Numerical taxonomy is the name given to various procedures whereby a set of individuals or units (termed as OTUs “Operational Taxonomic Unities” 1) is divided into two or more assemblages or subgroups (clusters) on the basis of a set of attributes which they share. Every OUT shows its own set of constant and variable characters. A character is defined as any property that can vary between OTUs and the values it can assume are called character states (Brenner et.al 2004).
The fundamental problem of numerical taxonomy may be summarized in the following principles (Sneath, 1958)
- The greater the content of information in the taxa of a classification and the more the characters on which it is based, the better a given classification will be.
- A priori, every character is of equal weight in creating natural taxa
- Overall similarity between any two entities is a function of their individual similarities in each of the many characters in which they are being compared
- Distinct taxa can be recognized because correlations of characters differ in the groups of organisms under study
- Phylogenetic inferences can be made from the taxonomic structures of a group and from character correlations, given certain assumptions about evolutionary pathways and mechanisms
- Taxonomy is viewed and practiced as an empirical science
- Classifications are based on phenetic similarity
Numerical taxonomy has the power to integrate data from a variety of sources, such as morphology, physiology, chemistry, affinities between DNA strands, amino acid sequences of proteins, and more. Through the automation of large portions of the taxonomic process, greater efficiency is promoted (Sokal & Sneath, 1996). Thus, much taxonomic work can be done by less highly skilled workers of automation. The data coded in numerical form can be integrated with existing electronic data processing systems in taxonomic institutions and used for the creation of descriptions, keys, catalogs, maps, and other documents. Being quantitative, the methods provide greater discrimination along the spectrum of taxonomic differences and are more sensitive in delimiting taxa. Thus they should give better classifications and keys that can be obtained by the conventional methods. The creation of explicit data taken for numerical taxonomy has already forced workers in the field to use more and better described characters. A fundamental advantage of numerical taxonomy has been the reexamination of the principles of taxonomy and of the purposes of classification. Numerical taxonomy has led to the reinterpretation of a no of biological concepts and the posing of new biological and evolutionary questions (Sneath & Sokal, 1973).
Techniques of cluster analysis can be applied readily in systematics and in many other fields of biology: ecology, treatment of quantitative biogeographical data, the recognition of various clinical forms of a disease, separation of distinctive racial groups, etc
The steps in clustering a set of data are as follows:
- The selection of the study objects.
- The selection of the characters helping to describe the objects.
- The identification of the units to be classified (objects or characters).
- The choice of the coding rules for each character and the elaboration of the object-character table.
- The choice of the clustering algorithms.
- The calculation of the arborescent graphs (or dendrograms).
- Interpretation of the results. (M. Di Bacco et al. 1994)
Cluster analysis has been used widely till now to derive phylogenetic inferences. The most important task in carrying out cladistic analysis is identifying the variables to be studied (http://www.statsoft.com/TEXTBOOK/stcluan.html ).
Parsimony is one of the methods followed for carrying out cluster analysis. It is a rule to choose amongst the number of cladogram produced and it refers to minimum number of steps taken towards to the last step of the cladogram (Bapat, G., 2010). Many software packages are available today viz. PHYLIP, PAUP, Gene Tree, Binumerics, “Biodiversity” Pro and many others (http://evolution.genetics.washington. edu/phylip/software.html). Amongst the widely used packages PAUP is used most widely today it is the most recommended package for drawing phylogenetic inferences (Blackwell et.al. 2006, Goes-Neto et.al.2001, Hibbett et.al.2007, James et.al 2006, Lamrood & Goes-Neto 2006, Larsson et.al. 2006, Kim & Jung 2002, Wagner & Fischer 2001, 2002a&b).
The principle of Maximum parsimony is also successfully applied to the qualitative mulvariate analysis using morphological parameters (Goes-Neto et.al. 2001, Kim & Jung 2002, Lamrood & Goes-Neto 2006).
Fischer (1996) was able to verify the generic status of Fomitiporia by restriction analyses of mitochondrial DNA and ribosomal DNA combined with results from sexuality, DNA content of nuclei, and karyology. Only recently, sequence data from the nuclear encoded large subunit (nuc-LSU) rDNA together with traditional characters were used to re-examine the classification and phylogenetic relationships of the Hymenochaetales (Fischer et al 2001, Niemelä et al 2001, Wagner 2001, Wagner and Fischer 2001, 2002, Wagner and Ryvarden 2002).Recently, phylogenetic analysis of sequence data of nuclear encoded small subunit (nuc-SSU) and mitochondrial encoded small subunit (mtSSU) rDNA (Hibbett and Donoghue 1995, Langer 1998, Hibbett and Thorn 2001), as well as nuc-LSU rDNA (Langer 2001) demonstrated several aphyllophoralean taxa such asHyphodontia,Schizopora,Oxyporus,Basidioradulum and Trichaptumas closely related to the Hymenochaetales. However, the relationships of these taxa have not been studied yet on the basis of nuc-LSU sequences comprising a worldwide taxon-sampling of the Hymenochaetales.
