Instrument Calibration of the Interface Region Imaging Spectrograph (IRIS) Mission.

The Interface Region Imaging Spectrograph (IRIS) is a NASA small explorer mission that provides high-resolution spectra and images of the Sun in the 133 - 141 nm and 278 - 283 nm wavelength bands. The IRIS data are archived in calibrated form and made available to the public within seven days of observing. The calibrations applied to the data include dark correction, scattered light and background correction, flat fielding, geometric distortion correction, and wavelength calibration. In addition, the IRIS team has calibrated the IRIS absolute throughput as a function of wavelength and has been tracking throughput changes over the course of the mission. As a resource for the IRIS data user, this article describes the details of these calibrations as they have evolved over the first few years of the mission. References to online documentation provide access to additional information and future updates.

Genview and Gencode : a pair of programs to test theories of genetic code evolution.

GENVIEW: and GENCODE: are tools for testing the adaptive nature of a genetic code under different assumptions about patterns of genetic error and the nature of amino acid similarity. GENVIEW: provides a user friendly, point-and-click interface by which a user may reproduce and extend analysis of the adaptive properties of the standard genetic code or any of its secondary derivatives. GENVIEW: is a graphical user interface (GUI) program which runs on Linux, Unix and Microsoft Windows platforms and is based on the GTKf + toolkit. GENVIEW: outputs ASCII configuration files which are interpreted by GENCODE: to perform an analysis. GENCODE: is available for the same platforms as GENVIEW.

A simple model based on mutation and selection explains trends in codon and amino-acid usage and GC composition within and across genomes.

BACKGROUND: Correlations between genome composition (in terms of GC content) and usage of particular codons and amino acids have been widely reported, but poorly explained. We show here that a simple model of processes acting at the nucleotide level explains codon usage across a large sample of species (311 bacteria, 28 archaea and 257 eukaryotes). The model quantitatively predicts responses (slope and intercept of the regression line on genome GC content) of individual codons and amino acids to genome composition. RESULTS: Codons respond to genome composition on the basis of their GC content relative to their synonyms (explaining 71-87% of the variance in response among the different codons, depending on measure). Amino-acid responses are determined by the mean GC content of their codons (explaining 71-79% of the variance). Similar trends hold for genes within a genome. Position-dependent selection for error minimization explains why individual bases respond differently to directional mutation pressure. CONCLUSIONS: Our model suggests that GC content drives codon usage (rather than the converse). It unifies a large body of empirical evidence concerning relationships between GC content and amino-acid or codon usage in disparate systems. The relationship between GC content and codon and amino-acid usage is ahistorical; it is replicated independently in the three domains of living organisms, reinforcing the idea that genes and genomes at mutation/selection equilibrium reproduce a unique relationship between nucleic acid and protein composition. Thus, the model may be useful in predicting amino-acid or nucleotide sequences in poorly characterized taxa.

Rewiring the keyboard: evolvability of the genetic code.

The genetic code evolved in two distinct phases. First, the 'canonical' code emerged before the last universal ancestor; subsequently, this code diverged in numerous nuclear and organelle lineages. Here, we examine the distribution and causes of these secondary deviations from the canonical genetic code. The majority of non-standard codes arise from alterations in the tRNA, with most occurring by post-transcriptional modifications, such as base modification or RNA editing, rather than by substitutions within tRNA anticodons.

Testing a biosynthetic theory of the genetic code: fact or artifact?

It has long been conjectured that the canonical genetic code evolved from a simpler primordial form that encoded fewer amino acids [e.g., Crick, F. H. C. (1968) J. Mol. Biol. 38, 367-379]. The most influential form of this idea, "code coevolution" [Wong, J. T.-F. (1975) Proc. Natl. Acad. Sci. USA 72, 1909-1912], proposes that the genetic code coevolved with the invention of biosynthetic pathways for new amino acids. It further proposes that a comparison of modern codon assignments with the conserved metabolic pathways of amino acid biosynthesis can inform us about this history of code expansion. Here we re-examine the biochemical basis of this theory to test the validity of its statistical support. We show that the theory's definition of "precursor-product" amino acid pairs is unjustified biochemically because it requires the energetically unfavorable reversal of steps in extant metabolic pathways to achieve desired relationships. In addition, the theory neglects important biochemical constraints when calculating the probability that chance could assign precursor-product amino acids to contiguous codons. A conservative correction for these errors reveals a surprisingly high 23% probability that apparent patterns within the code are caused purely by chance. Finally, even this figure rests on post hoc assumptions about primordial codon assignments, without which the probability rises to 62% that chance alone could explain the precursor-product pairings found within the code. Thus we conclude that coevolution theory cannot adequately explain the structure of the genetic code.

