Open Access Method

Comparative context analysis of codon pairs on an ORFeome scale

Gabriela Moura1, Miguel Pinheiro2, Raquel Silva1, Isabel Miranda1, Vera Afreixo2, Gaspar Dias2, Adelaide Freitas3, José L Oliveira2 and Manuel AS Santos1*

Author Affiliations

1 Centre for Cell Biology, Department of Biology, University of Aveiro, 3810-193 Aveiro, Portugal

2 Institute of Electronics and Telematics Engineering, University of Aveiro, 3810-193 Aveiro, Portugal

3 Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal

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Genome Biology 2005, 6:R28  doi:10.1186/gb-2005-6-3-r28


The electronic version of this article is the complete one and can be found online at: http://genomebiology.com/2005/6/3/R28


Received:24 September 2004
Revisions received:25 November 2004
Accepted:17 January 2005
Published:15 February 2005

© 2005 Moura et al.; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Codon context is an important feature of gene primary structure that modulates mRNA decoding accuracy. We have developed an analytical software package and a graphical interface for comparative codon context analysis of all the open reading frames in a genome (the ORFeome). Using the complete ORFeome sequences of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Candida albicans and Escherichia coli, we show that this methodology permits large-scale codon context comparisons and provides new insight on the rules that govern the evolution of codon-pair context.

Background

The standard genetic code uses 64 codons for only 22 amino acids, including the amino acids selenocysteine and pyrrolysine whose incorporation into protein requires the reassignment of the UGA and UAG stop codons, respectively [1,2]. This degeneracy of the genetic code has important implications for gene primary structure evolution as it provides nature with a vast array of options for building open reading frame (ORF) sequences for any particular protein. However, the usage of synonymous codons for building ORFs is not random, suggesting the existence of mechanistic or evolutionary constraints that limit the degree of freedom for coding sequence building [3-6]. In other words, each organism uses a set of rules for building ORF sequences which restrict the total number of options provided by the degeneracy of the genetic code. These rules are only partly understood. Nevertheless, it is becoming increasingly clear that codon usage and context bias reflect the action of two main evolutionary forces: selection for mRNA decoding efficiency and mutational drift acting indiscriminately on coding and noncoding DNA [7-10].

Codon usage reflects selection for translational efficiency, as highly expressed genes tend to use codons that are decoded by abundant cognate tRNAs [11-13]. Similarly, the context of a sequential pair of codons (codon-pair) is biased, but this bias is apparently linked more to decoding accuracy than to translational speed [14-17]. This suggests that the translational machinery is sensitive to the nature of the codon-pair present in the ribosome A and P decoding sites [16,18-20], raising the possibility that, like codon usage, codon context may also be species specific. This is supported by the fact that tRNA populations diverge in the number and abundance of tRNA isoacceptors for each codon family and also in the pattern of modified nucleosides in the tRNAs, which also affects mRNA decoding accuracy.

To shed new light on the overall pattern of codon context at the species level and evaluate how codon-pair context varies between species, we have developed software and statistical methodologies for codon-pair context analysis on all the ORFs in a genome as a whole (the ORFeome). Because our main interest is to evaluate the effect of codon context on mRNA decoding accuracy, this study focuses on the context of codon-pairs and not on long-range context effects. With a few exceptions, long-range context is not relevant for mRNA decoding by the ribosome. These new methodologies were tested using the complete ORFeome sequences of the eukaryotes Saccharomyces cerevisiae, Candida albicans and Schizosaccharomyces pombe and the bacterium Escherichia coli. The methodology developed provides robust and flexible tools for intra- and inter-ORFeome comparative codon-pair context analysis, permits identification of species-specific codon context fingerprints and provides new insight into the role of codon context on mRNA decoding accuracy and ultimately on the pressure imposed by the translational machinery on the evolution of the ORFeome. The software developed, called Anaconda, is available at [21].

Results

Global analysis of codon context in yeast

The Anaconda bioinformatics system developed in this study identifies the start codon of an ORF and reads it by moving a 'decoding window' three nucleotides at a time in the 3' direction until it encounters a stop codon. While doing so it fixes the middle codon of the reading window and memorizes its 5' and 3' neighbors. Anaconda creates a table of frequencies of 64 × 64 codons that allows computation of the number of times the complete set of contiguous codon pairs occurs in an ORF or in an ORFeome. The overall architecture of Anaconda is described in Figure 1.

thumbnailFigure 1. Architecture of the Anaconda bioinformation system. The Anaconda package contains a data-acquisition module that permits downloading raw data from genome databases and filter it into a local database. This data is then processed using a ribosome simulation algorithm and transferred to a 64 × 64 table that renders itself to statistical analysis. The processed data is then transferred to the visualization module that has a number of different tools that permit different types of data visualization and analysis. RSCU, relative synonymous codon usage values from very highly expressed genes, necessary for codon adaptation index (CAI) calculation (see [55]).

