10 resultados para automation of fit analysis

em National Center for Biotechnology Information - NCBI


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A rapidly growing area of genome research is the generation of expressed sequence tags (ESTs) in which large numbers of randomly selected cDNA clones are partially sequenced. The collection of ESTs reflects the level and complexity of gene expression in the sampled tissue. To date, the majority of plant ESTs are from nonwoody plants such as Arabidopsis, Brassica, maize, and rice. Here, we present a large-scale production of ESTs from the wood-forming tissues of two poplars, Populus tremula L. × tremuloides Michx. and Populus trichocarpa ‘Trichobel.’ The 5,692 ESTs analyzed represented a total of 3,719 unique transcripts for the two cDNA libraries. Putative functions could be assigned to 2,245 of these transcripts that corresponded to 820 protein functions. Of specific interest to forest biotechnology are the 4% of ESTs involved in various processes of cell wall formation, such as lignin and cellulose synthesis, 5% similar to developmental regulators and members of known signal transduction pathways, and 2% involved in hormone biosynthesis. An additional 12% of the ESTs showed no significant similarity to any other DNA or protein sequences in existing databases. The absence of these sequences from public databases may indicate a specific role for these proteins in wood formation. The cDNA libraries and the accompanying database are valuable resources for forest research directed toward understanding the genetic control of wood formation and future endeavors to modify wood and fiber properties for industrial use.

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We describe a genome-wide characterization of mRNA transcript levels in yeast grown on the fatty acid oleate, determined using Serial Analysis of Gene Expression (SAGE). Comparison of this SAGE library with that reported for glucose grown cells revealed the dramatic adaptive response of yeast to a change in carbon source. A major fraction (>20%) of the 15,000 mRNA molecules in a yeast cell comprised differentially expressed transcripts, which were derived from only 2% of the total number of ∼6300 yeast genes. Most of the mRNAs that were differentially expressed code for enzymes or for other proteins participating in metabolism (e.g., metabolite transporters). In oleate-grown cells, this was exemplified by the huge increase of mRNAs encoding the peroxisomal β-oxidation enzymes required for degradation of fatty acids. The data provide evidence for the existence of redox shuttles across organellar membranes that involve peroxisomal, cytoplasmic, and mitochondrial enzymes. We also analyzed the mRNA profile of a mutant strain with deletions of the PIP2 and OAF1 genes, encoding transcription factors required for induction of genes encoding peroxisomal proteins. Induction of genes under the immediate control of these factors was abolished; other genes were up-regulated, indicating an adaptive response to the changed metabolism imposed by the genetic impairment. We describe a statistical method for analysis of data obtained by SAGE.

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In three experiments, electric brain waves of 19 subjects were recorded under several different experimental conditions for two purposes. One was to test how well we could recognize which sentence, from a set of 24 or 48 sentences, was being processed in the cortex. The other was to study the invariance of brain waves between subjects. As in our earlier work, the analysis consisted of averaging over trials to create prototypes and test samples, to both of which Fourier transforms were applied, followed by filtering and an inverse transformation to the time domain. A least-squares criterion of fit between prototypes and test samples was used for classification. In all three experiments, averaging over subjects improved the recognition rates. The most significant finding was the following. When brain waves were averaged separately for two nonoverlapping groups of subjects, one for prototypes and the other for test samples, we were able to recognize correctly 90% of the brain waves generated by 48 different sentences about European geography.

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In two experiments, electric brain waves of 14 subjects were recorded under several different conditions to study the invariance of brain-wave representations of simple patches of colors and simple visual shapes and their names, the words blue, circle, etc. As in our earlier work, the analysis consisted of averaging over trials to create prototypes and test samples, to both of which Fourier transforms were applied, followed by filtering and an inverse transformation to the time domain. A least-squares criterion of fit between prototypes and test samples was used for classification. The most significant results were these. By averaging over different subjects, as well as trials, we created prototypes from brain waves evoked by simple visual images and test samples from brain waves evoked by auditory or visual words naming the visual images. We correctly recognized from 60% to 75% of the test-sample brain waves. The general conclusion is that simple shapes such as circles and single-color displays generate brain waves surprisingly similar to those generated by their verbal names. These results, taken together with extensive psychological studies of auditory and visual memory, strongly support the solution proposed for visual shapes, by Bishop Berkeley and David Hume in the 18th century, to the long-standing problem of how the mind represents simple abstract ideas.

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A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.

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The African trypanosome, Trypanosoma brucei, has been shown to undergo genetic exchange in the laboratory, but controversy exists as to the role of genetic exchange in natural populations. Much of the analysis to date has been derived from isoenzyme or randomly amplified polymorphic DNA data with parasite material from a range of hosts and geographical locations. These markers fail to distinguish between the human infective (T. b. rhodesiense) and nonhuman infective (T. b. brucei) “subspecies” so that parasites derived from hosts other than humans potentially contain both subspecies. To overcome some of the inherent problems with the use of such markers and diverse populations, we have analyzed a well-defined population from a discrete geographical location (Busoga, Uganda) using three recently described minisatellite markers. The parasites were primarily isolated from humans and cattle with the latter isolates further characterized by their ability to resist lysis by human serum (equivalent to human infectivity). The minisatellite markers show high levels of polymorphism, and from the data obtained we conclude that T. b. rhodesiense is genetically isolated from T. b. brucei and can be unambiguously identified by its multilocus genotype. Analysis of the genotype frequencies in the separated T. b. brucei and T. b. rhodesiense populations shows the former has an epidemic population structure whereas the latter is clonal. This finding suggests that the strong linkage disequilibrium observed in previous analyses, where human and nonhuman infective trypanosomes were not distinguished, results from the treatment of two genetically isolated populations as a single population.

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Replication-incompetent retroviral vectors encoding histochemical reporter genes have been used for studying lineal relationships in a variety of species. A crucial element in the interpretation of data generated by this method is the identification of sibling relationships, or clonal boundaries. The use of a library of viruses in which each member is unique can greatly facilitate this aspect of the analysis. A previously reported murine retroviral library containing about 80 members demonstrated the utility of the library approach. However, the relatively low number of tags in the murine library necessitated using low infection rates in order to give confidence in clonal assignments. To obviate the need for low infection rates, a far more complex library was created and characterized. The CHAPOL library was constructed such that each member encodes a histochemical reporter gene and has a DNA tag derived from a degenerate oligonucleotide pool synthesized to have a complexity of > 1 x 10(7). The library was tested after infection of cells in vitro or in vivo. The DNA tag from each histochemically labeled cell or clone of cells was recovered by PCR and sequenced for unambiguous identification. Three hundred and twenty tags have been identified after infection, and so far no tag has been seen to result from more than one independent infection. Thus, an equal distribution of inserts is suggested, and Monte Carlo analysis predicts a complexity of > 10(4) members.