1. Introduction

DNA sequencing methodology has become one of the most widely used techniques in molecular biology and DNA sequences submitted to databases has increased exponentially each year, resulting in an enormous increase in the size and amount of data generated[1]. Phylogenetic analysis of molecular sequences is indispensible in the areas of Systematic and Evolutionary Biology and DNA sequences are important resources for phylogenetic analysis. It is becoming a very common procedure to analyze relationships within a taxonomic group by isolating sequenced DNA and constructing the phylogeny. Methods for constructing phylogenies have been developed by the discipline of Phylogenetics or Cladistics[2].

Most of the methods are based on multiple alignment of DNA sequences and calculation of distances (proportional to insertions, deletions and mutations) between these sequences. Once the distance matrix is obtained, one can choose the appropriate clustering method to obtain the phylogenetic tree.

Unfortunately quality of the multiple alignment obtained usually vary with the alignment parameters (gap opening and extension penalties) and often an expert manipulation step is required to obtain a reasonable alignment and thus areasonable distance matrix.

In this work, we propose to use Relative complexity Measure method to find the distances between the sequences without needing to align them. We also clustered the DNA sequences based on their physico-chemical properties to see which of the clustering methods, if any, yielded to a reasonable phylogenetic tree. The innovative concepts brought to this domain within this work are:

1.1. Physico-chemical Properties of DNA

DNA is a molecule that can store genetic information, have this information expressed, and have the information precisely duplicated. Information, in its most restricted technical meaning, is an ordered sequence of symbols, whether that sequence is chain of bases or computer 0’s and 1’s.

The DNA is a double-stranded anti-parallel deoxyribose nucleic acid sequences in a double-helix and composed of four different types of bases: Adenine (A), Thymine (T), Guanine (G) and Cytosine (C) (A wider description can be found in[3].) Bases differ in each other by their physico-chemical properties. In this way, they represent different symbols in the sequence.

DNA has physical and chemical properties which depend on the distribution of nucleotides A, T, G, C. Nucleotides have different physico-chemical properties according to their molecular structure. The effect of difference in physico-chemical properties between amino acids plays a significant role in determining the rate of codon substitution [4]. Nucleotides are grouped according to physico-chemical properties. A frequently used grouping property is having either a Purine or Pyrimidine base. Purine bases are A and G, whereas Pyrimidine bases are C and T (Figure 1). Another property is Hydrogen bonding between two complementary bases. Triple Hydrogen bonds formed between G and C while A and T form two bonds. Third property, bases A and C are 6-amino bases, whereas G and T are 6-keto bases[5].

1.2. Relative Complexity Measure Method

Relative Complexity Measure is a recently introduced phylogeny construction method that does not require an alignment procedure, and does not consider an evolutionary assumption – which are said to be the odds of current phylogeny programs[6]. In the recent works[8,9], RCM has found to be successful in constructing phylogenies of several taxonomic groups based on molecular sequences from different parts of genome.

RCM computes organism relatedness based on the overall complexity of the sequences. However, the measure of complexity critically depends on the alphabet used to describe the sequence[9]. By using different physico-chemical properties of DNA we can group nucleotides and represent them as reduced alphabets. In this work, we propose to compare effects of physico-chemical features of DNA on phylogeny construction by RCM method using three reduced alphabets.

2. Materials and Method

2.1. Molecular Sequences

The most common clinically important Candida species were chosen for the phylogenetic analyse of sequences. 396-bp region of the mitochondrial cytochrome b gene has been obtained from[10]. Selected Candida species are C. albicans, C. glabrata, C. parapsilosis, C. tropicalis, C. krusei, C. lusitaniae, C. dubliniensis and an outgroup Filobasidiella neoformans were used as operational taxonomic units.

Table 1 lists all sequences used in this study with their GenBank accession numbers. Candida data is also used in the study of utilization of RCM method in[7]. Information on sequencing, fungal strains, strain origins and some other information of used sequences can be reached from Yokoyama et al[10].

2.2. Reduced Alphabets

Three reduced alphabets have been generated according to the physico-chemical properties of DNA (Table 2). Sequence compositional complexity profiles are here decomposed into partial profiles using the branching property of the Shannon entropy[11]. Thus, a regular DNA sequence composed of ACGT’s would be converted into a sequence of 0’s and 1’s.

Table 2 lists the reduced alphabets used in this study and grouping of the bases. A sample outputs for a simple sequence were tabulated in Table 3 with respect to corresponding reducing alphabet.

2.3. Phylogenetic Analysis

Phylogenetic analyses of molecular sequences were performed by using Relative Complexity Measure method. Method was explained in Otu and Sayood in detail[6]. Small modifications on RCM allowed us to analyze more than one sequence at a time. RCM can search for occurrence of a subsequence within the complementary string, but in current situation, it will create history for more than one sequence synchronous.