The codon-pair context frequency table built by Anaconda allows the statistical analysis of contingency tables to be used to test whether the context is significantly biased [22-25]. These tables allow one to test the existence of association between codon-pairs through the chi-square (χ2) test of independence; to identify preferred and rejected pairs of codons in the ORFeome through the analysis of adjusted residuals for contingency tables (Table 1 and Figure 2); and to construct a codon context map on an ORFeome scale (Figure 3). The Anaconda algorithm, its graphical interface and implemented statistical methodologies were tested using the yeast S. cerevisiae ORFeome. For this, the complete ORFeome was downloaded from the yeast genome database [26], the adjusted residual values for the total number of codon pairs were calculated (see Materials and methods) and each residual value present in a cell of the contingency table (64 lines × 64 columns) was converted into a two-color coded map (Figure 3). In the latter, green represents positive values greater than +3 (herein called preferred codon-pairs) and red represents negative values lower than -3 (herein called rejected codon-pairs) according to the color scale indicated in Figure 3a. The data clearly show that each codon has a set of preferred 3' codon neighbors (green) and rejects a set of other codons (red), indicating that codon context is highly biased in S. cerevisiae. However, in a rather large number of cases, the 3' codon context is not biased or at least strongly rejected or preferred. This is indicated by the black color in the map (Figure 3) and in the histogram of the residuals distribution (Figure 4). This black color corresponds to residual values that fall within the interval of -3 to +3 and correspond to codon contexts that do not contribute to the bias for a confidence level of 99.73% (Table 1 and Figure 2). The overall empirical distribution of residual values for codon context in the yeast ORFeome (Figure 4) clearly shows that a large fraction (about 47%) of codon-pair contexts fall within the interval of -3 to +3, indicating that in many cases the context may not be under high selective constraint.

Table 1. The 3' codon context of CUG

thumbnailFigure 2. Codon context is highly biased in yeast. The bar chart shows the distribution of the adjusted residual values given in Table 1 for the 3' context of the S. cerevisiae CUG codon. See Table 1 legend for details.

thumbnailFigure 3. S. cerevisiae genome map of codon context. For visualization purposes the values of the residuals of the 64 × 64 codon context table were converted into a color-coded map in which red represents the negative values (bad context) and green the positive values (good context). The values that are not statistically significant are indicated in black (-3 to +3). The color scale represents the full range of values of residuals for yeast codon context. Fixed codons represent the P-site codons and the 3' context refers to the A-site codons as viewed by the ribosome simulation software module. (a) The yeast complete 3' codon context map shows a diagonal green line, which indicates that most codons prefer themselves as neighbors on their 3' side. The map also indicates that without exception, each codon prefers a defined set of neighbors (green) and avoids others (red). The intensity of red and green indicates the extent of the preference or rejection. (b) Codons that are represented in the map can be visualized by zooming into particular areas of the map (boxed in dark blue in (a)). The order of the fixed and 3' context codons indicated in (b) is predefined in the software module.

thumbnailFigure 4. Distribution of the adjusted residuals from the S. cerevisiae codon context map. Forty-three percent of the residuals fall within the nonsignificant -3 to +3 interval, indicating that a very large number of codon combinations are not significant to the rejection of independence - that is, are not significantly preferred or rejected in this genome.

Codon clustering unveils unique features of codon context

The codon-pair context maps shown in Figure 3a,b were built using a manually predefined distribution of codons in both lines and columns. To better understand the full extent of the codon-pair context bias in yeast, the data were clustered using the Pearson's correlation coefficient [27], which enables grouping of codons with similar context preferences. Using double clustering (that is, clustering both lines and columns) several distinct groups of red and green codon-pair contexts were identified for the S. cerevisiae ORFeome, thus showing that certain groups of codons have similar 3'-neighbor preferences (Figure 5).

thumbnailFigure 5. Codon context bias is organized in discrete groups. A two-way Pearson clustering by single linkage of the codon context data highlights regions of good and bad codon context, indicating that codon context bias is highly structured. A significant number of codons do not fall into the major clusters, indicating that their preferences and rejections are defined on a one-to-one basis. The 3' codon contexts whose residual values fall within the nonstatistically significant -3 to +3 interval are also scattered in the map, indicating that there is no cluster of codons that have little or no preference for particular codons as 3' neighbors.

To identify the codons responsible for defining the subgroups with high bias (red and green clusters) and evaluate whether these could define codon-pair context rules, one zooms in on the context subclusters. Three specific subclusters (one red and two green) were analyzed in this study (Figure 6a-c). The red subcluster shown in Figure 6a is defined by codon-pairs in which the last nucleotide of the first codon is uridine (U) and the first nucleotide of the next codon (3' side) is adenosine (A). As no such rule was observed for the other codon positions - that is, positions 1 and 2 or 2 and 3 of codon 1 or positions 1 and 2 or 2 and 3 of codon 2 (data not shown), the codons are clustered based on the following context rejection rule: XXU-AYY. The intensity of rejection (given by the adjusted residual itself) is not identical for all codon combinations within the subcluster. However, with the exception of the asparagine AAU and serine AGU codons, and some others whose residual values fall within the non-statistically significant -3 to +3 interval, all other U-ending codons avoid 3'-neighbor codons starting with an A. If one assumes that fixed codons in the map (lines) represent P-site codons and 3' codons (columns) represent A-site codons, then the above rule suggests that the third base of a P-site codon somehow influences the choice of the first base of the A-site codon. In other words, and assuming that context modulates decoding accuracy, S. cerevisiae codon pairs that end with an U and start with an A are likely to cause some trouble to the ribosome during decoding.

thumbnailFigure 6. Codon clusters define specific codon-context rules in S. cerevisiae. (a) A major cluster of bad context is defined by codon pairs whose wobble base of the first codon is uridine (U) and the first base of the 3' neighbor is adenosine (A). This cluster defines a XXU-AYY context rule, in which X and Y are any nucleotide. Within this cluster some of the Asn and Ser codons represent exceptions to the above rule as their residual signal is positive (green cells). (b,c) Two of the good context clusters define two distinct codon context rules, namely (b) XXC-AYY and (c) XXU-GYY rules. As before, some of the codons within those clusters are exceptions to the above rules and a number of codons have no particular preferences or rejections (black cells).

