# Comprehensive Analysis and Comparison on the Codon Usage Pattern of WholeMycobacterium tuberculosis Coding Genome from Different Area

Authors: Li Gun; Ren Yumiao; Pan Haixian; Zhang Liang
Journal: BioMed Research International
 (2018)

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## Abstract

Phenomenon of unequal use of synonymous codons inMycobacterium tuberculosis is common. Codon usage bias not only plays an important regulatory role at the level of gene expression, but also helps in improving the accuracy and efficiency of translation. Meanwhile, codon usage pattern ofMycobacterium tuberculosis genome is important for interpreting evolutionary characteristics in species. In order to investigate the codon usage pattern of theMycobacterium tuberculosis genome, 12Mycobacterium tuberculosis genomes from differentarea are downloaded from the GeneBank. The correlations between G3, GC12, whole GC content, codon adaptation index, codon bias index, and so on ofMycobacterium tuberculosis genomes are calculated. The ENC-plot, relationship between A3/(A3+T3) and G3/(G3+C3), GC12 versus GC3 plot, and the RSCU of overall/separated genomes all show that the codon usage bias exists in all 12Mycobacterium tuberculosis genomes. Lastly, relationship between CBI and the equalization of ENC shows a strong negative correlation between them. The relationship between protein length and GC content (GC3 and GC12) shows that more obvious differences in the GC content may be in shorter protein. These results show that codon usage bias existing in theMycobacterium tuberculosisgenomes could be used for further study on their evolutionary phenomenon.


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## Body

## 1. Introduction

Tuberculosis (TB) is caused byMycobacterium tuberculosisthat most often affect the lungs. Today, TB is still an unsolved problem. According to the WHO, in Oct, 2017, fact sheet pointed out that the TB is one of the top 10 causes of death worldwide [1]. It spreads from person to person through the air. One person may be infected by TB when only inhaling a few of TB germs. So, many scientists studied the characteristics including codon usage pattern [2], promoter architecture of toxin-antitoxin systems [3], drug-resistance [4], molecular epidemiology [5], protein function and immunogenicity [6], and drugs for the treatment [7] of TB. Typically, Sheen et al. studied the multiple genomes ofMycobacterium tuberculosis and found out some specific novel genes and mutations associated with pyrazinamide resistance [8]. Sun et al. and Khrustalev et al. characterize the mutations in streptomycin-resistantMycobacterium tuberculosis [9, 10]. Recently, bioinformatics tools are widely used for analyzing the TB [11].One amino acid is often encoded by more than one codon because the genetic code is redundant. This phenomenon is also known as synonymous codon usage. Many factors may affect the codon usage inMycobacterium tuberculosis, such as mutation pressure [12], gene length [13], and natural selection [14]. Most previous studies deal with the relatively smaller set ofMycobacterium tuberculosisisolates [15, 16] or the isolates from one area [17, 18]. In this paper, genetic diversity ofMycobacterium tuberculosis from 12 areas is compared and analyzed. So, the objectives of this study aim to deal with the following issues: (1) correlation between codon preference parameters such as the codon usage index (CBI), the effective number of codons (ENC), the overall GC content, the codon adaptation index (CAI), and the Frequency of Optimal Codons (FOP); (2) mutation and evolution characteristics via the relationship between ENC and the GC3 of the whole 12Mycobacterium tuberculosis genomes; (3) phylogenetic analysis via RSCU values of the separated genomes forMycobacterium tuberculosis.


## 2. Materials and Methods

The sequences ofMycobacterium tuberculosis (accession numbers in NCBI are shown in the Table 1) from Peru, Argentina, China, and so on in Homo sapiens are downloaded and examined. Specifically, coding sequences withinMycobacterium tuberculosis genome are selected according to the following: (1) containing more than 300 bases, (2) starting with the start codon, (3) number of bases being a multiple of 3, and (4) having no stop codon in the inner sequence. Thus, a total of 12965 coding sequences forMycobacterium tuberculosis are selected (originally the total of coding sequences is 24520).Table 1
Basic characteristics of sequences forMycobacterium tuberculosis.
Accessionnumber Number oftotal genes Number ofselected genes Country LWDR01000001.1 2048 1166 Argentina CP011510.1 1973 1138 China CP017596.1 2062 1154 Colombia AP017901.1 2141 933 Japan CP012506.2 2039 1018 Kazakhstan CP010968.1 1985 1083 Malaysia JKXT01000001.1 1841 1002 Mali NZ_CM002052.1 2173 1089 Panama NZ_CP023622.1 2159 1110 Peru CP008981.1 1857 1028 South Korea NZ_CM001226.1 2214 1082 Sweden AP018033.1 2028 1162 Vietnam


