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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 &
Manuel AS Santos1
Genome Biology volume 6, Article number: R28 (2005) Cite this article
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.
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].
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.
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
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.
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.
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).
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.
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).
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.
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).
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.
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
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.
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.
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
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 d ij are calculated by:
is the variance estimated for r ij . Haberman [54] has shown that, under independence between A and B, the adjusted residuals d ij have a standardized normal probability distribution, and therefore P(- 3 <d ij < 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 |d ij | ≥ 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.
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].
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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.
Centre for Cell Biology, Department of Biology, University of Aveiro, 3810-193, Aveiro, Portugal
Gabriela Moura, Raquel Silva, Isabel Miranda & Manuel AS Santos
Institute of Electronics and Telematics Engineering, University of Aveiro, 3810-193, Aveiro, Portugal
Miguel Pinheiro, Vera Afreixo, Gaspar Dias & José L Oliveira
Department of Mathematics, University of Aveiro, 3810-193, Aveiro, Portugal
Adelaide Freitas
Gabriela Moura
Miguel Pinheiro
Raquel Silva
Isabel Miranda
Vera Afreixo
Gaspar Dias
José L Oliveira
Manuel AS Santos
Correspondence to Manuel AS Santos.
Moura, G., Pinheiro, M., Silva, R. et al. Comparative context analysis of codon pairs on an ORFeome scale. Genome Biol 6, R28 (2005). https://doi.org/10.1186/gb-2005-6-3-r28
Revised: 25 November 2004
DOI: https://doi.org/10.1186/gb-2005-6-3-r28
Codon
Codon Position
Mutational Bias
Relative Synonymous Codon Usage


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Let me guess, that blogger in the first pic also loves to sing, doesn't he?
Pi Bani, you guessed correctly!
That guy in the first picture....golf, sing, guard..
Now this blogger is giving his identity away.
Now small and light is in.
Hello Puteri, it was really a beautiful surprise have you on board again after MIA so long. Missed you, Puteri.
Aha! I know who that fellow is...karoake kaki tu. Ha ha.
Wow! You balek kampong, huh?
I bet it was fun meeting bloggers for the first time.
I too am looking forward to meet several Sarawak friends...that handsome fellow in first pic...the Dean Martin of Miri especially. I owe him two big dinners!
As well a couple of Sarawak beauties, one whom I dedicated birthday greeting two weeks ago...she a really, stunning beauty too, an Orang Ulu. Now my good friend.
You know, Sarawak sure can produce exotic, stunning beauties! Not to mention one by the name of Puteri.
Wow! Your hubby must be 6'4"? Remind me not to meet him at the OK Corral? South Fork, okay, ha ha.
I popped over your place sometime back, but gone MIA, then as got no links, unable to knock your door.
But thrilled you came by.
You keep your finger off the trigger and have a nice day, best regards, Lee.
Lee, yes that's our friend the kaki karaoke! Haha.
What 6'4"? You are taller than him lah. He looked tall there because the two fellas standing to him are short! Heheh.
When are you planning on being in Sarawak? You will definitely have a great time esp when you meet up with all those friends you have made along the blogging world!
We won't be in Malaysia, not for another 2 years, I think. But right now we are enjoying the friends and the food!


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Q: Python Testing with Kill Process When testing a Python script I will often use a 'raw_input()' or 'input()' as a marker in the script, and when that marker is reached do a ctrl + c to kill the process in my command prompt.
I was worried this would lead to memory leaks, this brief thread indicates it shouldn't - killing python process leads memory leak?
It is bad practice to test scripts this way?  Can it lead to any negative effects?

A: Usually there's no problem with using raw_input() or input() with ctrl + c to terminate your program. When you press ctrl+ c during raw_input or input invocation you're just raising a KeyboardInterrupt exception and Python knows how to handle exceptions appropriately. If you don't handle the KeyboardInterrupt, this exception will be handled by the default top-level exception handler, this default exception handler prints the stack trace from which the exception occurred just before exiting the interpreter.
For some applications, they might not free their resources after they exit or after you kill the process, most operating systems today are smart enough to free memory when its unused. But this is not applicable to your question.

A: Not sure about the leaks but I'm sure breakpoints are your friend here. In idle right click on the line you want to stop on and click set breakpoint, then clear when you're finished debugging.

