KLASIFIKASI. Klasifikasi:

Transkripsi

1 KLASIFIKASI Tujuan klasifikasi: - Alat penyampaian informasi - Sebagai dasar pengembangan sistem identifikasi - Mengetahui sejarah evolusi mahluk hidup (mikrobia) 1. Alat penyampaian informasi Klasifikasi:. summarizing & cataloging information about microorganisms (database). Information retrieval system (large amount). Its position in the classification system is denoted by the use of a name.. e.g. Bacillus: gram + bacteria, forms endospore under aerobic conditions B. subtilis: secret extracellular enzymes, amylase & protease, use nitrate, competent to perform transformation e

2 2. Dasar penyusunan sistem identifikasi Microorganisms must be classified into groups before identification system ca be devised For recognition of new isolates Without prior classification of strain into groups -> impossible to assign new isolates to a taxon 3. Mengetahui sejarah evolusi mikroba: Indicate the phylogenetic relationships For some, phylogeny and classification are identical Kriteria klasifikasi yang efektif To serve the purposes effectively, a classification system should: have high information content be stable be empirical be scientifically based

3 Kelemahan Klasifikasi Tradisional Tidak prediktif Tidak stabil Tidak objektif (subjektif) Alasan kelemahan: Apriory choice of characters (pengaruh Linnaeus) Subjective -> disagreement between scientists Lengthy discourse concerning the relative important of characters e.g. misguided assumption: morphological -> genera physiological -> species serological -> sub-species

4 Tipe-tipe Klasifikasi 1.Klasifikasi artifisial: tujuan khusus:. Useful for the specialist. Little value to microbiology most bacteria are excluded. artificial seldom display the natural relationship, e.g. Escherichia coli & Shigella dysenteriae: strain of the taxa share great DNA sequence relatedness, phenotypically are very similar, from every view point they are single species.. E.g. Bacillus cereus & Bacillus thuringiensis: plasmid coding for - endotoxin. monothetic ( single character) S. dysenteriae must cause dysentry in human non-pathogenic strain can not be included in this taxa

5 Klasifikasi Artfisial Based on restricted information: e.g. pathogenicity Tend to be unstable: Erwinia herbicola (plant pathologist) and Enterobacter agglomerans (clinical microbiologist) Erwinia agglomerans. Identification system derived from monothetic classification missclassification! Non-pathogenic isolates of S. dysenteriae genus Escherichia. Non-toxic, plasmid deficient strain of B. thuringiensis identified as B. cereus. Conclussion: although artificial classification have their use, as a general system of value to all microbiologists, their limitations are severe!!

6 2. Klasifikasi alami a. Fenetik b. Filogenetik a. Klasifikasi Alami Fenetik General purpose classification A system that is of value to all microbiologists Encompass all bacteria and all aspects of them Natural based on overall similarity (affinity) containing all aspects (molecular physiological habitat relationship) Phenetic: refer to similarities based on the complete organism (phenotype & genotype) as it exists at present with no reference to the evolutionary pathways or ancestry of the organism. Contrast with the term natural used in evolutionary context Polythetic: good predictivity

7 b. Klasifikasi Alami Filogenetik Natural: a unique history of decent with modification Based on phylogenetic relationship This will be congruent with phenetic if there is no parallel and convergent evolution and the rate of changes proceed constantly in all lineages Cladistic: the branching pattern that describes the pathway of ancestry of a group of organism monophyletic group (posses a homologous characters: primitive or derived characters Traditional evolutionist: classification is practised with reference to the phylogeny but without the requirement that all groups be monophyletic

8 Keunggulan klasifikasi fenetik vs filogenetik 1. Goodness of the classification: Phylogenetic classification: reflect the evolutionary pathway of the organisms it is impossible to compare with the true cladogeny Phenetic classification: less well defined, but represent the similarities between and every organism. Various statistical methods have been developed. The accuracy of the classification cannot be evaluated difficult to define the ultimate phenetic classification

9 Keunggulan Klasifikasi 2. Keterujian (Veriviability) Phylogenetic approach : difficult to verify Phenetic classification: more accesible to verification, objective and can be repeated 3. Kepraktisan (Practicalities): Phylogenetic approach: rely on gene sequences data, hybridization technology offering simple identification procedures molecular systematics Phenetic approach: can be analised to select the most diagnostic characters for delineation of groups and provide reliable identification system