The above observations were confirmed by analyzing two green codon-pair context subclusters (good contexts). In these cases, two different clustering rules were identified, namely the XXC-AYY and the XXU-GYY (Figure 6b,c). Like the bad context subcluster discussed previously, in these good context subclusters there are exceptions that include red and black context cells. Nevertheless, there is a strong trend for the above rule within each subcluster, indicating once more that the third base of the P-site codon influences the first base of the A-site codon. The fact that these rules cannot be seen for other codon positions, and that there are exceptions to these rules for other codon families in the overall map, excludes the possibility that the third-first base rules identified reflect dinucleotide preferences or rejections arising from DNA replication and repair ([28] and see later).

Comparative codon context analysis

Because the S. cerevisiae codon-pair context map produced a clear context pattern, we wondered whether this map could represent a species-specific fingerprint, as is the case for the codon-usage fingerprint. For this, maps for S. pombe, C. albicans and E. coli were also constructed, with the latter being used as an outgroup. Some similarities between the codon-pair context maps were immediately visible, namely a strong green diagonal line in the yeast maps (Figure 7). There are, however, important differences that become evident when the negative and positive residual values are ranked for the yeast species studied (Table 2). These values represent the most negative and positive residuals of the yeast maps and consequently provide a good indication of the differences in codon context present in the three yeast species. Of the 10 most positive residual values ranked in Table 2, only two are common for the three yeast species, namely GAA-GAA, GGU-GGU and GCU-GCU. A similar result was obtained when the most negative values were ranked (Table 2). In addition, the C. albicans genome shows a more biased codon-pair context status. For example, the 10th most positive residual (49,476 for ACA-ACA) is higher than the maximum residual value for S. cerevisiae and S. pombe: 45,422 for CAG-CAG and 35,086 for UCU-UCU, respectively (Table 2).

thumbnailFigure 7. Codon context maps are species specific. Comparison of the genomic codon context maps of S. cerevisiae, C. albicans, S. pombe and E. coli shows that they are all different. There are common features between the maps but differences are clearly visible, indicating that each species has a specific set of codon context rules. Among the common features, the green diagonal line in the yeast maps is the most relevant. This diagonal indicates that almost all codons prefer themselves as their 3' neighbors and is strongly marked in the C. albicans context map, suggesting that in this species, tandem codon repetition is very common.

Table 2. Ranking of the 10 most negative and 10 most positive residual values in S. cerevisiae, S. pombe and C. albicans contexts

An additional approach to identifying codon-pair context differences between S. cerevisiae, S. pombe and C. albicans, was undertaken by overlapping the complete codon context maps displayed in Figure 7. For this, the maps built with a predefined order of codons for both the 64 lines and the 64 columns were merged, allowing the construction of a comparison codon-pair context map. We call this a differential codon-pair context map (DCM) and it corresponds to the module of the difference between the residuals of overlapped cells of the 64 × 64 context table (Figure 8). A new color scale based on gradation of blue was used for the differential display. Using this methodology, the codon context differences for the three yeast species became self-evident, indicating that codon context - like codon usage - is species specific (Figure 8). In all three DCMs shown in Figure 8 there are common features, which are indicated by the black cells; however, the differences (blue) are clearly visible. As expected from the phylogenetic distance of the various species studied, the DCMs for the pairs E. coli-S. cerevisiae and E. coli-C. albicans show many more differences than the DCM for the pair S. cerevisiae-C. albicans.

thumbnailFigure 8. Differential display maps for comparative analysis of codon context. To compare the codon context maps of different species, the order of the codons displayed in the map was fixed and the maps overlapped using a differential display tool built into the Anaconda bioinformation system. Maps representing the context differences between (a) S. cerevisiae and C. albicans, (b) E. coli and S. cerevisiae and (c) C. albicans and S. cerevisiae were obtained by calculating the module of the difference between the residuals of each map. The differences are represented in blue according to the color scale. The blue cells indicate the highest context difference and the black cells represent pairs of codons that have similar residual values between two species (module of the difference between residuals falls within the 0-15 interval). The maps show rather large differences in codon context between E. coli and S. cerevisiae or C. albicans and smaller differences between S. cerevisiae and C. albicans.

The DCMs also show that codon-pair context is more similar for the pair S. pombe-S. cerevisiae (data not shown) than for the other two yeast pairs, indicating that there are fewer differences between S. pombe and S. cerevisiae than between C. albicans and S. cerevisiae. This is surprising, considering that S. pombe diverged from S. cerevisiae 420 million years ago whereas C. albicans diverged from the latter only 170 million years ago [29]. The effect of the rather strong green diagonal (codon repeats) in the C. albicans maps is also visible in the DCMs (blue cells) of the C. albicans-S. cerevisiae pairs (Figure 8a). In order to shed more light on the differences in the codon context maps for the three yeasts, codon pairs were ranked according to the module of the difference between residuals (Table 3). Surprisingly, only one codon pair for the three yeast species (CAA-CAA) is present among the 10 highest values that were ranked. Further, the difference between these three species is not only qualitative, as shown above, but is also quantitative. For example, for the S. pombe-S. cerevisiae pair, the highest difference was found for the pair CAG-CAG with a value of 27,798, whereas in the S. pombe-C. albicans map the CAA-CAA pair showed a difference value of 100,639. In fact, in the latter yeast pair DCM all 10 values related are higher than the highest value (27,798) found for the CAG-CAG codon pair in the S. pombe-S. cerevisiae map (Table 3). Therefore, when taken together, DCMs and residuals rankings provide unique insight into the codon-pair context differences, even for phylogenetically related species such as yeasts.