Figure 1. Physico-chemical properties of nucleotides. Bonding and Purine/Pyrimidine structure are at the left side. Keto/Amino groups are at the right side of the Figure

Table 1. Fungal strains and accession numbers of cytochrome b genes sequenced in this study Species Accession no. Candida albicans AB044909 Candida albicans AB044910 Candida albicans AB044911 Candida albicans AB044918 Candida albicans AB044919 Candida dubliniensis AB044912 Candida dubliniensis AB044913 Candida dubliniensis AB044914 Candida glabrata AB044920 Candida glabrata AB044921 Candida glabrata AB044922 Candida glabrata AB044915 Candida krusei AB044924 Candida krusei AB044923 Candida krusei AB044925 Candida lusitaniae AB044926 Candida lusitaniae AB044927 Candida parapsilosis AB044928 Candida parapsilosis AB044929 Candida parapsilosis AB044916 Candida tropicalis AB044930 Candida tropicalis AB044917 Candida tropicalis AB044931 Candida tropicalis AB044932 Candida tropicalis AB044933 Filobasidiella neoformans AB040656

Table 2. Reduced alphabets, abbreviation and groupings Grouping factor Code Groups Puine-Pyrimidine A {A, G} {T, C} Complementary pairing 1 {A, T} {G, C} Keto-Amino bases X {A, C} {G, T}

Table 3. A sample sequence and corresponding outputs Alphabet Output Original sequence AGTGGTCACCTGATCAGTGGTCACCTGATC Purine-Pyrimidine 001001101110011001001101110011 Complementary pairing 010110101101001010110101101001 Tautomeric form 011111000011010011111000011010

We have used Unweighted Pair-Group Method with Arithmetic mean (UPGMA) tree construction method in NEIGHBOR routine of Phylogeny Inference Package (PHYLIP) version 3.69[12]. Resulting phylogenies were drawn by using FigTree version 1.3.1.

Distances among produced phylogenies were calculated by TreeDist routine of PHYLIP with Symmetric distance option. Symmetric distance is used to calculate topology distances among phylogenies, not the branch length distances.

Figure 2. Pseudo-code that describes how to combine A1X sequence from reduced alphabets

Phylogeny names are abbreviated as ORG = original sequence, A = Purine/Pyrimidine grouping, 1 = double/triple bond grouping, X = Keto/Amin grouping. Other abbreviations are combinations of them with ‘AND’ or ‘OR’. As an example, A1X distance is generated using all sequences and while computing distance if any of A or 1 or X stops reading from history, all stop and increase distance by 1 (Figure 2).

For 1X', sequences 1 and X were used and RCM distance score calculated by copying from history until all sequences stop copying.

3. Results and Discussion

Table 4. Matrix of Topology Distances between Tree Pairs

The three different reduced alphabets had been extracted by using physico-chemical properties of DNA sequences from Candida species. Phylogenetic distance matrices for each set of reduced alphabet sequence were calculated by relative complexity measure method. Since UPGMA was accepted as the best method for constructing phylogenetic trees based on cytochrome b sequences of fungi[13], phylogenetic trees were calculated by UPGMA technique. Graphical representations of the resulting phylogenies were plotted by FigTree in Figure 3. A distance matrix has been created by topology distance scores of each phylogeny pairs as indicated in Table 4. Figure 4 is the graphical representation of the topology distance data matrix.

Figure 3. Graphical representation of different sequences based on three reduced alphabets. (a) Original phylogeny, (b) phylogeny [A] based on purine/pyrimidine grouping, (c) phylogeny [1] based on triple/double bonding grouping, (d) phylogeny [X] based on keto/amino structure grouping, (e) phylogeny based on A and 1 and X, (f) phylogeny based on A and 1, (g) phylogeny based on A and X, (h) phylogeny based on 1 and X, (i) phylogeny based on A or 1 or X, (j) phylogeny based on A or 1, (k) phylogeny based on A or X, (l) phylogeny based on 1 or X.Phylogenies containing X also have acceptable topologies since they are all showing 2 TreeDist distance from the original phylogeny. Sequence X has completely different information than the 1 and A. The information content of the sequence is arising from transversion of bases. However, phylogenies produced by involvement of X are much more similar to original phylogeny than the two other sequences

All the phylogenies are successfully clustered similar groups under same cluster except C. tropicalis AB044917. However, it is marked in Bastola et al[7] as there might be a problem with this sequence. According to the topology distances matrix and its graphical representation, A1 is found to have the same topology as original topology, X, 1X, AX and A1X follow next in order. Combination of sequences A and 1 is expected to be the same as original nucleotide sequence and it is the closest phylogeny to ORG as in Figure 4 and Table 4.

As a general opinion, combinations of sequences have distant topology, especially in the use of or statement. As a group combination - where or statement used - gives worst results, A and 1 are located separately among them. When A and 1 are separately used, they contain half of the information content of original sequence. However, RCM does not deal with transition/transversion ratio, instead it uses relative complexity.

Figure 4. Supertree of produced phylogenies based on topology distance

4. Conclusions

Utilization of reduced alphabets based on physico-chemical properties of DNA or amino acid sequences have been assessed by bioinformaticians and molecular biologists for analysing binding domains[14], promoter prediction[15, 16], gene finding[17], Nonsynonymous Mutations in the Protein-Coding Genes[18] and many others. However, effect of physico-chemical properties on phylogeny construction was not very well covered within these studies.

In this study, we are able to understand that, reduced alphabets of DNA sequences by physico-chemical properties are to be useful in phylogeny construction. Our results have shown that combinations of reduced alphabet sequences by and give closer results to true phylogeny. Especially, combination of A (purine/pyrimidine grouping) and 1 (double/triple bonding) has given closest solution together. On the other hand, X (keto/amin grouping) gives next best solution by only itself, and fails when combined with another sequence. Nevertheless, some results were unexpected, since the changing structure of DNA.

In future work, we will consider different types of sequences from various sizes and different locations from genome while selecting molecular source. Utilization of reduced alphabets of DNA by other types of phylogeny analysis methods and use of reduced alphabets of protein sequences are also among the planned future works.