10 Pilihan antara Klasifikasi fenetik dan Filogenetik Jensen (1983) suggested that the classification what is needed are: Classification that reflect what is known about the taxa Procedures for generating hypothesis about evolutionary relationships. Many systematists now agree that the two systems (phenetic & phylogenetic) should be combined as far as possible

11 KLASIFIKASI NUMERIK FENETIK (Taksonomi Adansonian) Taksonomi Numerik: pengelompokan unit takson dengan metode kuantitatif berdasarkan keeadaan sifat-sifat Perintis Aplikasi Sistematik Numerik : Peter H.A. Sneath (1957) Lima Prinsip Taksonomi Adansonian: 1.Taksonomi alami ideal: taksonomi yang mengandung informasi terbesar yaitu yang didasarkan atas sebanyak-banyaknya sifat. 2. Masing-masing sifat diberi nilai yang setara dalam mengkonstruksi taksa alami. 3. Similaritas keseluruhan (afinitas) merupakan fungsi proporsi sifat yang dimiliki bersama. 4. Taksa yang berbeda didasarkan atas sifat yang dimiliki. 5. Similaritas tidak besifat filogenetis.

12 Taksonomi Tradisional: monotetik karakter tunggal dipilih secara subyektif tidak dapat mengakomodasi variasi (mutan) Taksonomi Numerik: mengandung banyak informasi sebanyak-banyak karakter (politetik) dapat mengakomodasi variasi sistem simpanan informasi yang berharga sistem retrieval bagi para ilmuwan

13 Prosedur Taksonomi Numerik: 1. Pemilihan strain dan uji karakter Pemilihan strain (OTU) Pemilihan karakter Akuisisi data secara tepat Pengkodean data (data coding) 2. Evaluasi Eror Estimasi test error Komputasi resemblance Konstruksi dendrogram (pengklasteran) Evaluasi dendrogram (co-phenetic-correlation test) 3. Pendefinisian tingkat takson

14 Contoh: Tabel n x t Karakter Strain Mikroba (Operational Taxonomical Unit) A B C D E

15 Komputasi nilai resemblance (similaritas): Hasil Uji Strain B Hasil uji Strain A a b - c d

16 Indeks similaritas: Simple matching coefficient a + d (S SM ) = x 100% a + b + c + d Jaccard coefficient a (S J ) = x 100% a + b + c

17 Contoh kalkulasi SSM SSM (A-B) : a = 4; b = 2; c = 0; d = 4: SSM = 80% SSM (A-C) : a = 2; b = 4; c = 3; d = 1: SSM = 30% SSM (A-D) : a = 2; b = 4; c = 2; d = 2: SSM = 40% SSM (A-E) : a = 3; b = 3; c = 4; d = 0: SSM = 30% dan selanjutnya!!!

18 Matriks Similaritas A B C D E A 100 B C D E

19 Clustring analysis (Analisis Kluster) Sim (%) Strain Mikrobia (OTU) 100 A B C D E 90 A B C D E 80 (A, B) C D E 70 (A, B) C (D,E) 60 (A, B) C (D,E) 55 (A, B) (C)(D,E)} 50 (A, B) (C)(D,E)} 40 (A, B) } (C)(D,E)}] 30 (A, B) } (C)(D,E)}] 20 (A, B) } (C)(D,E)}] 10 (A, B) } (C)(D,E)}]

20 Algoritme Pengklasteran (Clustering Algoritm) 1. Single linkage: fusi klaster dengan nilai similaritas tertinggi 2. Average linkage: fusi klaster dengan nilai similaritas rerata (UPGMA) 3. Complete linkage: fusi klaster dengan nilai similaritas terkecil UPGMA: Unweighted Paired Group Method with Arithmetic Averages

21 Konstruksi dendrogram Hasil klasifikasi: A B D E C

22 Evaluasi dendrogram: Analisis korelasi ko-fenetik Matriks Similaritas original (X) A B C D E A 100 B C D E

23 Matriks similaritas derived from Dendrogram (Y) A B C D E A 100 B C D E

24 Analisis Kofenetik-korelasi S SM X Y X 2 Y 2 XY A-B A-C A-D A-E B-C B-D B-E C-D C-E D-E ΣX ΣY ΣX 2 ΣY 2 ΣXY

25 Koefisien Korelasi (r) (nxy XY) r = (nx 2 (X) 2 ) (n (Y 2 ) (Y) 2 ) r 60% (nilai r yang dapat diterima)