redpajama_set_name: RedPajamaStackExchange


text:
The Canadian Journal for the Scholarship of Teaching and Learning
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NEW YORK (Reuters) - Andrew Garfield, Laurie Metcalf and Nathan Lane won Tony Awards on Sunday (June 10) while rocker Bruce Springsteen and composer Andrew Lloyd Webber were lauded with special prizes at the annual ceremony honoring the best of Broadway theatre.
Garfield, known for his Hollywood take on Spiderman, won best actor in a play for his acclaimed performance in the Angels In America revival, a monumental two-part drama about Aids during the Reagan years which went into Sunday's ceremony with 11 nominations, the most for a play in Broadway history.
Lane won his third Tony award for his Angels in America performance as closeted conservative lawyer Roy Cohn, who died of Aids.
Praising playwright Tony Kushner, an emotional Lane said: "Tony wrote one of the greatest plays of the 20th century, and it is still speaking to us as powerfully as ever in the midst of such political insanity."
Metcalf won for the revival of Edward Albee's Three Tall Women, playing opposite Glenda Jackson as a younger version of Jackson's imperious character.
Springsteen said appearing on Broadway has been "one of the most exciting things I've ever experienced".
The rocker received his special Tony at the 72nd annual awards from fellow musician Billy Joel at a gala ceremony at Radio City Music Hall hosted by singer-songriter-actors Josh Groban and Sara Bareilles.
Actor, writer and comedian John Leguizamo also received a special Tony for his one-man show, Latin History For Morons, also nominated for best play.
The Tonys capped another record year on Broadway with US$1.7 billion (S$2.3 billion) in box office receipts, despite the smallest number of new productions in 20 years.
Armed with rave reviews and a fervent fan base, the two-part Harry Potter And The Cursed Child was the favourite for best play and picked up a handful of technical awards early on.
The play, about the grown wizard's troubled relationship with his teen-aged son, has 10 nominations and at US$69 million was the most expensive play in Broadway history.
In a David and Goliath scenario, the relatively modest musical The Band's Visit, about Egyptian musicians stranded in a small Israeli town, was expected to triumph over splashy crowd-pleasers SpongeBob SquarePants, Disney's Frozen and Tina Fey's adaptation of her hit comedy film Mean Girls. The Band's Visit won four early awards including best book of a musical.


Our trip (my husband and I) took over one month. This description is much compressed and features only the walks. What it proves is that walking can be part of any journey. Whether a dedicated trek or intermittent day walks, the rewards are infinite.
My walking journey started a world away from the Arctic Circle in the Cotswold area of England. Starting in Chipping Camden, I could take short stints on the famous Cotswold Way. Romantic towns like Stowe on the Wold, Upper and Lower Slaughter, Broadway, and Merton on the Marsh, reward you with pastoral scenes, local fetes and manor houses.
Foottrails, an English walking tour group has many Cotswold walking tours to suit everyone's tastes and preferences. For a non-commercial site check out the National Trails website which I used for independent walking..
Next, I continued on to Harrogate, England by car, where I attended the International Gilbert and Sullivan Festival. Harrogate is a perfect center for walks throughout Yorkshire. Footpaths and public walk ways bring you to the historic sites and iconic villages.
It's amazing how much a two mile walk from Harrogate to the great English gardens of Harlow Carr can pack in. You will encounter flocks of sheep, broad meadows and woodlands, with fairy doors sprinkled among the path's foliage by school children for their friends to find in delight.
Once you get to the gardens there are miles of flower filled trails. I met a little girl with her parents along the route. She was dressed as a fairy. I said, "I have heard there are fairies; but, this is the first time I actually saw one." And I meant it.
If you are a fan of the Midsomer Murder series on the BBC, you will enjoy the walk to several of the TVs recognizable sites on the Midsomer Route. It is all well chronicled in free walking maps of the areas. For example, the town of Hensley on Thames has a wonderful walking tour of the "Causton" area. Also see the suggestions from Explore Midsomer, which provides a driving tour with links to many walking routes.
Whether you drive or walk in Midsomer, stop for tea in a church. The local ladies are top notch at Victoria Sponge baking. Victoria Sponge is white layer cake with a history. Try it yourself with this NY Times recipe, then bow down to the church ladies. There is nothing like sipping real English tea laid on a hand-knitted doily, while looking out the window at a thatched roof or at pictures of the Victor and his wife.