Divergent domains in the 25S rDNA were used for species identification and phylogenetic studies in yeasts (Kurtzman, 1992a and b), in Fusarium (Guadet et al., 1989), in Lyophyllum (Moncalvo et al., 1993) and in Lentinus (Hibbett and Vilgalys, 1993)
Mitochondrial DNA has been useful for evolutionary studies in fungi because of its high copy number that makes restriction fragments to be visualized clearly (Bruns et al., 1991). The mtDNA evolves faster than the nuclear DNA (Bruns and Szaro, 1992; Castro et al., 1998) that makes mtDNA RFLP analysis ideal for use when studying closely related species (Saitou and Ueda, 1994; Vincent et al., 1986). However, it is very difficult to use mtDNA when comparing distantly related species because the patterns from mtDNA RFLPs are very different (Bruns et al., 1991).
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Mitochondrial DNA (mtDNA) is attractive for evolutionary study because of its relatively small size and high copy number within cells. Although mtDNA is not variable enough to distinguish every individual, it is adequate for studies of population and species differences. Fungal mitochondrial genomes are usually circular, however, linear subgenomes are also known (Kawano et al. 1982; Wesolowski and Fukuhava 1981). Fungal mtDNA is not always inherited uniparentally and recombination has been reported to occur. The sizes of the fungal mtDNAs are very variable (18.9 Kb to 176 Kb: Clark- Walker and Sriprakash 1981; Hintz et al. 1985). The mitochondrial genomes are highly variable in size and contain much non-homologous DNA even between closely related species (Hintz et al. 1985; Weber et al. 1986) Mitochondrial DNA characters often show a strong positive correlation with other taxonomic features. For example, Garber and Yoder (1984) detected mtDNA variation in 23 isolates of Cochliobolus heterostrophus that was correlated with the distribution of mating type, nuclear rDNA RFLPs, toxin production, and geographical distribution.
The ITS Region as tool for Systematics for the identification of Phellinus
Molecular techniques are becoming more important than ever as means to study taxonomic and phylogenetic relations among fungi. The Ribosomal RNA gene (- ribosomal DNA) is a very old ancient one that all organisms have. Ribosomal RNA genes form a mosaic pattern of conserved and variable regions which makes taxonomic analysis possible at many levels. Researchers have to select the region that is most appropriate for studying their taxa at a particular level (Zambino and Szabo 1993; Moncalvo et al., 1995). Levels of sequence variability in a given region are different in different fungal taxa and no unique regions can be used to identify all fungal species to discuss phylogenetic relationships among all fungi. (Zambino and Szabo 1993; Moncalvo et al.,1995).
Regions most common1y used for phylogentic analysis are nuclear and mitochondrial small subunit RNAs, nuclear and mitochondrial large subunit RNAs, internal transcribed spacers (ITS) (Zambino and Szabo 1993). The ITS occurs between coding regions for nuclear small subunit and large subunit rRNAs and intergenic regions (IGS). The nuclear small subunit rRNA gene region may be useful among species or genera of fungi (Bruns et al., 1990). Non coding regions of the ITS may be more variable than coding regions (White et al., 1990) and are appropriate for the analysis of closely related species in the fungi (Zambino and Szabo 1993).
The nuclear ribosomal RNA gene exists in numerous copies in the nucleus. The ITS region was selected in the phylogeny of the Phellinus and related genera because it has been recognized to be appropriate in the phylogenetic study of interspecific or intraspecific level (Zambino and Szabo 1993).
Misidentification and species synonyms based on morphological identification have been reduced using the molecular techniques (Muthelo V. G. 2009). The limitations of using DNA sequence analysis arise when polymorphisms and DNA heterogeneity are observed. This implies high intra-species diversity or the presence of more than one copy of the gene in the genome. These may predate the taxa (Bunyard et al., 1996) and may cause paralogous comparisons (Bruns et al., 1991).
Ribosomal DNA in Phylogenetic Study
DNA sequence data which are most frequently used in phylogenetic analyses are 18S, 26S, ITSs and mitochondrial rDNAs because of their ubiquitous occurrence and essential function in eukaryotic cell (Carbone and Kohn, 1993; Lobuglio et al., 1994; Moncalvo el al., 1995a and 1995b: Swann and Taylor, 1993) Sequences of 18S and 26S rDNAs are very well-conserved and show more than 95% similarity among different fungal genera in the same family. They have been used in the systematics of wide range of fungi and seem not to be adequate in the systematics of genera in the same family or species in the same genus (Swann and Taylor, 1993; Wilmotte et al., 1993). On the other hand, ITS is so variable that it cannot be aligned accurately among genera and has been used in the systematics of species of a same genus (Moncalvo et al., 1995a and 1995b; Yan et al., 1995). Mitochondrial SSU rDNA sequences evolve 10 times faster than the sequences in nuclei (Bruns and Szaro, 1992). Mitochondrial SSU rDNA sequences are composed of conserved core regions, which arc alignablc across different classes, and variable domains, which evolve rapidly and are the loci of the majority of length mutations. Therefore, it seems that mtSSU rDNA sequences can be used in phylogenetic analyses between classes and between genera.
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