Design and synthesis of biofunctional magnetic/fluorescent glyco-nanoparticles and quantum dots and their application as specific molecular imaging probes

206 p.

Association analyses of DNA polymorphisms in bovine SREBP-1, LXRα,FADS1 genes with fatty acid composition in Canadian commercial crossbred beef steers

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.

Preprocess and data analysis techniques for affymetrix DNA microarrays using bioconductor: a case study in Alzheimer disease

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