### 2.1. Effective Number of Codons (ENC) Analysis

ENC analysis can be used to quantify the absolute codon usage bias in coding sequences. ENC was calculated using the following formula [19]:(1)ENCcalculatied=2+9f-2+1f-3+5f-4+3f-6,where f-k  (k=2,3,4,6) is the mean of fk values for the k-fold degenerate amino acids; f-k denotes the average homozygosity for the amino acid class whose degree of codon degeneracy is k. The coefficients 9, 1, 5, and 3 denote the number of amino acids belonging to different classes. Here, fk is estimated using the formula: fk=(nS-1)/(n-1), where n is the total number of occurrences of the codons for that amino acid and S=∑ni/n2  (i=1⋯k), and ni here is the total number of occurrences of the ith codon for that amino acid. Genes for which the codon choice is only constrained by a mutation bias will lie on or just below the curve of the expected ENC values [20]. Therefore, to elucidate the relationship between GC3 and ENC values, the expected ENC values for different GC3 were calculated as follows [21]:(2)ENCexpectation=2+s+29s2+1-s2,where s represents the given GC3%.


### 2.2. RSCU Value Analysis

The RSCU values for all genes inMycobacterium tuberculosis are calculated to determine the characteristics of synonymous codon usage without the confounding influence of amino acid composition and coding sequence size of different gene samples following a previously described method [22]. The RSCU index was calculated as follows:(3)RSCU=gij∑jnigijni,where gij is the observed number of the ith codon for thejth amino acid, which has ni kinds of synonymous codons. RSCU values represent the ratio between the observed usage frequency of one codon in a gene sample and the expected usage frequency in the synonymous codon family, given that all codons for the particular amino acid are used equally. The synonymous codons with RSCU values > 1.0 have positive codon usage bias and were defined as abundant codons, whereas those with RSCU values < 1.0 have negative codon usage bias and were defined as less-abundant codons.


### 2.3. Codon Adaptation Index (CAI) Calculation

The CAI value is generally used to predict the expression level of an inbred gene (it is more suitable for unicellular organisms) and can be used to predict the expression level of foreign genes. The CAI value can be calculated via the following equation [23]:(4)CAI=∏K=1LwK1/L,where L refers to the number of codons used in the gene. The value of CAI is between 0 and 1; the larger the value, the stronger the codon usage bias. wij is the relative adaptiveness of a codon, which can be calculated via(5)wij=RSCUijRSCUimax.In (5), the RSCUij denotes RSCU value of the codon for the ith amino acid. And the RSCUimax is the RSCU value for the most frequently used codon for the specific amino acid. The value of the CAI falls between 0 and 1; larger value denotes the larger bias of the codon usage.


### 2.4. Calculation for the Codon Bias Index (CBI)

The codon bias index reflects the presence of components with high codon usage in a particular gene. The value of CBI has been widely used for it can more clearly describe the foreign gene expression in the host. The CBI calculation method can be expressed by the following formula [24]:(6)CBI=Nopt-NranNtot-Nran,where the Nopt represents the total number of occurrences of the superior codon in the gene; in this work, the superior codons are the codons whose RSCU value is more than 1.6. Nran represents the sum of the number of occurrences of the superior codon when all the synonymous codons are random in a certain protein; Ntot represents the occurrence number of the amino acid corresponding to the superior codon in the gene. In this work, the CBI of genes forMycobacterium tuberculosis genome is calculated and analyzed. Accordingly, the Frequency of Optimal Codons (FOP) ofMycobacterium tuberculosis genome, which is the weighted average of the RSCU of superior codons (RSCU value > 1.6), is calculated similarly.


### 2.5. Parity Rule 2 (PR2) Analysis

The parity rule 2 (PR2) plot is usually used to estimate the impact of mutation and selection pressure on codon usage of genes. In the PR2 plot, the abscissa is[G3/(G3+C3)] denoting the GC-bias at the third codon position in entire coding sequences, while the ordinate is the A3/(A3+U3) which denotes the AU-bias at the third codon position of entire coding sequences [24]. In this work, the effects of mutation pressure and natural selection on the codon usage of genes inMycobacterium tuberculosis will be analyzed via the PR2 analysis.