From Midsomer we drove to the Port of Tilbury, England. We were ready to board the Marco Polo, the ship that would transport us to the Arctic. We stopped first at the neoliths of the Orkney Islands, the most Northerly part of Scotland; to me more impressive than Stonehenge. The hikers get the thrill of walking back in time to take a peek into the real lives of the world's first farmers.
The WalkHighlands website has some great walks of these fascinating islands. The Orkney Islands Council has a good website for walking on the islands, as well. My husband Stu loved these islands, and walked for miles for the first time. I suspect he strongly identified with Stone Age Man. Although he claims his identification was with the Viking explorers who left messages in the Runic language on cave walls.
Once leaving the Orkneys we sailed to Norway to start in Trondheim, a cosmopolitan city with top urban amenities. Yet, only a twenty-five-minute tram ride away from center city you find the Bymarka, a lake with wooded walking trails, hikes and wild life of the near North. For a quick start on your Bymarka walking adventure check out the Explore Norway site.
From Trondheim it is still a good 1100 km to Tromso, the start if the Arctic. From what the Norwegians call the "Paris of Norway," take a cable car to the hiking venue above the city. I met up with long distance walkers camping and hiking in the area. NerdNomads is a good place to get trail information.
A day later by sea we arrive in at Honinsvag for a stop at the North Capp, the most northerly place in Europe and well within the Arctic Circle. Here you see the markers for the International Walking trails, which were carved out of the Arctic in only 2013. For a full array of Northern Norway walks, look at the NorthNorge site.
Walking in the Arctic is other worldly. Yes, it is as sparse in habitation as you may imagine. And EVERYONE is a walker. The terrain is rocky or flat tundra with miles of flat grass. The sea surrounds you and as you get ever higher the sky explodes with rainbows. In summer it stays light until midnight and later, so your walks are multiplied.
The air is cool; but, temperate. You can climb to more frigid heights; but, in winter you need special gear. On a different trip in Lapland, I learned the importance of the husky, not only to pull a sled through the tundra; but, as a walking companion. In this trip, many walkers attached a husky to their belt as they walked together with the dog as a guide.
Our Arctic Adventure ends in Russia, with Murmansk its most northerly city and Archangel. To walk anywhere in Russia you first need a visa to visit the country. I strongly suggest you use an adventure guide. Because of the short time and the immense number of sights to see, we sadly did not hit the trails. We were surrounded by the history of WW11, the Cold War and a perspective truly from the other side of the world, and the other side of history. If you have any interest in the Arctic Convoys, collaboration between the Russia, England, Norway and the US that was seminal in saving us all from the Nazi rule, you need to go. For my husband and me, both of Russian ancestry, we felt right at home in a very foreign land.


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Congressional honor? A breeder of hope? Hold not your breath …
By Dr. Denny Wilkins on August 1, 2016 • ( 3 Comments )
As honor dwindles, so does hope.
Is hope a descendant of honor?
If if is, perhaps a little hope can be derived from recent statements of members of Congress in response to the lunacy of the GOP candidate for president. Donald "I am your voice" Trump has rashly criticized two Americans who lost their son to combat in a foreign land. Trump did this, apparently, because Khizr and Ghazala Khan are Muslim Americans from Pakistan.
Some Republican members of Congress have repudiated Trump's remarks.
From Sen. John McCain of Arizona: "While our party has bestowed upon him the nomination, it is not accompanied by unfettered license to defame those who are the best among us."
From Sen. Kelly Ayotte of New Hampshire, who is seeking re-election: "I am appalled that Donald Trump would disparage [the Khans] and that he had the gall to compare his own sacrifices to those of a Gold Star family."
From an aide, speaking for House Speaker Paul Ryan of Wisconsin: "The speaker has made clear many times that he rejects this idea, and himself has talked about how Muslim Americans have made the ultimate sacrifice for this country."
Sen. Roy Blunt of Missouri, who is seeking re-election, said the Kahns "deserve to be heard and respected."
Rep. Mike Coffman of Colorado, who is seeking re-election, said he was "deeply offended when Donald Trump fails to honor the sacrifices of all of our brave soldiers who were lost in that war."
Sen. Lindsey O. Graham of South Carolina, whose campaign for the GOP presidential nomination sputtered and died:
This is going to a place where we've never gone before, to push back against the families of the fallen. There used to be some things that were sacred in American politics — that you don't do — like criticizing the parents of a fallen soldier, even if they criticize you. If you're going to be leader of the free world, you have to be able to accept criticism. Mr. Trump can't.
