Numerical taxonomy of fungi and bacteria pptx

Numerical taxonomy is a classification system in biological systematics that groups organisms based on their character states using numerical methods. Developed by Robert Sokal and Peter Sneath in 1963, it employs mathematical algorithms like cluster analysis to create taxonomies objectively, rather than relying on subjective evaluations. Each organism is represented by a vector of numbers reflecting its characteristics, which are then compared to determine similarities. This method, also known as phenetics, treats all characters with equal weight and aims to classify organisms based on overall similarity, regardless of their evolutionary relationships

1. Presented by V.Sathiya Aravindan Topic Numerical taxonomy

2. Numerical Taxonomy In 1957, Sokal and Sneath They formalized the field, earning them the title of "fathers of numerical taxonomy." Foundation for numerical taxonomy

3. Timeline of Contributions 1957 Sokal & Sneath - Foundation 1963 Sokal & Sneath - Formalization 1966 Various - Cluster Analysis 1970s Various - Microbial Application 1980s Various - Computational Advances 1990s Various - Molecular Integration 2000s–Present Various – Expansion

4. Robert R. Sokal was a key figure in the development of numerical taxonomy. He authored several books on the subject, including " Principles of Numerical Taxonomy ."

5. What is Taxonomy? Taxonomy in plant pathology is the classification and identification of plant pathogens. It involves studying and categorizing these organisms based on their characteristics

6. Source:AI World School What is Numerical Taxonomy? Numerical taxonomy is a method of classifying organisms using numbers and statistics. It helps scientists understand plant diseases by analyzing many characteristics of the pathogens.

7. How Numerical Taxonomy Works 1 Collect Data Scientists gather information about many traits of the plant pathogens. 2 Assign Numbers Each trait is given a number, for example, 0 for round spores, 1 for oval spores, and 2 for elongated spores. 3 Create a Data Matrix All the numbers are put into a table, called a data matrix. Each row represents a pathogen, and each column represents a trait. 4 Calculate Similarities Scientists calculate how similar each pair of organisms is using mathematical formulas. 5 Group Organisms Based on the similarities, organisms are grouped together. The more similar they are, the closer they are placed in the group.

8. Table Explanation Pathogen Spore Size (µm) Colony Colour Growth Rate (mm/day) A 5 White 10 B 7 Yellow 12 C 5 White 11

9. Let us Assume Pathogen A – Fusarium equiseti Pathogen B- Fusarium moniliformae Pathogen C – Fusarium oxysporum f.sp cubense You assign numbers to the traits: Spore Size: 5 = 0, 7 = 1 Colony Colour: White = 0, Yellow = 1 Growth Rate: 10 = 0, 11 = 1, 12 = 2

10. Fungus Colony Color Colony Texture Spore Shape Microconidia Macroconidia Spore Size (µm) Host Range Fusarium oxysporum White Cottony Sickle-shaped Ellipsoidal Fusiform 5–7 Tomatoes Fusarium graminearum Pink Powdery Elliptical Oval Fusiform 6–8 Wheat, Barley Fusarium solani Yellow Velvety Oval Globose Fusiform 7–9 Potatoes, Peas, Fusarium verticillioides Purple Slimy Cylindrical Filamentous Fusiform 8–10 Corn, Sorghum, Rice Fusarium avenaceum Red Fluffy Needle-like Ellipsoidal Fusiform 6–8 Carrots, Parsnips, Celery Fusarium moniliforme Brown Woolly Fusiform Ovoid Filiform 5–7 Maize, Sorghum, Millet Fusarium graminum Orange Granular Oblong Globose Fusiform 7–9 Grasses, Cereals Fusarium culmorum Gray Wrinkled Ellipsoidal Ellipsoidal Fusiform 6–8 Wheat, Barley, Rye Fusarium poae Green Fuzzy Ovoid Globose Fusiform 5–7 Grasses, Cereals Fusarium sporotrichioides Blue Silky Filamentous Ellipsoidal Fusiform 7–9 Various crops, especially grains