### 2.6. Neutral Evolution and Clustering Analysis

The neutrality plot is the so-called neutral evolution analysis, which could be performed to determine and compare the extent of influence of mutation pressure and natural selection. In this work, the neutral evolution analysis of the codon usage pattern ofMycobacterium tuberculosis is conducted and analyzed by plotting the GC12 values of the synonymous codons against the GC3 values. In the present study, clustering analysis was used to analyze the difference in codon usage patterns amongMycobacterium tuberculosis via RSCU of coding sequences. In the clustering analysis progress, in order to minimize the effect of amino acid composition on codon usage, the three stop codons and two particular amino acid codons, AUG and UGG, are excluded. The pdist and dendrogram function of Matlab are used to build the tree. The function could generate a dendrogram plot of the hierarchical binary cluster tree. Others, such as the relationship between ENC and protein length, relationship between CBI and protein length, relationship between overall GC content and protein length, histogram of CBI, FOP, and overall GC content, as well as the protein versus GC12/GC3s are all explored.


## 2.1. Effective Number of Codons (ENC) Analysis

ENC analysis can be used to quantify the absolute codon usage bias in coding sequences. ENC was calculated using the following formula [19]:(1)ENCcalculatied=2+9f-2+1f-3+5f-4+3f-6,where f-k  (k=2,3,4,6) is the mean of fk values for the k-fold degenerate amino acids; f-k denotes the average homozygosity for the amino acid class whose degree of codon degeneracy is k. The coefficients 9, 1, 5, and 3 denote the number of amino acids belonging to different classes. Here, fk is estimated using the formula: fk=(nS-1)/(n-1), where n is the total number of occurrences of the codons for that amino acid and S=∑ni/n2  (i=1⋯k), and ni here is the total number of occurrences of the ith codon for that amino acid. Genes for which the codon choice is only constrained by a mutation bias will lie on or just below the curve of the expected ENC values [20]. Therefore, to elucidate the relationship between GC3 and ENC values, the expected ENC values for different GC3 were calculated as follows [21]:(2)ENCexpectation=2+s+29s2+1-s2,where s represents the given GC3%.


## 2.2. RSCU Value Analysis

The RSCU values for all genes inMycobacterium tuberculosis are calculated to determine the characteristics of synonymous codon usage without the confounding influence of amino acid composition and coding sequence size of different gene samples following a previously described method [22]. The RSCU index was calculated as follows:(3)RSCU=gij∑jnigijni,where gij is the observed number of the ith codon for thejth amino acid, which has ni kinds of synonymous codons. RSCU values represent the ratio between the observed usage frequency of one codon in a gene sample and the expected usage frequency in the synonymous codon family, given that all codons for the particular amino acid are used equally. The synonymous codons with RSCU values > 1.0 have positive codon usage bias and were defined as abundant codons, whereas those with RSCU values < 1.0 have negative codon usage bias and were defined as less-abundant codons.


## 2.3. Codon Adaptation Index (CAI) Calculation

The CAI value is generally used to predict the expression level of an inbred gene (it is more suitable for unicellular organisms) and can be used to predict the expression level of foreign genes. The CAI value can be calculated via the following equation [23]:(4)CAI=∏K=1LwK1/L,where L refers to the number of codons used in the gene. The value of CAI is between 0 and 1; the larger the value, the stronger the codon usage bias. wij is the relative adaptiveness of a codon, which can be calculated via(5)wij=RSCUijRSCUimax.In (5), the RSCUij denotes RSCU value of the codon for the ith amino acid. And the RSCUimax is the RSCU value for the most frequently used codon for the specific amino acid. The value of the CAI falls between 0 and 1; larger value denotes the larger bias of the codon usage.


## 2.4. Calculation for the Codon Bias Index (CBI)

The codon bias index reflects the presence of components with high codon usage in a particular gene. The value of CBI has been widely used for it can more clearly describe the foreign gene expression in the host. The CBI calculation method can be expressed by the following formula [24]:(6)CBI=Nopt-NranNtot-Nran,where the Nopt represents the total number of occurrences of the superior codon in the gene; in this work, the superior codons are the codons whose RSCU value is more than 1.6. Nran represents the sum of the number of occurrences of the superior codon when all the synonymous codons are random in a certain protein; Ntot represents the occurrence number of the amino acid corresponding to the superior codon in the gene. In this work, the CBI of genes forMycobacterium tuberculosis genome is calculated and analyzed. Accordingly, the Frequency of Optimal Codons (FOP) ofMycobacterium tuberculosis genome, which is the weighted average of the RSCU of superior codons (RSCU value > 1.6), is calculated similarly.


