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,
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
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
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
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 !!
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
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
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
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
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
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.
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
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
Contoh: Tabel n x t
Karakter Strain Mikroba (Operational Taxonomical Unit)
A B C D E 1 + + - - -2 + - + - -3 + - - - -4 - - + - + 5 + + + + + 6 - - + + + 7 + + - - + 8 + + - + + 9 - - + - + 10 - - - + +
Komputasi nilai resemblance (similaritas):
Hasil Uji Strain B Hasil uji Strain A
+
-+ a b
Indeks similaritas:
Simple matching coefficient
a + d (SSM) = --- x 100% a + b + c + d Jaccard coefficient
a (SJ) = --- x 100% a + b + c
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 !!!
Matriks Similaritas
A B C D E A 100 B 80 100 C 30 30 100 D 40 60 50 100 E 30 50 60 70 100Clustring 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)}]
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
Konstruksi dendrogram
Hasil klasifikasi:
A B D E C 100 90 80 70 60 50 40 30Evaluasi dendrogram: Analisis korelasi ko-fenetik A B C D E A 100 B 80 100 C 30 30 100 D 40 60 50 100 E 30 50 60 70 100
