A large number of loci for genetic diseases have been mapped on the human genome and a group of hereditary diseases among them have thus far proven unsuccessful to clone. It is conceivable that such "unclonable" diseases are not linked to abnormalities of protein coding genes (PCGs), but of non-coding RNAs (ncRNAs). We developed a novel approach termed OMiR (OMIM and miRNAs), to test whether microRNAs (miRNAs) exhibit any associations with mapped genetic diseases not yet associated with a PCG.
View Article and Find Full Text PDFWe studied miRNA profiles in 4419 human samples (3312 neoplastic, 1107 nonmalignant), corresponding to 50 normal tissues and 51 cancer types. The complexity of our database enabled us to perform a detailed analysis of microRNA (miRNA) activities. We inferred genetic networks from miRNA expression in normal tissues and cancer.
View Article and Find Full Text PDFWe devised a novel procedure to identify human cancer genes acting in a recessive manner. Our strategy was to combine the contributions of the different types of genetic alterations to loss of function: amino-acid substitutions, frame-shifts, gene deletions. We studied over 20,000 genes in 3 Gigabases of coding sequences and 700 array comparative genomic hybridizations.
View Article and Find Full Text PDFBackground: DNA microarrays are among the most widely used technical platforms for DNA and RNA studies, and issues related to microarrays sensitivity and specificity are therefore of general importance in life sciences. Compatible solutes are derived from hyperthermophilic microorganisms and allow such microorganisms to survive in environmental and stressful conditions. Compatible solutes show stabilization effects towards biological macromolecules, including DNA.
View Article and Find Full Text PDFMotivation: Microarray and other genome-wide technologies allow a global view of gene expression that can be used in several ways and whose potential has not been yet fully discovered. Functional insight into expression profiles is routinely obtained by using gene ontology terms associated to the cellular genes. In this article, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO).
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