GOP members of Congress have pounced on Trump's repeated denunciations of the Kahns. I suppose that's honorable behavior. And I suppose it suggests hope that coherent, intelligent, moral thought would from arise somewhere in the Republican Party. (Note: Democrats are not immune from incoherent, unintelligent, amoral thought.)
But it would be more honorable if GOP congressional critics of Trump's irrational behaviors had also withdrawn their endorsements of his candidacy and subsequent nomination. But few, if any, have done so.
Jennifer Steinhauer of The New York Times explains:
For congressional Republicans, Mr. Trump's inflammatory remarks are a vexing challenge. On one hand, they want to distance themselves just enough to try to grab support from voters of both parties who do not intend to vote for Mr. Trump but may split their tickets. But they do not want to outright flip on their prior endorsements of Mr. Trump, because they need his supporters' votes to win, too. …
Republican leaders believe they need to continue to support Mr. Trump, if only to provide cover for the party's candidates up for re-election. Republicans struggle to name policy positions of Mr. Trump that they prefer to those of his Democratic opponent, Hillary Clinton, but they often settle on their fear of the type of justices a President Clinton would name to the Supreme Court.
In other words, GOP congressional leaders need re-election to retain power in the House and Senate. So they may criticize Trump but won't rescind endorsements for fear of alienating Trump voters — whose votes members of Congress need.
If you point this out to them, their likely reply: "Hey, I want to be in Congress not for me — I just want to be able to do the business of the American people."
How much "business of the American people" has Congress accomplished (and Democrats are complicit) in recent years? So far this year, GOP leaders have only accomplished not doing their job — giving a fair hearing and a vote to the duly nominated candidate for the Supreme Court vacancy.
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Categories: American Culture, Crime/Corruption, Journalism, Politics/Law/Government, United States
Tagged as: Congress, elections, honor, hope, Kelly Ayotte, Khizr and Ghazala Khan, Lindsey Graham, McCain, Mike Coffman, Paul Ryan, Republicans, Roy Blunt, scotus, trump
Your Daily Devotional for August 1, 2016
Trump, WaPo and the GOP: gutless politicians, tepid journalism
Juliette Kings says:
Reblogged this on West Coast Review and commented:
We can only hope that more people will stand up against a childish bully who acts like a bratty out of control third grader. Ignorance knows no honor. Hate know no honor. Ignorance and hate has no place in our government – and neither does the man who prides himself on spreading such hate, with excuses that his pea brained feelings were hurt. This is a well thought out and interesting post. Thanks you S&R.
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MAYURI BOONHAM
THE MAGIC FISH
EX NIHILO / THE HUMAN EDGE
ERHEBUNG
VĀC II
SIVALOKA
"Boonham, with her new company ATMA, is one of the foremost exponents of new Bharata Natyam dance"
CONTACT ATMA DANCE
email: info@atmadance.com
ATMA Dance is a London-based company Limited by Guarantee, with a charitable status. It was formed in 2009 under the Artistic Direction of Choreographer and Performer Mayuri Boonham. The word ATMA, taken from the Sanskrit language meaning, from the soul, encompasses Boonham's artistic vision as she strives to create choreographic works that relate to life and the world in which we live in. Using the classical South Asian form of Bharatanatyam at the core of her movement vocabulary, Boonham infuses influences of Contemporary art forms to reimagine South Asian dance, creating an evocative and unpredictable aesthetic. By communicating stories in both abstract movement or within a narrative, Boonham's approach of creating and conceptualisation pushes boundaries, whilst entering new territories to create works that has a truly omni-cultural appeal.
Within one year of the company's formation in 2009, ATMA Dance produced two new touring works, 'Sivaloka' and 'Ghatam', both of which were nominated for the London Fringe Festival award in 2011. The works toured nationally and internationally, including seasons such as Spring Loaded at The Place, the Bristol Harbourside Festival as well as a performance in the Netherlands.
ATMA Dance received funding from the Arts Council England in early 2013. With this support and since her appointment as Choreographic Affiliate for the Royal Ballet, Mayuri has been carrying out research projects with Royal Ballet artists. Two new creations evolved from this research: 'Vāc II', which was staged as part of Draftworks in April 2013 and 'Forsaken', a commission from The Royal Opera for the Verdi/Wagner Deloitte Ignite Festival 2013 curated by Stephen Fry and The Royal Opera, presented in September 2013. 'Forsaken' featured Royal Ballet artists Yasmine Naghdi, Sander Blommaert and Nicol Edmonds and saw Mayuri collaborate with two opera singers, two musicians/veena players and a pianist.
