J
Juan A. G. Ranea
Researcher at University of Málaga
Publications - 59
Citations - 1120
Juan A. G. Ranea is an academic researcher from University of Málaga. The author has contributed to research in topics: Gene & Medicine. The author has an hindex of 15, co-authored 52 publications receiving 949 citations. Previous affiliations of Juan A. G. Ranea include Carlos III Health Institute & University College London.
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Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome.
TL;DR: A new approach to predict protein interactions from phylogenetic trees is presented, which incorporates information on the overall evolutionary histories of the species (i.e. the canonical "tree of life") in order to correct by the expected background similarity due to the underlying speciation events.
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Protein Superfamily Evolution and the Last Universal Common Ancestor (LUCA)
TL;DR: Functional analysis of a set of 140 ancestral protein domains reveals a genetically complex LUCA with practically all the essential functional systems present in extant organisms, supporting the theory that life achieved its modern cellular status much before the main kingdom separation.
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Evolution of protein superfamilies and bacterial genome size.
TL;DR: The structural annotation of 56 different bacterial species based on the assignment of genes to 816 evolutionary superfamilies in the CATH domain structure database is presented, and the recurrence of specific superfam families within and across the genomes is analysed.
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Regulatory variants: from detection to predicting impact.
TL;DR: Methods and techniques for discovering disease-associated non-coding variants using sequencing technologies, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches are described.
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Microeconomic principles explain an optimal genome size in bacteria
TL;DR: In this paper, the same microeconomics principles that define the optimum size in a factory can also explain the existence of a statistical optimum in bacterial genome size, which is reached when the bacterial genome obtains the maximum metabolic complexity (revenue) for minimal regulatory genes (logistic cost).