Miguel A. Andrade-Navarro
University of Mainz
261 Papers
893 Citations
Miguel A. Andrade-Navarro is an academic researcher from University of Mainz. The author has contributed to research in topics: Biology & Gene. The author has an hindex of 52, co-authored 217 publications. Previous affiliations of Miguel A. Andrade-Navarro include Ottawa Hospital Research Institute & Max Delbrück Center for Molecular Medicine.
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Papers
The features of polyglutamine regions depend on their evolutionary stability
TL;DR: It is concluded that a taxa-driven evolutionary analysis is of the highest importance for the comprehensive study of any feature of polyglutamine regions, as stable polyQ have more interactors than unstable polyQ.
Assessing the reliability of gene expression measurements in very-low-numbers of human monocyte-derived macrophages
TL;DR: This work demonstrates that it is possible to employ samples with a scarce number of cells in experimental studies and encourages the application of this approach on other cell types, as well as statistical and computational analysis of gene expression profiles.
Evolution-guided evaluation of the inverted terminal repeats of the synthetic transposon Sleeping Beauty
TL;DR: The consensus sequence did not support enhanced transposition, suggesting alternative mechanisms responsible for the preferential amplification of these sequence variants in the salmon genome.
orthoFind Facilitates the Discovery of Homologous and Orthologous Proteins.
TL;DR: A new web application called orthoFind is presented, which allows a quick search for homologous and orthologous proteins given one or more query sequences, allowing a recurrent and exhaustive search against reference proteomes, and being able to include user databases.
Assessment of curated phenotype mining in neuropsychiatric disorder literature.
Jean-Fred Fontaine,Josef Priller,Eike Jakob Spruth,Carol Perez-Iratxeta,Miguel A. Andrade-Navarro +4 more
TL;DR: This study shows that starting from expertise covering a limited set of neurological disorders and using text and data mining methods, meaningful and novel associations regarding genes, chemicals and phenotypes can be derived for an expanded set of neuropsychiatric disorders.
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