In May 2013 ATMA Dance premiered 'Erhebung' – a dance-sculpture sonic art installation developed in collaboration with Sculptor Jeff Lowe, Sound Artist Bill Fontana and Costume Designer Moritz Junge. This work toured at art galleries and museums between 2013-2014. In 2014 ATMA Dance premiered Ex Nihilo / The Human Edge for nine dancers at The Linbury Studio, Royal Opera House before touring in the autumn in the same year.
Wher
In 2016 ATMA Dance were in residence at The Unicorn Theatre, London where they developed initial ideas for 'The Magic Fish' – a new work for young children and family audiences. Commissioned by The Place, GemArts, Pavilion Dance South West and funded by Arts Council England the work premiered at The Place and headlined their Something Happening For Kids Festival 2016.


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World News /18 Jun 2018
Gavin Wilde 06.18.18
U.S. Cyber Policy, Beyond Ones and Zeros
Critics have derided the White House's decision this past May to scrap its Cyber Coordinator post—created by the Obama administration to consolidate policy courses of action on cybersecurity issues—as short-sighted and tone-deaf, particularly at the height of concern over Russia's nefarious activity toward U.S. political processes. However, the move creates an opportunity to examine whether the overall U.S. approach to cybersecurity has been overly narrow relative to the Russian threat—which itself has demonstrated the need for Washington to forge partnerships with industry and to expand beyond the network-centric aspects of information warfare.
Since the onset of Internet ubiquity in the early 1990s, Moscow has sought agreement with the U.S. to avoid a digital arms race, finding little receptivity on the U.S. side. However, in the early stages of the Obama and Medvedev administrations, the "reset" atmosphere found envoys locked in extensive bilateral consultations on cybersecurity. Despite some initial optimism, by 2012 the primary fruit of these consultations was a better grasp of the intractable differences in how Russia and the U.S. defined the cyber problem and its corollaries. On one hand, the U.S. has held the free flow of information to be sacrosanct—an extension of human rights, critical to innovation—while emphasizing the security of digital networks against intrusion. On the other side, Russia has claimed nations' sovereign "information space" inviolate, seeking to legitimize their exertion of broad control over how the Internet and media can be used within their own borders—to include blocking "undesirable" content. Despite the ongoing debate in the UN and other multilateral fora, bilateral talks on the issue have languished since Putin's 2012 return to the presidency, as overall relations with the U.S. deteriorated.
Judging by 2018 standards, this philosophical deadlock appears both prescient and ironic. In the intervening years, Russia would go on to become the world's foremost transgressor of sovereignty in the information space, flooding the marketplace of ideas with distortion, falsehood, and propaganda. As a result, the laissez faire approach to Internet governance once advocated by the U.S. appeared significantly less advisable; the Council on Foreign Relations last October went so far as to advocate for abandoning it as a strategy. Meanwhile, prospects for increased Internet regulation in both countries have been tempered by Russian authorities' embarrassing inability to sustain it, and U.S. lawmakers' woeful inability to conceptualize it. This state of play likely equates to an inability on either side to conclude, much less ensure compliance to, any meaningful pact on rules and norms in the cyber realm.
In addition to these irreconcilable terms of reference, the impasse has become even more complicated by the inexorable rise to power of a third party: the tech sector. In key aspects, the Internet has become as much a party to the negotiations as it ever was the subject. Giants like Facebook, Twitter, Google, Apple (and Russia's own Telegram) maintain access to incalculable stores of data on American and Russian citizens, commanding trillions of dollars in market share, and technical prowess that increasingly outpaces governance. This data includes models meticulously designed to cater to the psychologies of designated populations—both to drive ad revenues and to influence voting preferences.
In several key aspects, the "information space" can exercise more organic sovereignty than the U.S. or Russia could impose upon it. Professor Joseph Nye of Harvard—a preeminent international relations theorist and former U.S. Assistant Secretary of Defense—proclaimed in 2011 that "states will remain the dominant actor on the world stage, but they will find [it] far more crowded and difficult to control." The resultant "sovereignty gap"—as termed by Lucas Kello, director of Oxford University's Centre for Technology and Global Affairs, argues that the tech sector be viewed and courted by U.S. policymakers as a peer and partner, less a competitor or adversary, in countering the scourge of disinformation. (Ambassador Dan Fried and Alina Polyakova of the Atlantic Council recently proposed just such a public-private coalition.) Such partnership might also engender a greater sense of civic responsibility amongst the tech sector and incentivize innovative solutions to the use of online platforms for malign behavior.