## 2.5. Parity Rule 2 (PR2) Analysis

The parity rule 2 (PR2) plot is usually used to estimate the impact of mutation and selection pressure on codon usage of genes. In the PR2 plot, the abscissa is[G3/(G3+C3)] denoting the GC-bias at the third codon position in entire coding sequences, while the ordinate is the A3/(A3+U3) which denotes the AU-bias at the third codon position of entire coding sequences [24]. In this work, the effects of mutation pressure and natural selection on the codon usage of genes inMycobacterium tuberculosis will be analyzed via the PR2 analysis.


## 2.6. Neutral Evolution and Clustering Analysis

The neutrality plot is the so-called neutral evolution analysis, which could be performed to determine and compare the extent of influence of mutation pressure and natural selection. In this work, the neutral evolution analysis of the codon usage pattern ofMycobacterium tuberculosis is conducted and analyzed by plotting the GC12 values of the synonymous codons against the GC3 values. In the present study, clustering analysis was used to analyze the difference in codon usage patterns amongMycobacterium tuberculosis via RSCU of coding sequences. In the clustering analysis progress, in order to minimize the effect of amino acid composition on codon usage, the three stop codons and two particular amino acid codons, AUG and UGG, are excluded. The pdist and dendrogram function of Matlab are used to build the tree. The function could generate a dendrogram plot of the hierarchical binary cluster tree. Others, such as the relationship between ENC and protein length, relationship between CBI and protein length, relationship between overall GC content and protein length, histogram of CBI, FOP, and overall GC content, as well as the protein versus GC12/GC3s are all explored.


