Biology Reference
In-Depth Information
corrected to evolutionary distances within a substitution mode (Saitou and Nei, 1987). The sequences
with the shortest distances are clustered together in a tree, where the tree length is optimized to
correspond to the distance matrix. The root is not specifi ed by a NJ tree. It is very common to identify
the root by adding a sequence to the set that is known to be more different from all the others that
is known as an out group. The position of the out group on the tree indicates the root. For example,
in the case of the human mitochondria, a chimpanzee outgroup was used to identify the root.
There are two methods under optimal criteria approach. These are maximum parsimony (MP)
and maximum likelihood (ML) methods (Nei and Kumar, 2000; Ludwig and Klenk, 2001). The MP
method uses the actual sequence data instead of distances and searches for the tree(s) with minimum
length, i.e. topology of the tree can be explained with a minimum number of transformations from
one character state to another. A number of possible trees are compared and each is given a score
that is a refl ection of minimum number of character changes (e.g. amino acid substitutions) that
would be required over evolutionary time to fi t the sequences in that tree. Comparing the number
of inferred mutations for each possible tree, we defi ne an informative site as one that favours one of
the possible trees in terms of fewer mutations. The optimal tree is considered to be the one requiring
the fewest changes, the most parsimonious tree.
ML method is similar to MP method in that possible trees are compared and given a score. The
score is based on how likely the given sequences would have evolved in a particular tree given a model
of amino acid or nucleotide substitution probabilities. It estimates the likelihood for tree topology that
could have resulted in the sequence alignment under the given model of evolution and searches for
the tree with maximum likelihood (Swofford
et al
., 1996; Nei and Kumar, 2000). Bootstrapping is a
statistical method in which resamplings are made within the original multiple sequence aligned and
new data sets are made. Starting with the multiple sequence alignment it builds a random multiple
sequence alignment by sampling with replacement the different sites of the original multiple alignment.
For a large number, say 10,000, of such pseudo multiple sequence alignments a tree is built by the same
method used to build the original trees. Each node of the tree can be given a bootstrap percentage
indicating how frequently those species joined by that node group together in different trees. It is
particularly convenient because it does not require anything more than the actual data used to build
the tree. It can also be used in conjunction with any tree-building method. Based upon the 10,000 trees,
bootstrapping can be used to annotate the internal branches of the original tree. For each internal
branch, we count how many times the same particular “split” is found among the 10,000 trees. The
higher this number the more one is confi dent in the robustness of the split.
2) Molecular techniques based on 16S rRNA gene
A number of analytical methods are now available for measuring the variations in the sequences of 16S
rDNA. Pure cultures of microorganisms whose identifi cation has been made on a polyphasic approach
(that is based on morphological, biochemical, physiological, phenotypic and chemotaxonomic
criteria) can be subjected to 16S rRNA gene sequence analysis and phylogenetic relationships can
be drawn by any one of the methods described above. During recent years, ecological criteria
are given equal importance with molecular markers in the identifi cation and characterization of
strains of bacteria into ecotypes (Whitaker
et al
., 2003; Whitaker, 2006; Martiny
et al
., 2006; Staley,
1999, 2006; Cohan, 2006; Fraser
et al
., 2008). To investigate the prokaryotic diversity of a given
ecosystem, culture-independent approach known as metagenomic analysis has been developed.
According to Whitman
et al
. (1998) the prokaryotic diversity assumes greater signifi cance as their
cellular production rate is estimated to be 1.7 x 10
30
cells per year and their carbon content equals