The Obama admin created the Cyber Coordinator post to deal with ongoing Russian cyber threats. (Pete Souza)
Russia's interference in the 2016 elections fused traditional digital hacking and a discipline which Dartmouth researchers in 2003 dubbed "cognitive hacking"—referring to "an attack that relies on changing human users' perceptions and corresponding behaviors in order to be successful." By marshaling its political and military capital to prioritize digital security above all else, Washington's capacity to foresee, much less counter, the weaponization of pilfered, fabricated, distorted, or artificially-amplified information has proven grossly insufficient. To make up for lost ground, U.S. cyber policy should begin to measure its efficacy not only by how vulnerable its digital infrastructure is to intrusion by external actors, but also how vulnerable its public is to manipulation by those same actors.
In addition, the approach taken by some European allies—for whom Russian information warfare poses an existential threat—serves as a model to follow. Harvard University researcher Jed Willard recently contrasted U.S. and European conceptions of that threat, noting that relative to "the emphasis on cyber defenses and social-media algorithms in many American conversations…how rarely northeast-European officials emphasized technical solutions to the problem…Most were far more focused on their populations' psychological resilience." Baltic States like Estonia, Latvia, and Finland are all too keenly aware that the very narratives that hold nations together—the institutional confidence, core beliefs, and foundational truths underpinning them—are prime Russian targets. This stands in stark contrast with heretofore prevalent U.S. concepts, in which the key battlefields are net-based. These concepts, however, must now evolve, as Dmitri Teperik, head of Estonia's International Centre for Defence and Security recently wrote, toward the space "where the cognitive, online, and real worlds meet up."
This space—the battlefield of the mind—is the focal point of Russian cyber policy and doctrine, as examined by the Swedish Defence Research Agency (FOI) in March 2015: "Under this definition, the cognitive domain is fully subsumed into cyberspace." This, however, is not a new development for Moscow, which has long drawn on its unique geopolitical and historical perspective to hone its information warfare strategy. Each modern technological advance – from the photocopier to the smartphone – has been assessed by Russian officialdom for its prospective impact on human psychology and ideology. Sergey Rastorguyev, a pioneer in Russian information warfare theory in the 1990s, focused extensively on sociology, cognition, and consciousness—the processes by which humans construct and shape their realities. The defining characteristic of the information age, in Rastorguyev's telling, is the ease it affords these processes.
Fortunately, U.S. views appear to be evolving, and advocacy for an expanded focus has increased of late. Former Deputy Assistant Secretary of Defense Linton Wells II, who has written extensively on how technological advances intersect with national security policy, recently characterized gaps in the U.S. conceptual framework as a key disadvantage—noting that the U.S. is "not organized, trained, [or] equipped to be agile and effective" in cognitive conflicts. In testimony to the Senate Armed Services Committee in April 2017, RAND Corporation Senior Information Scientist Rand Waltzman decried a myopic focus on the technical dimensions of a problem-set that extends well into the psychosocial dimensions, advocating for a strategic infusion of political capital into the field of "cognitive security…[which] focuses on the exploitation of cognitive biases in large public groups [and] social influence as an end unto itself." This past March, the same committee rightly questioned whether the Department of Defense was properly arrayed to address the threat, with Senator Bill Nelson (D-Fla.) noting that "Russia's information operations troops conduct technical and cognitive operations in an integrated way." Meanwhile, U.S. Cyber Command's newly-released strategy connotes positive shifts—including the recognition that American social cohesion stands at risk from cyber operations, and that, "along with government resiliency, better alignment between private sector technology development and national security goals…can render adversarial activities inconsequential."
Columbia University's James Bone, founder of the first cognitive risk-management consultancy and author of Cognitive Hack: The New Battleground in Cybersecurity, asserts that "a greater, conscientious effort is needed to raise awareness by every organization [and] government official." Expanding the cybersecurity framework to encompass cognitive security will enable policymakers to forge partnerships and initiatives "to better understand these threats, identify and assess new variants of the attack, and develop contingencies rapidly." Bone's proclamation reads like an updated mission statement for a wide range of U.S. political, military, and intelligence officials engaged in cyber issues—a call to scope the problem more broadly than they have to date.