No abstract

We have implemented in Python the COmparative GENomic Toolkit, a fully integrated and thoroughly tested framework for novel probabilistic analyses of biological sequences, devising workflows, and generating publication quality graphics. PyCogent includes connectors to remote databases, built-in generalized probabilistic techniques for working with biological sequences, and controllers for third-party applications. The toolkit takes advantage of parallel architectures and runs on a range of hardware and operating systems, and is available under the general public license from http://sourceforge.net/projects/pycogent. RationaleThe genetic divergence of species is affected by both DNA metabolic processes and natural selection. Processes contributing to genetic variation that are undetectable with intraspecific data may be detectable by inter-specific analyses because of the accumulation of signal over evolutionary time scales. As a consequence of the greater statistical power, there is interest in applying comparative analyses to address an increasing number and diversity of problems, in particular analyses that integrate sequence and phenotype. Significant barriers that hinder the extension of comparative analyses to exploit genome indexed phenotypic data include the narrow focus of most analytical tools, and the diverse array of data sources, formats, and tools available. Theoretically coherent integrative analyses can be conducted by combining probabilistic models of different aspects of genotype. Probabilistic models of sequence change underlie many core bioinformatics tasks, including similarity search, sequence alignment, phylogenetic inference, and ancestral state reconstruction. Probabilistic models allow usage of likelihood inference, a powerful approach from statistics, to establish the significance of differences in support of competing hypotheses. Linking different analyses through a shared and explicit probabilistic model of sequence change is thus extremely valuable, and provides a basis for generalizing analyses to more complex models of evolution (for example, to incorporate dependence between sites). Numerous studies have established how biological factors representing metabolic or selective influences can be represented in substitution models as specific parameters that affect rates of interchange between sequence motifs or the spatial occurrence of such rates [1][2][3][4]. Given this solid grounding, it is desirable to have a toolkit that allows flexible parameterization of probabilistic models and interchange of appropriate modules.There are many existing software packages that can manipulate biological sequences and structures, but few allow specification of both truly novel statistical models and detailed workflow control for genome scale datasets. Traditional phylogenetic analysis applications [5,6] typically provide a number of explicitly defined statistical models that are difficult to modify. One exception in which the parameterization of entirely novel substitution models was poss...

The light-induced turnover of P700 was measured spectrophotometrically in a wide variety of algae and some photosynthetic mutants. Analysis of the postillumination recovery of P700+ revealed that the apparent first-order rate constant for reduction via the cyclic pathway was much lower that that via the noncyclic pathway. After activation of photosystems 1 and 2 the half-time for reduction of P700+ was 5-20 ms, whereas after activation of primarily photosystem 1 a longer half-time of ca. 150 ms was observed. The extent of the photooxidation of P700 was the same in both regimes of illumination. The longer half-time was also noted after inhibition of photosystem 2 by 3-(3,4-dichlorophenyl)-1,1-dimethylurea or mild heat shock and in mutant algae known to lack a functional photosystem 2. No signal was observed in mutants lacking P700 itself but those strains lacking either plastocyanin or cytochrome f were capable of a very slow turnover (reduction t 1/2 greater than 500 ms at room temperature). This very slow turnover was not affected by carbonyl cyanide m-chlorophenylhydrazone or the plastoquinone antagonist, 2,5-dibromo-3-methyl-6-isopropyl-p-benzoquinone, indicating that the pathway for reduction of P700+ in these mutants is not energy linked and does not utilize the intersystem electron transport chain. The slow, 150 ms, reduction of P700+ due to cyclic flow was not observed when cells were engaged in photosynthesis at high-light intensities. The data are interpreted as evidence for the involvement of the total functional pool of P700 in both electron transport pathways, and we suggest that cyclic electron transport does not contribute to photosynthesis in oxygen-evolving autotrophs.

No abstract

No abstract