by divergence of it from other pre-existing species. The driving force behind evolution is natural selection in which “unfit” forms are eliminated through changes of environmental conditions or sexual selection so that only the fittest are selected (Darwin 1859). Mutation is the mechanism behind the evolution that occurs spontaneously to provide the biolog- ical diversity within a population. The development of bioinformatics tools and various in silico methods has provided very useful and fast methods to perform phylogenetic analysis. Two types of methods are most commanly used for it: distance based and character based. The distance-based methods include unweighted paired group method with arithmetic mean (UPGMA) (Murtagh 1984), minimum evolution method (ME) (Rzhetsky and Nei 1993), neighbour joining (NJ) (Saitou and Nei 1987), and Fitch–Margoliash method (FM) (Fitch and Margoliash 1967). The character-based method derives trees that optimize the distribution of the actual data pattern for each character. The most commonly used character-based methods include Maximum Parsimony (MP) method (Sober 1983) and Maximum Likelihood (ML) method (Felsenstein 1981). The criteria to J. Lakhani Department of Computer Science, Poornima University, Jaipur, India Department of Computer Science, Maharaja Ganga Singh University, Bikaner, India A. Khuteta Department of Computer Science, Poornima University, Jaipur, India A. Choudhary Department of Computer Science, Keen College, Bikaner, India D. Harwani (*) Department of Microbiology, Maharaja Ganga Singh University, Bikaner, India e-mail: dharmesh@mgsubikaner.ac.in © Springer International Publishing AG 2017 A.P. Hansen et al. (eds.), Rhizobium Biology and Biotechnology, Soil Biology 50, DOI 10.1007/978-3-319-64982-5_10 185