Table 3. Ranking of the codon pairs that display the highest residual difference between S. cerevisiae, S. pombe and C. albicans

Contribution of mutation bias to codon-pair context

An important feature of the codon-pair context map in the yeasts analyzed, but not in E. coli, is the presence of a diagonal green line (Figures 3, 7). The existence of this green line implies that in those yeasts, most codons prefer to have another identical codon on their 3' side, indicating a degree of tandem codon duplication in the ORFeome of yeasts. Trinucleotide repeats are characteristic of eukaryotic genomes and have been attributed to DNA polymerase slippage during genome replication [30]. Whether the codon duplication observed in the ORFeome of the yeasts analyzed is a consequence of DNA replication only, or also reflects an evolutionary constraint imposed by the mRNA decoding machinery on those ORFeomes, is not yet clear and we are currently investigating this. In any case, this diagonal line in the codon context maps of yeasts is a strong feature, since the highest residuals of codon pairs (preferred pairs) occur for tandem codon repeats (Table 2).

The above observations prompted us to investigate whether mutational bias also played a part in codon-pair context bias and whether such bias could be extracted from the codon-pair context maps. For this, particular attention was given to GC content because it plays a major role in codon usage [31]. An algorithm was implemented into Anaconda for calculating %GC total, %GC at codon position 1 (GC1), %GC at codon position 2 (GC2) and %GC at codon position 3 (GC3). While scanning an ORFeome, Anaconda divides ORFs into GC-content subgroups and creates groups of ORFs with high and low GC content. It also determines the distribution of ORFs according to their GC total and GC3 (Figure 9a,c). Codon-pair codon context maps can be built for each subgroup of codons and the maps compared using the DCM tool (Figures 9b,d and 10).

thumbnailFigure 9. GC3 distribution in the complete ORFeome of S. cerevisiae and E. coli and its influence on the overall codon-pair context analysis. In order to study the role of mutational bias upon codon-pair context the ORFeomes of both (a,b) S. cerevisiae and (c,d) E. coli were distributed according to the %GC3 of individual ORFs. The GC3 of the S. cerevisiae and E. coli ORFeomes varied between the intervals 11.9-76.7% and 20-89.4%, respectively. For S. cerevisiae, however, most ORFs had a %GC3 between 35 and 40% (light blue bar in (a)), while for E. coli the majority of the ORFs have a %GC3 between 50 and 60% (light blue bars in (c)). Determination of the codon-pair context for the low and high GC3 subgroups permitted identification of their context differences. The computation of the number of residuals that changed their signal (for example, positive to negative) from one subgroup (low GC3) into the other (high GC3) provided a quantitative measure of the role of GC3 on codon-pair context (red bars in (b) and (d)). For both S. cerevisiae and E. coli GC3 bias has a strong effect on codon-pair context for weak residuals (-3 to +3), but no such effect was observed for contexts with the highest residuals (strong context), indicating that GC3 bias is mainly felt in weak codon-pair contexts.

thumbnailFigure 10. ORFs with low and high GC3 have different codon-pair contexts. To highlight the effect of GC3 bias on codon-pair context, the context maps for the subgroups of low GC3 and high GC3 ORFs of both S. cerevisiae and E. coli were overlapped using the differential display codon-pair context (DCM) tool. The DCM maps for S. cerevisiae and E. coli showed significant differences (light blue cells in the DCMs), in particular in E. coli, indicating that GC3 bias influences codon-pair context.

Because GC bias is better observed at the third codon position as a result of the degeneracy of the genetic code, GC3 was used to evaluate whether mutational bias contributed to the codon-pair context using the S. cerevisiae and E. coli ORFeomes as proof of principle. In the former, the ORF distribution varied from a minimum of 11.9% to a maximum of 76.7%; however, most ORFs fell within a narrow interval between 35-40% GC3 (Figure 9a). In the case of E. coli, the ORF distribution is broader, varying from a minimum of 20.0% to a maximum of 89.4%, but most ORFs have a GC3 between 50% and 60% (Figure 9c). This distribution made it possible to build codon-pair context maps for the low GC3 and high GC3 subgroups. As differences between these low and high GC3 context maps were expected to allow for evaluation of the importance of the bias introduced by mutational drift into the codon-pair context maps, these maps were overlapped using the DCM tool. As before, the maps were built using a single colour (blue) to aid visualization of the context differences. If mutational drift did not contribute to the context bias, the codon-pair context maps of the GC3 subgroups would be identical, producing a black differential display map. This is because the difference of the module of the residuals would be zero for all cells of the table of residuals.

The differential display map for the low and high GC3 ORF subgroups of S. cerevisiae showed several differences, indicating that GC bias contributes to the codon-pair context. However, most of these differences corresponded to small deviations in the strength of the rejection or preference of the codon-pair contexts (Figure 9b and 10, see also Table 4). In other words, the residual values had the same positive or negative signal in both cases but the value was higher in one GC3 subgroup than the other and vice versa. In some cases, an inversion of signal of the residuals (for example, from positive to negative) was detected, indicating that the residual of the codon-pair was positive in one GC3 subgroup and negative in the other GC3 subgroup (light blue in Figure 9b). This inversion of signal provides clear evidence for the influence of GC content bias in the codon-pair context. Similar results were obtained for the E. coli ORFeome; however, a much larger number of inversions of the residual signal was observed in this case, indicating that the GC content bias is far stronger in E. coli than in S. cerevisiae (Figures 9d and 10, see also Table 4). The reasons for these differences and the quantitative contribution of mutational bias to codon-pair context bias is not yet fully understood and is currently being investigated. However, Anaconda already provides strong evidence for a role for mutational bias on codon-pair context.

Table 4. GC3 influences codon-pair context

Discussion

Codon context has been extensively studied in prokaryotic, eukaryotic, mitochondrial and viral genomes, and these studies unequivocally showed that codon-pair context is biased [9,10,32-35]. However, no tool has yet been developed to display codon context data and in particular codon-pair context (short-range context) in a way that would facilitate interpretation of the data and allow inter- or intra-genome context comparisons. This is essential if putative general rules that govern codon-pair context evolution are to be unraveled. The Anaconda bioinformation system has been developed to address this problem. By using statistical methodologies based on contingency tables and residual analysis (see Materials and methods), specific codon-pair context patterns were unveiled and displayed using a color coded ORFeome-context map. The data highlighted codon-pair context bias in yeasts and E. coli and some rules that define codon-pair context patterns in yeast.