## 3. Results and Discussion

Codon usage pattern belonging to compositional characteristics such as theT3, C3, A3, G3, GC12, and GC3 and overall GC content of 12Mycobacterium tuberculosis genomes from different area are calculated. The results show that the average value of overall GC content is 65.36%, average value of GC3 is 79.18%, and the average T3 (11.38%) and A3 (9.43%) of 12Mycobacterium tuberculosis genomes are fewer than GC3. Furthermore, the average ENC, FOP, CBI, and CAI are 41.25, 0.64, 0.39, and 0.45.ENC is an important index to measure the codon usage bias in a genome and play a major role in their codon usage profile. In order to investigate the synonymous codon usage pattern of theMycobacterium tuberculosis genomes, the ENC versus GC3 is plotted and the result is shown in Figure 1. Every point represents one coding gene inMycobacterium tuberculosis. It can be seen that most dots are under the expected curve. Many scientists tended to think that the ENC value is belonging to a range of 21 to 61, but when the fk is calculated via (n∑(ni/n)2-1)/(n-1), all ENC values fell in the range from 20 to infinite; here, the value 20 denotes the codon usage bias using only one possible synonymous codon to correspond to an amino acid, and the larger value indicates that there is less bias of using all possible synonymous codons, and the frequency of using all possible synonymous codons may tend to be equal.Figure 1
Plot of ENC versus GC3 withinMycobacterium tuberculosis genomes.The parity rule states that if there is no mutation in genes, or no bias on the codon selection effect, the base content should obey the laws A = T and G = C. This method is usually used to analyze the PR2 bias of the third place codon via comparing theA3/(A3+T3) and the values of G3/(G3+C3). The PR2 bias plot of 12Mycobacterium tuberculosis genomes is shown in Figure 2. The distance between dot and the center denotes the degree and direction of the PR2 bias. From Figure 2, in most genes, A3 are less than T3. Most C3 content of the genes are more than the occurrence rate of T3 in these genes.Figure 2
PR2-bias plot analysis ofMycobacterium tuberculosis genomes.Usually, the neutrality plot is used to analyze the directional mutation pressure versus natural selection of a certain genome, which could also reveal the relationship between the GC12 and the GC3 with the GC12 as the ordinate and the GC3 as the abscissa. The neutrality plot of 12Mycobacterium tuberculosis genomes is shown in Figure 3. Each point shown in Figure 3 represents one separate gene for theMycobacterium tuberculosis. The result shows that the distribution of most GC12 rate is between 0.5 and 0.65 and most of the GC3 rate is during 70%–90%. In addition, GC3 is more than GC12 in most of the genes. Meanwhile, the correlation analysis in Table 2 shows that there is a weak correlation with the value of −0.015. Form these data performance, it can be seen that the natural selection may be a greater impact factor on codon preference inMycobacterium tuberculosis genomes.Table 2
Correlation of several basic parameters inMycobacterium tuberculosis genomes.
Title GC G C 3 s CAI CBI ENC FoP G C 3 s 0.536 CAI −0.37 −0.588 CBI 0.263 0.618 −0.325 ENC −0.474 −0.809 0.553 −0.685 FoP 0.28 0.46 −0.134 0.839 −0.612 GC12 0.832 −0.015 −0.056 −0.088 −0.034 0.033Figure 3
Neutrality plot of GC12 versus GC3s for 12Mycobacterium tuberculosis genomes.RSCU value, which is an important parameter for evaluating the bias of the synonymous codon, represents the ratio between the occurrence frequency of one codon and the expected usage frequency in a gene sample. Codons whose RSCU values are more than 1.0 would be regarded as having positive codon usage bias. On the contrary, those RSCU values less than 1.0 are defined as the less-abundant codons. The overall RSCU value can intuitively reflect the preference for codon usage in a certain genome. The overall RSCU values of 12Mycobacterium tuberculosis genomes are calculated and the results are shown in Figure 4. The codon usage bias shows that the red bars in those RSCU values are more than 1.5, which could be regarded as the abundant codons; these codons are UUC, CUG, AUC, GUC, GUG, UCG, CCG, ACC, AAC, UGC, UGA, CGC, CGG, AGC, and GGC. Among them, CUG has the highest RSCU value of 3.12. The blue bars in Figure 4 are showing the opposite (RSCU values are less than 0.5) characteristics which could be called the less-abundant codons. Generally, RSCU values for the codons ending with the A or T are smaller than the RSCU value of the codons ending with G or C. There are three stop codons (UGA, UAA, and UAG), and no corresponding amino acid corresponding to stop codons. In Figure 4, RSCU values for terminal codons are UGA = 1.645, UAA = 0.45, and UAG = 0.905, and sum of them is 3 (there are 3 stop codons). On the other hand, the RSCU value 1.645 for UGA here denotes that 54.8% (1.645/3) of stop codons are UGA, which is more than the sum of UAA and UAG.Figure 4
Overall RSCU of the 12Mycobacterium tuberculosis genomes.The value of RSCU is usually used as a measurement of the codon usage bias. The overall RSCU values (Figure4), to a certain degree, can reflect the characteristics of codon usage, but when it comes to the codon usage of individual sequence for a particular genome, it can be seen that there is a great difference of RSCU values from different genes even of one genome. RSCU values of 12Mycobacterium tuberculosis genomes are separately shown in Figure 5. There is some difference between all genomes especially in the usage of CUU, GUG, ACC, GAU, CGG, GGC, and so on.Figure 5
Heatmap of RSCU values for 12 separatedMycobacterium tuberculosis genomes.The CBI value can more clearly describe the foreign gene expression in the host. The CBI values of 12 separatedMycobacterium tuberculosis genomes are calculated via (6). Meanwhile, in order to explore the relationship between the CBI and the ENC, the ENC values are equalized to the range of the CBI via the following equation:(7)E.ENC=ENC-ENCminENCmax-ENCminCBImax-CBImin+CBImin.Most previous researchers pointed out that the smaller ENC value may denote higher codon usage bias (CBI). The phenomenon is shown in Figure6, and here the negative relationship between them is digitized by the linear fitting results. The results show that the negative correlation coefficient is −0.78657.Figure 6
Overall relationship between CBI and the equalization of ENC.Proteins inMycobacterium tuberculosiscould be analyzed via many methods, mass spectrometry, codon usage, and so on [25, 26]. Codon usage is linked to nucleic acids and proteins, especially the GC content. In Figure 7, relationship between protein length and GC content (GC3 and GC12) is described. Most proteins are concentrated within five hundred amino acids in length; it can also be seen from Figure 7 that when encoding the same length of protein, most of the GC3 content is higher than the GC12 content. As the length of the code increases, the GC content of either GC12 or GC3 tends to increase slightly. But within the lengths between 1000 and 2000 nucleotide residues, the GC-contents (GC12 and GC3) are not obviously varied with the gene length (Okazaki fragment in bacteria is between 1000 and 2000 nucleotide residues; its corresponding protein length is between 333 and 667 or so). Here, only proteins of less than 1500 amino acids in length are considered.Figure 7
Relationship between protein length and GC content (GC3S and GC12).Codon bias has a very broad significance for exploring a genome. From the molecular level, codon bias can also explain the basic phenomenon of evolutionary process in biology [27]. Sometimes, the differences between RSCU values of different genomes can be used to describe the evolutionary distance. Numerical closeness of RSCU values between coding sequences shows the closeness of relationship [28]. Genetic diversity ofMycobacterium tuberculosisfrom different area may have different frequency of ancestral strains [29]. Diversity and disease pathogenesis forMycobacterium tuberculosisfrom different area may show difference too [30, 31]. When the evolutionary relationship of 12 separatedMycobacterium tuberculosis is concerned, samples from different countries have subtle differences. Genetic relationship of them is shown in Figure 8.Figure 8
Phylogenetic tree of 12Mycobacterium tuberculosisgenomes.Mycobacterium tuberculosis exists in most countries and exploring its codon usage bias is useful for understanding genetic characteristics and geographical differences of whole genomes. Although many scientists endeavor to study theMycobacterium tuberculosis from the genetic perspective [32, 33] for its very important for defendingtuberculosis [34], its codon usage bias is not very clear until now. In this study, not only are traditional methods, such as ENC-plot, PR2 analysis, RSCU values, and phylogenetic analysis of 12Mycobacterium tuberculosisgenomes, studied, but also some new methods, CBI versus ENC and protein length versus GC3S/GC12, are all explored; the former relationship describing the relationship between CBI and ENC could reveal their negative correlation characteristics. Relationship between protein length and GC content (GC3S and GC12) can reflect the changes of GC content within different coding sequences during the evolutionary process. Previous studies indicated that genetic diversity exists inMycobacterium tuberculosis isolated from differenttuberculosis [35] when the samples are from different areas. Many studies also revealed that the host is an important factor that can affect the codon usage characteristics [36]. Overall RSCU values of overall 12Mycobacterium tuberculosis genomes (Figure 4) and their separated genomes (Figure 5) all reveal strong codon usage bias. All these results combined with the relationship between CBI and ENC, protein length, and GC content (GC3 and GC12) show that the natural selection pressure is a little more important than mutation pressure. There are only 12 genomes ofMycobacterium tuberculosis analyzed in this work; althoughMycobacterium tuberculosisexists in almost all countries. We selected 12 geographically representative genomes in order to analyze their regional differences. Codon usage differences ofMycobacterium tuberculosisfrom broader range and even from one area are not mentioned in this work; it is necessary to expand the scope of the genomes in the further study.


