983 resultados para Ribosomal Dna
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A metric representation of DNA sequences is borrowed from symbolic dynamics. In view of this method, the pattern seen in the chaos game representation of DNA sequences is explained as the suppression of certain nucleotide strings in the DNA sequences. Frequencies of short nucleotide strings and suppression of the shortest ones in the DNA sequences can be determined by using the metric representation.
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Recurrence plot technique of DNA sequences is established on metric representation and employed to analyze correlation structure of nucleotide strings. It is found that, in the transference of nucleotide strings, a human DNA fragment has a major correlation distance, but a yeast chromosome's correlation distance has a constant increasing. (C) 2004 Elsevier B.V All rights reserved.
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Two previously reported DNA polymorphisms of sterol regulatory element binding transcription factor 1 (SREBP1) and liver X receptor alpha (LXRα) and two DNA polymorphisms of fatty acid desaturase 1 (FADS1) were evaluated for associations with fatty acids in brisket adipose tissue of Canadian cross-bred beef steers. The polymorphism of 84 bp insert/deletion in intron 5 of SREBP1 was significantly associated with the concentration of 9c C17:1 (P=0.013). The G>A single nucleotide polymorphism (SNP) in the exon 4 of LXRα gene was associated with the concentration of 9c, 11t C18:2 (P=0.04), sum of conjugated linoleic acids (CLA) (P=0.025) and 11c C20:1(P=0.042). Two DNA polymorphisms in the promoter region of FADS1, deletion/insertion of ->GTG in rs133053720 and SNP A>G in rs42187276, were significantly associated with concentrations of C17:0 iso, C17:0 ai, total branched chain fatty acids (BFA), 12t C18:1, 13t/14t C18:1, 15t C18:1, and 13c C18:1 (P<0.05). Further studies are needed to validate the associations and to delineate the roles of the gene polymorphisms in determining the fatty acid composition in beef tissues.
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[ENG]Aiming at an integrated and mechanistic view of the early biological effects of selected metals in the marine sentinel organism Mytilus galloprovincialis, we exposed mussels for 48 hours to 50, 100 and 200 nM solutions of equimolar Cd, Cu and Hg salts and measured cytological and molecular biomarkers in parallel. Focusing on the mussel gills, first target of toxic water contaminants and actively proliferating tissue, we detected significant dose-related increases of cells with micronuclei and other nuclear abnormalities in the treated mussels, with differences in the bioconcentration of the three metals determined in the mussel flesh by atomic absorption spectrometry. Gene expression profiles, determined in the same individual gills in parallel, revealed some transcriptional changes at the 50 nM dose, and substantial increases of differentially expressed genes at the 100 and 200 nM doses, with roughly similar amounts of up- and down-regulated genes. The functional annotation of gill transcripts with consistent expression trends and significantly altered at least in one dose point disclosed the complexity of the induced cell response. The most evident transcriptional changes concerned protein synthesis and turnover, ion homeostasis, cell cycle regulation and apoptosis, and intracellular trafficking (transcript sequences denoting heat shock proteins, metal binding thioneins, sequestosome 1 and proteasome subunits, and GADD45 exemplify up-regulated genes while transcript sequences denoting actin, tubulins and the apoptosis inhibitor 1 exemplify down-regulated genes). Overall, nanomolar doses of co-occurring free metal ions have induced significant structural and functional changes in the mussel gills: the intensity of response to the stimulus measured in laboratory supports the additional validation of molecular markers of metal exposure to be used in Mussel Watch programs
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DNA microarray, or DNA chip, is a technology that allows us to obtain the expression level of many genes in a single experiment. The fact that numerical expression values can be easily obtained gives us the possibility to use multiple statistical techniques of data analysis. In this project microarray data is obtained from Gene Expression Omnibus, the repository of National Center for Biotechnology Information (NCBI). Then, the noise is removed and data is normalized, also we use hypothesis tests to find the most relevant genes that may be involved in a disease and use machine learning methods like KNN, Random Forest or Kmeans. For performing the analysis we use Bioconductor, packages in R for the analysis of biological data, and we conduct a case study in Alzheimer disease. The complete code can be found in https://github.com/alberto-poncelas/ bioc-alzheimer