Forces that shape codon-pair context

Studies carried out in the 1980 s in E. coli have demonstrated that codon-pair context influences mRNA decoding accuracy and efficiency, indicating that the translational machinery imposes significant constraints on codon-pair context [17,36,37]. For example, in starved E. coli cells, the asparagine AAU and AAC codons are misread as lysine at high frequency [16]. Quantification of the level of lysine misincorporation at those codons and determination of the effect of the 3' nucleotide context on lysine misincorporation showed that the AAU codon is misread up to nine times more frequently than the AAC codon, and that the 3' nucleotide context (III-I context) influenced the level of misreading by as much as twofold [16]. Additional studies carried out in vitro in E. coli, have also shown that ribosomes discriminate C-ending Phe UUC and Leu CUC codons less well than the U-ending Phe UUU and Leu CUU, showing that synonymous codons differ in translational accuracy [38]. Therefore, a possible role for codon-pair context is minimization of decoding error, in particular in those codons that are poorly discriminated by the ribosome.

In E. coli, over-represented codon-pairs are translated more slowly than under-represented codon-pairs, indicating that codon-pair context also influences translational speed [14]. This suggests that codon-pair context in E. coli is under strong selective constraints imposed by the translational machinery. Whether the context patterns now unveiled in yeast reflect similar selective constraints remains unclear. Nevertheless, the codon-pair context maps described here provide a good starting point to address this important biological question in vivo in yeast in a guided manner. Additional evidence for a role for selection on codon-pair context was highlighted by the negligible, or even zero, contribution of GC3 to the context bias in very frequent or very infrequent codon-pairs (strong contexts) in both S. cerevisiae and E. coli (Figure 9, Table 4) and by a number of exceptions to the context rules that define the subclusters of codon-pairs (Figure 6). For example, within the XXU-AYY subcluster of rejected codons (Figure 6a), the codon pairs AAU-AGC, AAU-AGU, AAU-AAU, AAU-AAC and the set of AGU-AGC, AGU-AGU, AGU-AAU, AGU-ACA, AGU-AUA have positive residuals, indicating that they are codon pairs preferred by the ORFeome. Similar exceptions are found within the subclusters of preferred codon pairs shown (Figure 6b,c). Furthermore, a detailed analysis of the overall ORFeome context map (Figure 5) shows that other codon-pairs violate the XXU-AYY rules, namely GGU-AUG, GGU-AUC, GGU-AUU, GGU-ACC, GGU-ACU. This supports the hypothesis that those clusters of the context map are not formed on the basis of particular dinucleotide combinations that may be related to genome mutational drift. This is further confirmed by our observation that the dinucleotide preference in the XXU-AYY, XXC-AYY and XXU-GYY codon pairs is not observed when the various positions within each codon or codon-pair are analyzed. In other words, in the codon pair X1X2X3-Y1Y2Y3, the X3-Y1 preferences are not observed for the dinucleotides X1-X2, X2-X3, Y1-Y2 and Y2-Y3 (data not shown).

Despite these arguments, mutational bias does influence codon-pair context [7,39-41]. Observed mutational bias reflects mutational events that act indiscriminately on all DNA sequences (coding and noncoding DNA) and is consequently a property of the genome rather than the result of selection acting within ORFs [42-45]. The data presented here is in line with those observations. For example, context maps shown in this study indicate that several of the context clusters are formed on the basis of dinucleotide context rules (III-I rule), namely the XXU-AYY, XXC-AYY, XXU-GYY (Figure 6a-c). As dinucleotide context is related to DNA repair and replication constraints those clusters reflect mutational bias [28]. An important feature that highlights the influence of mutational bias on codon-pair context is GC content, in particular GC3 content. GC content has a strong influence in codon usage and in extreme cases can even drive certain codons out of ORFeomes [46,47]. The data presented here clearly show that GC3 affects codon-pair context; however, this effect is mainly visible for codon-pairs that have weak residuals (Table 4, Figure 9). As strong residuals (either positive or negative) provide an indirect measure of the strength of the codon-pair association, it is likely that for extreme residuals GC3 bias introduces only noise into the analysis whereas for residuals near the statistically nonsignificant interval (-3, +3), GC3 bias represents a major contribution to the context bias observed (Figure 9).

Apart from those cases mentioned above, other species-specific genomic features also contribute to codon-pair context bias highlighted by Anaconda. For example, the yeast codon-pair context maps show a feature of eukaryotic genomes which is not related to mRNA translation: trinucleotide repeats which are evident in the diagonal line present in Figures 3 and 7. This strongly suggests that there is a very high degree of tandem codon repeats (trinucleotide repeats), which are likely to arise from biased DNA replication (DNA polymerase slippage, see [30]). Whether these repeated codon-pairs improve mRNA translation efficiency or accuracy in yeast remains to be determined experimentally. As far as we are aware, there is no experimental evidence showing increased decoding accuracy or efficiency at those sites.

Finally, constraints imposed by protein sequences and mRNA secondary structure are also thought to influence codon context [48,49]. The context maps seem to exclude the former hypothesis because no cluster is formed as a result of selection or rejection of two adjacent amino acids. In regard to the latter constraint, the Anaconda algorithm was not designed to detect mRNA secondary structures and consequently this question cannot be addressed at this stage.