## 4. Conclusions

In this paper, the codon usage patterns of 12Mycobacterium tuberculosisgenomes, such as the ENC-plot, the A3/(A3+T3) versus G3/(G3+C3) plot, the relationship GC12 versus GC3, the RSCU of overall/separated genomes, the relationship between CBI and the equalization of ENC, and the relationship between protein length and GC content (GC3S and GC12), and their phylogenetic relationship are all analyzed. The codon usage pattern and its influencing factors, especially, were identified for 12Mycobacterium tuberculosis genomes. It is observed that codon usage patterns inMycobacterium tuberculosis genomes are influenced by GC3 bias. Correlation between codon bias index and GC3 (=0.618) shows that the GC3 bias ofMycobacterium tuberculosis genomes may also reflect its codon bias index. Majorities of the codons are G/C ended; this phenomenon combined with a negative correlation between overall GC-content and CAI may indicate the important role of compositional constraints and mutation pressure in shaping the codon usage bias inMycobacterium tuberculosis genomes. We performed the comparative analysis of codon usage bias inMycobacterium tuberculosis genomes from different area. The results would help further elucidate the underlying dynamics of genetic evolution inMycobacterium tuberculosis genomes. These results showed that codon usage bias exists in theMycobacterium tuberculosisgenomes. All this information is important for explaining the function ofMycobacterium tuberculosis genomes and helps in understanding the evolutionary process of theMycobacterium tuberculosis.



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