Numerical taxonomy

Numerical methods in microbial taxonomy
1st sem MSc

1. NUMERICAL TAXONOMY

2. TAXONOMY- The Introduction Taxonomy is an area of biological science which comprises three distinct, but highly interrelated disciplines that include classification, nomenclature and identification. Classification - is that share similar morphologic, physiologic and genetic traits of organisms into specific groups or taxa. KKR1116 2

3. Continued… Nomenclature - the naming of microorganisms according to established rules and guidelines provide the accepted labels by which organisms are universally recognized. Identification is the practical use of classification criteria to distinguish certain organisms from other. KKR1116 3

4. NUMERICAL TAXONOMY Numerical Taxonomy is the classification system in the biological systematics which deals with the grouping by numerical methods of taxonomic units. It is based on their character states. KKR1116 4

5. HISTORY Numerical Taxonomy also called TAXOMETRICS was developed in 1950s as a part of multivariate analysis and in parallel with the development of computer. The Numerical Taxonomic approach was first suggested by Michael Adanson, a French Botanist in 18 th century and hence is also called ADANSONIAN TAXONOMY. Sneath and Sokal published a paper- Principles of numerical taxonomy in 1963. KKR1116 5

6. Michael Adanson Sneath and Sokal KKR1116 6

7. Numerical Taxonomy Numerical Taxonomy has been broadly successful in defining homogenous clusters of strains and in integrating data of different kinds- morphological, physiological; antigenic etc. NT is concerned primarily with phenotic relationships. The basic taxonomic category is the species. KKR1116 7

8. Continued… The data needed for numerical taxonomy must be adequate in quantity and quality. Most taxonomic work with bacteria is carried out on individual strains even though species ; genera and bigger groups, may also be studied. These entities are called “OPERATIONAL TAXONOMIC UNITS” [ OTUs] KKR1116 8

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10.   Choice of operational unit (OTU):  Characters  (attribute) selection: By applying statistics to processed data percentage of similarities and percentage of dissimilarities are estimated. After comparing the characters phenogram is constructed, indicating the relative resemblances and differences between various taxa. KKR1116 10

11. Aspects of Numerical Taxonomy There are two aspects:- Construction of taxonomic groups : Individuals are selected and their characters are spotted. Larger the number of characters better is the approach. Discrimination of taxonomic groups : When the taxonomic groups chosen for the study show overlapping of characters, discrimination should be used to select them. KKR1116 11

12. Principles of Numerical Taxonomy The greater the content of information in the taxa, and more the characters taken into consideration, the better a classification system will be. Every character should be given equal weightage in creating new taxa. For comparison purpose, the similarity between any two entities is considered. KKR1116 12

13. Hey wood defined numerical taxonomy as “numerical evaluation of the similarities between groups of organism and ordering of these groups into higher ranking taxa on the basis of these similarities. In numerical taxonomy selected characters are studied using large populations. High speed computers are used for the purpose of comparision . KKR1116 13

14. Similarity Coefficient The best way to compare two OTUs is to find out the number of characters in which they are identical[ i.e., both are positive or both are negative]. The next tables can be analysed to yield similarities between OTUs by counting the number of similar characters. The matches can be expressed as a percentage or proportion symbolized as S SM [simple matching co-efficient] which can be expressed as S SM= NS/ NS+ND X 100 where, NS is the number of similar characters ND is the number of dissimilar characters. KKR1116 14

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16. The Simple Matching Coefficient Simple matching coefficient is based on all the measured characteristics, where as Jaccard coefficient [ S J] doesn’t use characteristics when the organisms being compared are both negative for the feature. For example- a length of 1 meter may be an appropriate descriptive characteristic for totally irrelavent for a microorganism. When a simple matching co-efficient is used, the inclusion of such irrelavent features may make the organisms appear more similar than they really are. KKR1116 16

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18. Similarity Matrix The similarity values between all pairs of OTUs yield a checkboard of the entries; a square table of similarities known as similarity matrix or S matrix. The entries are 100 % indicating identity and 0% indicating complete dissimilarity between OTUs [ using S SM or SJ]. The similarity between the strains is calculated. This yields a table of similarities[similarity matrix] based on the chosen set of characteristics. KKR1116 18

19. Taxonomic Structure A table of similarities does not itself make evident the taxonomic structure of the OTUs. There are two main types of analysis to reveal taxonomic structure:- Cluster analysis and Ordination analysis Cluster Analysis : To reveal the taxonomic structure, Cluster analysis is usually employed. Cluster analysis results in tree-like diagram called DENDROGRAM[ or Phenogram ]. Organisms of high similarity occur in close genomic proximity where as organisms of low similarity are separated. KKR1116 19

20. Continued… Ordination analysis:- The result of ordination diagram or taxonomic map is expressed in close similar OTUs which are placed together. DENDROGRAM-,, A dendrogram is a diagram representing a tree, this diagram usually is placed on its side with the X axis or abscissa graduated in units of similarity. The organism in the two branches share so many characteristic that the two groups are seen to be separate only after examinations of association coefficients grater than the magnitude. KKR1116 20

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22. Continued… Organisms with great similarity are grouped together and separated from dissimilar organisms and such groups of organisms are called PHENONS [sometimes called phenoms]. KKR1116 22

23. APPLICATIONS OF NUMERICAL TAXONOMY NT can be successfully used in study of various angiosperm genera like Apocynin, Chenopodium, crotalaria, Cucurbita, wheat cultivars, maize cultivars ,etc. With the help of NT similarities and differences in bacteria, along with other microorganisms can be studied. Phytochemical data from seed protein and mitochondrial DNA, RELP studies has been numerically analyse to study the interspecific variations. KKR1116 23

24. ADVANTAGES OF NUMERICAL TAXONOMY The data of conventional taxonomy is improved by NT as it utilizes better and more number of described characters. As numerical methods are more sensitive in delimiting taxa, the data obtained can be efficiently used in the construction of better keys and classification systems. Many existing biological concepts have been reinterepted in the light of numerical taxonomy. Numerical taxonomy allows more taxonomic works to be done by less highly skilled workers. KKR1116 24

25. DISADVANTAGES OF NUMERICAL TAXONOMY Proponents of biological species concept may not accept the specific limits bound by these methods. Character selection is the greatest disadvantages in this approach. If character choosen for comparision are inadequate, the statistical methods may gives less satisfactory solution. Different taxonometric procedures may yield different results. A major difficulty is to choose an procedure for the purpose and the number of characteristics needed in order to obtain satisfactory results by these mechanical aids. KKR1116 25

26. REFERENCES A. Sneath and R. Sokal, Numerical Taxonomy (San Francisco: Freeman, 1973). General Microbiology [revised] by S.B. Sullia and S. Shantharam . Plant Taxonomy by O.P. Sharma [ second edition] page no- 115 KKR1116 26

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