Conclusions

The Anaconda algorithm was developed with the aim of studying codon-pair context on an ORFeome scale, define rules that govern codon-pair context, carry out large-scale interspecies codon-pair context comparisons and clarify the effect of selection and mutational drift on codon-pair context. The results provide important new insight on the role of codon-pair context on mRNA decoding accuracy and efficiency, and we expect that it will allow the development of reporter genes for in vivo and in vitro quantification of codon-decoding error and translational speed. Finally, Anaconda will be a valuable tool to redesign ORFs for efficient and accurate heterologous or homologous protein expression in yeast and, eventually, in other suitable host systems.

Materials and methods

Statistics

To study the association between contiguous codon-pairs, the coding sequences analyzed by Anaconda are processed in a 64 × 64 contingency table subdivided in mutually exclusive categories. If the 3' context is being analyzed, the rows of the table correspond to the codons in the P-site and the columns to the codons in the A-site of the ribosome. At the 5' context analysis the situation is inverted, and so the contingency table built is a transposed version of the one for 3' analysis.

A number of different mathematical methodologies have already been used to study codon context bias (for example [9,50-52]). In this study, the analysis of contingency tables and residuals (Figure 3) was considered appropriate, assuming a multinomial probabilistic model for the contingency table (a detailed discussion of this model in the context of genomic data can be found in [53]). In general, all these methodologies are based on z-score-type tests and give information about preference and rejection. Basically, those methodologies differ in the probabilistic model assumed, leading to statistics whose probability distribution is in most cases unknown. The advantage of the methodology proposed here is that its theory of inference is well known, yielding an analysis that is more sequential, more easily interpretable and with more complementary tools for analysis (for example, measures of association). In other words, this methodology was chosen because the adjusted residual values give direct information about preference and rejection in relation to what would be expected on a random basis. Furthermore, the probability distribution under the hypothesis of independence is determined without data simulations.

For analysis of contingency tables and residuals [22-25], given an r × c contingency table where a multinomial distribution is assumed (Table 5), the hypothesis of independence between the variables A and B is tested using the Pearson's statistic given by:

Table 5. A hypothetical r × c contingency table

where:

It is known that Pearson's statistic has an asymptotical chi-square probability distribution with (r - 1)(c - 1) degrees of freedom. To identify cells in the table responsible for the eventual rejections of independence, the adjusted residuals dij are calculated by:

where:

is the variance estimated for rij. Haberman [54] has shown that, under independence between A and B, the adjusted residuals dij have a standardized normal probability distribution, and therefore P(- 3 <dij < 3) ≈ 0.9973, as N → + ∞. This means that, for a 99,73% confidence level, the pair (Ai, Bj) is considered responsible for rejection of the hypothesis of independence if |dij| ≥ 3. In practice, we consider that an adjusted residual is statistically significant if its absolute value is greater then 3.

Additionally, to find codon context patterns in the contingency table, lines and columns can be grouped using classifying methodologies such as cluster analysis. These patterns are determined by calculating similarities between two vectors of the contingency table using the centred Pearson correlation coefficient and applying single linkage. The single-linkage method produces groups with 'chaining effect': that is, any element of a group is more 'similar' to an element of the same group than to any element of another group.

Software

The architecture of the Anaconda software is based on three main modules, namely data acquisition, processing and visualization (Figure 1). Each module works independently from the others and can easily be replaced or updated. Also, this component-based approach allows for insertion of new modules or new tools in each module, such as new statistical features.

The acquisition and processing modules download row data from genome databases, create a local database of usable ORFs and analyze the data using an algorithm that simulates the ribosome during mRNA decoding. It finally constructs a database containing the processed data. This data is then submitted to statistical analysis as described above. The visualization module allows the user to visualize the data matrices and gene sequences and to create filters that permit searching for specific sequence patterns defined by the user.

The data-acquisition module deals with genome input files, namely reading and interpreting FASTA sequences of complete or partial sets of ORFs from public or private genome databases. To ensure that the screened sequences have the best possible quality, and hence do not introduce background noise in the following analyses, several quality filters are applied to the reading process. When the filters are activated the data are classified according to the following criteria. Valid data consist of genes whose sequence is a multiple of three; which start with an AUG codon and stop with a UAG, UAA or UGA codon, and which satisfy other user-defined requirements. Rejected data consist of genes whose sequence does not fulfill the above requirements. The result is the separation of valid from rejected ORFs. Other parameters needed by the application, such as reference relative synonymous codon usage (RSCU) values for codon adaptation index (CAI) calculation [55], are also uploaded by this module.

The processing module is the core of the application, where the codon context analysis is performed. After prescanning the files, the user can test the existence of significant bias in the codon context and use the residual values to further explore the matrices of residual values (see Statistics, above). The data generated are then converted into a contingency table that includes the corresponding observed values of Pearson's statistics, and the matrix of adjusted residuals [25].

After processing, the data become available to the visualization module. This module is the graphical interface. It follows the file manager paradigm in which information is presented in hierarchical views. This module offers a set of tools that enable several tasks to be carried out, namely to search prespecified sequence patterns, to visualize data in histogram form, to cluster codon context data, and to export residual values. It is also possible to visualize other information at the gene level, such as rare codons and their distribution in the ORFs, to determine their ratio relative to the total number of codons, to determine the GC% at the first, second and third codon positions and determine the codon adaptation index (CAI) and the effective number of codons [55,56].

Acknowledgements

We thank FCT (Project: POCTI/BME/39030/2001), IEETA and the II-UA (CTS-12) for supporting the development of the Anaconda software. G.M. is funded by FCT grant SFRH/BPD/7195/2001 and M.P. by INFOGENMED (FP-V). M.S. is supported by an EMBO YIP Award.

References

  1. Sandman KK, Tardiff DF, Neely LA, Noren CJ: Revised Escherichia coli selenocysteine insertion requirements determined by in vivo screening of combinatorial libraries of SECIS variants.