11. Trait Code Description Colony Colour White 1 Pink 2 Yellow 3 Purple 4 Red 5 Brown 6 Orange 7 Gray 8 Green 9 Blue Colony Texture Cottony 1 Powdery 2 Velvety 3 Slimy 4 Fluffy 5 Woolly 6 Granular 7 Wrinkled 8 Fuzzy 9 Silky Microconidia Ellipsoidal 1 Oval 2 Globose 3 Filamentous Macroconidia Fusiform 1 Ovoid Table for trait coding

12. Datasets for Fusarium traits numbering Fungus Colony Colour Colony Texture Microconidia Macroconidia Spore Size (µm) Host Range Fusarium oxysporum 0 (White) 0 (Cottony) 0 (Ellipsoidal) 0 (Fusiform) 5–7 Tomatoes Fusarium graminearum 1 (Pink) 1 (Powdery) 1 (Oval) 0 (Fusiform) 6–8 Wheat, Barley Fusarium solani 2 (Yellow) 2 (Velvety) 2 (Globose) 0 (Fusiform) 7–9 Potatoes, Peas Fusarium verticillioides 3 (Purple) 3 (Slimy) 3 (Filamentous) 0 (Fusiform) 8–10 Corn, Sorghum, Rice Fusarium avenaceum 4 (Red) 4 (Fluffy) 0 (Ellipsoidal) 0 (Fusiform) 6–8 Carrots, Parsnips, Celery Fusarium moniliforme 5 (Brown) 5 (Woolly) 1 (Ovoid) 1 (Ovoid) 5–7 Maize, Sorghum, Millet Fusarium graminum 6 (Orange) 6 (Granular) 2 (Globose) 0 (Fusiform) 7–9 Grasses, Cereals Fusarium culmorum 7 (Gray) 7 (Wrinkled) 0 (Ellipsoidal) 0(Fusiform) 6–8 Wheat, Barley, Rye Fusarium poae 8 (Green) 8 (Fuzzy) 2 (Globose) 0 (Fusiform) 5–7 Grasses, Cereals Fusarium sporotrichioides 9 (Blue) 9 (Silky) 0 (Ellipsoidal) 0 (Fusiform) 7–9 Various crops, especially grains

13. Euclidean Distance: Euclidean distance measures the straight-line distance between two points in n-dimensional space. It is the most common distance metric used to determine how similar or different two points are.

14. Manhattan Distance: Manhattan distance (also known as L1 distance or taxicab distance) M easures the distance between two points by summing the absolute differences of their coordinates.

15. Jaccard Index (Jaccard Similarity Coefficient): The Jaccard index measures the similarity between two sets by dividing the size of their intersection by the size of their union. It ranges from 0 (no similarity) to 1 (identical sets)

16. Similarity Coefficient (Cosine Similarity): Cosine similarity measures the cosine of the angle between two non-zero vectors in an nnn -dimensional space. I t indicates how similar the two vectors are, with 1 being identical and 0 being completely dissimilar.

17. OTUs – Operational Taxonomical Units OTUs emerge from clusters of organisms that exhibit a certain level of similarity. Each OTU represents a taxonomic unit, whether at the species, genus, or higher level.         Steps in Operational Taxonomical Units Practical Example: In Vitro Culture Studies of  Fusarium udum  and  Fusarium solani Setting the Scene: Our laboratory focuses on understanding soil-borne fungal pathogens affecting crop plants. Specifically, we’re intrigued by  Fusarium udum  and  Fusarium solani , notorious culprits causing root rot and damping-off diseases in various crops. In Vitro Culture Techniques: We isolate fungal spores from infected plant tissues—perhaps from wilted chickpea plants or stunted tomato seedlings. These spores become our starting point for in vitro cultures. We place them on specialized agar media—nutrient-rich petri dishes that mimic their natural habitat. Defining OTUs in the Lab: Our goal: To understand how these fungi behave under controlled conditions. We monitor their growth, mycelial morphology, and spore production. If two fungal isolates exhibit similar characteristics, we group them into OTUs. OTU 1:  Fusarium udum  isolates OTU 2:  Fusarium solani  isolates

18. Thank You