General Methods of Classification-Dr C R Meera

Ø    Goals of Classification

A classification system should have two qualities.

a.              Stability

b.             Predictability

Stability- Frequent changes in the classification system can cause confusion. Therefore, it is important to strive for the development of a classification system that requires only minor adjustments when new information becomes available. This will help minimize confusion and ensure stability in the classification system.

Predictability- By knowing the characteristics of one member of a taxonomic group, it should be possible to assume that the other members of the same group probably have similar characteristics.

Predictability is an important characteristic of a classification system. It refers to the ability to anticipate and understand how the system categorizes and organizes information. A predictable classification system follows consistent rules and criteria, allowing users to have a clear understanding of how entities are classified and how they relate to each other.

Ø    General Methods of Classification

1.             Intuitive method

2.             Phenetic or Phenotypic classification

3.             Phylogenetic or Phyletic classification

4.             Genotypic or Genetic classification

5.             Numerical taxonomy or Adansonian classification

 1.             Intuitive method

         In this method, a microbiologist who has been studying the properties of the organisms for several years may decide that the organisms under study represent one or more species or genera. The drawback of this system is that the characters considered important by one person may not be so important to others. So different taxonomists may arrive at very different groupings. This is a primitive method that is not in use now.  However, some schemes based on intuitive methods have proved to be useful.

2.             Phenotypic classification

      For a very long time, microbial taxonomists relied on this system. This classification system is based on the mutual similarity in the phenotypic or morphological characteristics of the organisms. This system succeeded in bringing order to biological diversity. To some extent, this system also clarified the function associated with morphological structures. For eg: Flagella and motility are associated in most organisms. So, it could be assumed that flagella are involved in at least some types of motility.

Phenetic studies can also reveal evolutionary relationships. However, this system is not dependent on phylogenetic analysis. The phenetic system is based purely on morphological characters. This system will compare as many traits as possible, giving equal weightage to all traits studied. The best phenetic classification is the one constructed by comparing as many attributes/ morphological characters as possible. Organisms sharing many characteristics (>70%) are grouped into a single taxon.

3.             Phylogenetic or Phyletic classification

      With the publication of Darwin’s “On the Origin of Species’ in 1859, biologists began to develop phylogenetic or phyletic classification system. This classification system is based on the evolutionary relationships of organisms. The term “phylogeny” means ‘evolutionary development of a species’. In Greek, ‘phylon’ means ‘tribe or race’ and ‘genesis’ means ‘origin or generation.’ However, for much of the 20^th century, microbiologists could not effectively employ phylogenetic classification systems mainly due to the lack of good fossil records. When Carl Woese and George E Fox proposed the use of rRNA nucleotide sequences to assess evolutionary relationships among microorganisms, the doors opened for phylogenetic classification systems. 16S ribosomal RNA (or 16S rRNA) is the RNA component of the 30S subunit of a prokaryotic ribosome (SSU rRNA). It binds to the Shine-Dalgarno sequence and provides most of the SSU structure. The genes coding for it are referred to as 16S rRNA gene and are used in reconstructing phylogenies, due to the slow rates of evolution of this region of the gene. The validity of this approach is widely accepted and over 2 lakh different 16S and 18S rRNA sequences are saved in the International databases-  GenBank and the Ribosomal Database Project (RDP-II).

4.             Genotypic or Genetic classification

     In the genotypic classification system, genetic similarity between the organisms is evaluated. In this method comparison of either the individual gene or whole genome (haploid set of chromosomes in a microorganism) is done. Since 1970, it is widely accepted that if the genome of prokaryotes shows similarity in more than 70%, they could belong to the same species. Microbial genomes could be compared in many ways. The simplest method is DNA Base Composition determination. Other ways include Nucleic acid hybridization, Nucleic acid sequencing, and Genomic fingerprinting (evaluation of genes that evolve more quickly than those that encode rRNA.

5.             Numerical taxonomy or Adansonian classification

     Numerical taxonomy, also known as numerical phenetics or phenetic taxonomy, is a method used in biological classification to group organisms based on their overall similarity with the help of computers. Unlike traditional taxonomy, which relies on morphological characteristics and evolutionary relationships, numerical taxonomy employs quantitative data from various attributes or characteristics of organisms. This approach aims to create classifications based on the overall similarity or dissimilarity of organisms rather than their shared ancestry.

The process of numerical taxonomy involves several steps:

1. Data Collection: Quantitative data is collected from the organisms under study. This data can include various traits such as morphological features, biochemical characteristics, physiological measurements, or genetic markers.

2. Data Standardization: The collected data is standardized to ensure compatibility and comparability across different organisms. This step may involve converting measurements to standardized units or transforming data to eliminate biases or variations.

3. Similarity or Dissimilarity Calculation: A similarity or dissimilarity matrix is constructed based on the standardized data. Different mathematical methods can be used to calculate the degree of similarity or dissimilarity between pairs of organisms. Common methods include the Jaccard coefficient or correlation coefficients.

4. Cluster Analysis: Cluster analysis is performed using the similarity or dissimilarity matrix to group organisms into clusters or taxa.

5. Taxonomic Hierarchy: The clusters obtained from the cluster analysis are organized into a taxonomic hierarchy. This hierarchy can include higher-level groupings, such as classes or orders, as well as lower-level groupings, such as genera or species.

Numerical taxonomy has been widely used in fields such as ecology, microbiology, botany, and zoology. It provides a systematic and quantitative approach to classify organisms based on overall similarities, without relying on subjective interpretations or expert judgments. However, it is important to note that numerical taxonomy does not consider evolutionary relationships explicitly, which can be a limitation in certain contexts where phylogenetic information is crucial for understanding the evolutionary history of organisms.