    Nucleic Acids Res 2003, 31:2234-2241. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  2. Theobald-Dietrich A, Frugier M, Giege R, Rudinger-Thirion J: Atypical archaeal tRNA pyrrolysine transcript behaves towards EF-Tu as a typical elongator tRNA.

    Nucleic Acids Res 2004, 32:1091-1096. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  3. Thomas LK, Dix DB, Thompson RC: Codon choice and gene expression: synonymous codons differ in their ability to direct aminoacylated-transfer RNA binding to ribosomes in vitro.

    Proc Natl Acad Sci USA 1988, 85:4242-4246. PubMed Abstract | PubMed Central Full Text OpenURL

  4. Ikemura T: Correlation between the abundance of yeast transfer RNAs and the occurrence of the respective codons in protein genes. Differences in synonymous codon choice patterns of yeast and Escherichia coli with reference to the abundance of isoaccepting transfer RNAs.

    J Mol Biol 1982, 158:573-597. PubMed Abstract | Publisher Full Text OpenURL

  5. Carlini DB, Stephan W: In vivo introduction of unpreferred synonymous codons into the Drosophila Adh gene results in reduced levels of ADH protein.

    Genetics 2003, 163:239-243. PubMed Abstract | Publisher Full Text OpenURL

  6. Elf J, Nilsson D, Tenson T, Ehrenberg M: Selective charging of tRNA isoacceptors explains patterns of codon usage.

    Science 2003, 300:1718-1722. PubMed Abstract | Publisher Full Text OpenURL

  7. Akashi H: Synonymous codon usage in Drosophila melanogaster : natural selection and transational accuracy.

    Genetics 1994, 136:927-935. PubMed Abstract | Publisher Full Text OpenURL

  8. Berg OG, Silva PJ: Codon bias in Escherichia coli : the influence of codon context on mutation and selection.

    Nucleic Acids Res 1997, 25:1397-1404. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  9. Fedorov A, Saxonov S, Gilbert W: Regularities of context-dependent codon bias in eukaryotic genes.

    Nucleic Acids Res 2002, 30:1192-1197. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  10. McVean GAT, Hurst GDD: Evolutionary lability of context-dependent codon bias in bacteria.

    J Mol Evol 2000, 50:264-275. PubMed Abstract | Publisher Full Text OpenURL

  11. Duret L: tRNA gene number and codon usage in the C. elegans genome are co-adapted for optimal translation of highly expressed genes.

    Trends Genet 2000, 16:287-289. PubMed Abstract | Publisher Full Text OpenURL

  12. Ikemura T: Codon usage and tRNA content in unicellular and multicellular organisms.

    Mol Biol Evol 1985, 2:13-34. PubMed Abstract | Publisher Full Text OpenURL

  13. Moriyama EN, Powell JR: Codon usage bias and tRNA abundance in Drosophila.

    J Mol Evol 1997, 45:514-523. PubMed Abstract | Publisher Full Text OpenURL

  14. Irwin B, Heck JD, Hatfield GW: Codon pair utilization biases influence translational elongation step times.

    J Biol Chem 1995, 270:22801-22806. PubMed Abstract | Publisher Full Text OpenURL

  15. Parker J: Errors and alternatives in reading the universal genetic code.

    Microbiol Rev 1989, 53:273-298. PubMed Abstract | PubMed Central Full Text OpenURL

  16. Precup J, Parker J: Missense misreading of asparagine codons as a function of codon identity and context.

    J Biol Chem 1987, 262:11351-11355. PubMed Abstract | Publisher Full Text OpenURL

  17. Precup J, Ulrich AK, Roopnarine O, Parker J: Context specific misreading of phenylalanine codons.

    Mol Gen Genet 1989, 218:397-401. PubMed Abstract | Publisher Full Text OpenURL

  18. Curran JF, Poole ES, Tate WP, Gross BL: Selection of aminoacyl-tRNAs at sense codons: the size of the tRNA variable loop determines whether the immediate 3' nucleotide to the codon has a context effect.

    Nucleic Acids Res 1995, 23:4104-4108. PubMed Abstract | PubMed Central Full Text OpenURL

  19. Shpaer EG: Constrains on codon context in Escherichia coli genes. Their possible role in modulating the efficiency of translation.

    J Mol Biol 1986, 188:555-564. PubMed Abstract OpenURL

  20. Gutman GA, Hatfield GW: Nonrandom utilization of codon pairs in Escherichia coli.

    Proc Natl Acad Sci USA 1989, 86:3699-3703. PubMed Abstract | PubMed Central Full Text OpenURL

  21. Functional Evolutionary Genomics Laboratory: University of Aveiro. [http://www.bio.ua.pt/genomica/lab] webcite

  22. Bishop YMM, Fienberg SE, Holland PW: Discrete Multivariate Analysis. Theory and Practice Cambridge.

    UK: MIT Press 1975. OpenURL

  23. Everitt BS: The Analysis of Contingency Tables.

    New York: John Wiley and Sons 1997. OpenURL

  24. Sheskin DJ: Parametric and Nonparametric Statistical Procedures.

    London: Chapman & Hall/CRC 2000. OpenURL

  25. Agresti A: Categorical Data Analysis.

    New York: Wiley 2002. OpenURL

  26. Saccharomyces Genome Database. [http://www.yeastgenome.org] webcite

  27. Everitt BS: Cluster Analysis.

    New York: Arnold 1998. OpenURL

  28. Nussinov R: Doublet frequencies in evolutionary distinct groups.

    Nucleic Acids Res 1984, 12:1749-1763. PubMed Abstract | PubMed Central Full Text OpenURL

  29. Massey SE, Moura G, Beltrao P, Almeida R, Garey JR, Tuite MF, Santos MAS: Comparative evolutionary genomics unveils the molecular mechanism of reassignment of the CTG codon in Candida spp.