Early fixation of an optimal genetic code.

The evolutionary forces that produced the canonical genetic code before the last universal ancestor remain obscure. One hypothesis is that the arrangement of amino acid/codon assignments results from selection to minimize the effects of errors (e.g., mistranslation and mutation) on resulting proteins. If amino acid similarity is measured as polarity, the canonical code does indeed outperform most theoretical alternatives. However, this finding does not hold for other amino acid properties, ignores plausible restrictions on possible code structure, and does not address the naturally occurring nonstandard genetic codes. Finally, other analyses have shown that significantly better code structures are possible. Here, we show that if theoretically possible code structures are limited to reflect plausible biological constraints, and amino acid similarity is quantified using empirical data of substitution frequencies, the canonical code is at or very close to a global optimum for error minimization across plausible parameter space. This result is robust to variation in the methods and assumptions of the analysis. Although significantly better codes do exist under some assumptions, they are extremely rare and thus consistent with reports of an adaptive code: previous analyses which suggest otherwise derive from a misleading metric. However, all extant, naturally occurring, secondarily derived, nonstandard genetic codes do appear less adaptive. The arrangement of amino acid assignments to the codons of the standard genetic code appears to be a direct product of natural selection for a system that minimizes the phenotypic impact of genetic error. Potential criticisms of previous analyses appear to be without substance. That known variants of the standard genetic code appear less adaptive suggests that different evolutionary factors predominated before and after fixation of the canonical code. While the evidence for an adaptive code is clear, the process by which the code achieved this optimization requires further attention.

Selection, history and chemistry: the three faces of the genetic code.

The genetic code might be a historical accident that was fixed in the last common ancestor of modern organisms. 'Adaptive', 'historical' and 'chemical' arguments, however, challenge such a 'frozen accident' model. These arguments propose that the current code is somehow optimal, reflects the expansion of a more primitive code to include more amino acids, or is a consequence of direct chemical interactions between RNA and amino acids, respectively. Such models are not mutually exclusive, however. They can be reconciled by an evolutionary model whereby stereochemical interactions shaped the initial code, which subsequently expanded through biosynthetic modification of encoded amino acids and, finally, was optimized through codon reassignment. Alternatively, all three forces might have acted in concert to assign the 20 'natural' amino acids to their present positions in the genetic code.

The genetic code is one in a million.

Statistical and biochemical studies of the genetic code have found evidence of nonrandom patterns in the distribution of codon assignments. It has, for example, been shown that the code minimizes the effects of point mutation or mistranslation: erroneous codons are either synonymous or code for an amino acid with chemical properties very similar to those of the one that would have been present had the error not occurred. This work has suggested that the second base of codons is less efficient in this respect, by about three orders of magnitude, than the first and third bases. These results are based on the assumption that all forms of error at all bases are equally likely. We extend this work to investigate (1) the effect of weighting transition errors differently from transversion errors and (2) the effect of weighting each base differently, depending on reported mistranslation biases. We find that if the bias affects all codon positions equally, as might be expected were the code adapted to a mutational environment with transition/transversion bias, then any reasonable transition/transversion bias increases the relative efficiency of the second base by an order of magnitude. In addition, if we employ weightings to allow for biases in translation, then only 1 in every million random alternative codes generated is more efficient than the natural code. We thus conclude not only that the natural genetic code is extremely efficient at minimizing the effects of errors, but also that its structure reflects biases in these errors, as might be expected were the code the product of selection.