    Genome Res 2003, 13:544-557. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  30. Freudenreich CH, Kantrow SM, Zakian VA: Expansion and length-dependent fragility of CTG repeats in yeast.

    Science 1998, 279:853-856. PubMed Abstract | Publisher Full Text OpenURL

  31. Sueoka N: Translation-coupled violation of parity rule 2 in human genes is not the cause of heterogeneity of the DNA G+C content of third codon position.

    Gene 1999, 238:53-58. PubMed Abstract | Publisher Full Text OpenURL

  32. Fulgsang A: Patterns of context-dependent codon biases.

    Biochem Biophys Res Commun 2003, 304:86-90. PubMed Abstract | Publisher Full Text OpenURL

  33. Gouy M: Codon contexts in enterobacterial and coliphage genes.

    Mol Biol Evol 1987, 4:426-444. PubMed Abstract | Publisher Full Text OpenURL

  34. Yarus M, Folley LS: Sense codons are found in specific contexts.

    J Mol Biol 1985, 182:529-540. PubMed Abstract | Publisher Full Text OpenURL

  35. Buckingham RH: Codon context and protein synthesis: enhancements of the genetic code.

    Biochimie 1994, 76:351-354. PubMed Abstract | Publisher Full Text OpenURL

  36. Carrier MJ, Buckingham RH: An effect of codon context on the mistranslation of UGU codons in vitro.

    J Mol Biol 1984, 175:29-38. PubMed Abstract | Publisher Full Text OpenURL

  37. Murgola EJ, Pagel FT, Hijazi KA: Codon context effects in missense suppression.

    J Mol Biol 1984, 175:19-27. PubMed Abstract | Publisher Full Text OpenURL

  38. Dix DB, Thompson RC: Codon choice and gene expression: synonymous codons differ in translational accuracy.

    Proc Natl Acad Sci USA 1989, 86:6888-6892. PubMed Abstract | PubMed Central Full Text OpenURL

  39. Chen SL, Lee W, Hottes AK, Shapiro L, McAdams HH: Codon usage between genomes is constrained by genome-wide mutational processes.

    Proc Natl Acad Sci USA 2004, 101:3480-3485. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  40. Eyre-Walker A: Synonymous codon bias is related to gene length in Escherichia coli : selection for translational accuracy?

    Mol Biol Evol 1996, 13:864-872. PubMed Abstract | Publisher Full Text OpenURL

  41. Duan J, Antezana MA: Mammalian mutation pressure, synonymous codon choice, and mRNA degradation.

    J Mol Evol 2003, 57:694-701. PubMed Abstract | Publisher Full Text OpenURL

  42. Akashi H: Codon bias evolution in Drosophila. Population genetics of mutation-selection drift.

    Gene 1997, 205:269-278. PubMed Abstract | Publisher Full Text OpenURL

  43. Sueoka N, Kawanishi Y: DNA G + C content of the third codon position and codon usage biases of human genes.

    Gene 2000, 53-62. PubMed Abstract | Publisher Full Text OpenURL

  44. Lobry JR, Sueoka N: Asymmetric directional mutation pressures in bacteria.

    Genome Biol 2002, 3:research0058.1-0058.14. BioMed Central Full Text OpenURL

  45. Knight RD, Freeland SJ, Landweber LF: A simple model based on mutation and selection explains trends in codon and amino-acid usage and GC composition within and across genomes.

    Genome Biol 2001, 2:research0010.1-100.13. BioMed Central Full Text OpenURL

  46. Osawa S, Jukes TH: On codon reassignment.

    J Mol Evol 1995, 41:247-249. PubMed Abstract | Publisher Full Text OpenURL

  47. Knight RD, Freeland SJ, Landweber LF: Rewiring the keyboard: evolvability of the genetic code.

    Nat Rev Genet 2001, 2:49-58. PubMed Abstract | Publisher Full Text OpenURL

  48. McHardy AC, Puhler A, Kalinowski J, Meyer F: Comparing expression level-dependent features in codon usage with protein abundance: an analysis of 'predictive proteomics'.

    Proteomics 2004, 4:46-58. PubMed Abstract | Publisher Full Text OpenURL

  49. Cohen B, Skiena S: Natural selection and algorithmic design of mRNA.

    J Comput Biol 2003, 10:419-432. PubMed Abstract | Publisher Full Text OpenURL

  50. Boycheva S, Chkodrov G, Ivanov I: Codon pairs in the genome of Escherichia coli.

    Bioinformatics 2003, 19:987-998. PubMed Abstract | Publisher Full Text OpenURL

  51. Shah AA, Giddings MC, Gesteland RF, Atkins JF, Ivanov IP: Computational identification of putative programmed translational frameshift sites.

    Bioinformatics 2002, 18:1046-1053. PubMed Abstract | Publisher Full Text OpenURL

  52. Hooper SD, Berg OG: Detection of genes with atypical nucleotide sequence in microbial genomes.

    J Mol Evol 2002, 54:365-375. PubMed Abstract OpenURL

  53. Avery PJ, Henderson DA: Fitting Markov chain models to discrete state series such as DNA sequences.

    Appl Statist 1999, 48:53-61. OpenURL

  54. Haberman SJ: Analysis of residuals in cross-classified tables.

    Biometrics 1973, 29:205-220. OpenURL

  55. Sharp PM, Li WH: The codon adaptation index - a measure of directional synonymous codon usage bias, and its potential applications.

    Nucleic Acids Res 1987, 15:1281-1295. PubMed Abstract | PubMed Central Full Text OpenURL

  56. Wright F: The 'effective number of codons' used in a gene.

    Gene 1990, 87:23-29. PubMed Abstract | Publisher Full Text OpenURL