A BIOinformatics research unit with a TRANSlational interest.

In the Translational Bioinformatics Unit (TransBio) we aim to address relevant challenges and bottlenecks in the course of translating basic science research into clinical knowledge. TransBio is a multi-disciplinary team formed by individuals with different combinations of biological, clinical and quantitative knowledge.

TransBio team is located at Navarrabiomed which is a “biomedical research centre of the Government of Navarre that promotes and facilitates the research of public healthcare professionals. Its location in the heart of the Hospital Complex of Navarre encourages close proximity and collaboration between researchers and professionals working in clinical and healthcare environments”. TransBio has long-term established collaborations with teams at Karolinska Institutet, King´s College London  and KAUST.

If you are interested in working with us, see here for details (if an offer is available) or please contact us directly.

URGENT: we are recruiting postdoctoral researchers.

Recent publications of interest (see the entire list here):

  • Planell N, Lagani V, Sebastian-Leon P, van der Kloet F, Ewing E, Karathanasis N, et al. STATegra: Multi-Omics Data Integration – A Conceptual Scheme With a Bioinformatics Pipeline. Front Genet. 2021;12:143.
  • Rad Pour S, Pico de Coana Y, Martinez de Morentin X, et al. Predicting anti-PD-1 responders in malignant melanoma from the frequency of S100A9+ monocytes in the blood. Journal for ImmunoTherapy of Cancer, 2021. to appear.
  • Moreno-Indias I, Lahti L, Nedyalkova M, Elbere I, Roshchupkin G, Adilovic M, et al. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions . Front Microbiol. 2021;12:277
  • Gomez-Cabrero D, Walter S, Abugessaisa I, Miñambres-Herraiz R, Palomares LB, Butcher L, et al. A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts. GeroScience. 2021 DOI: 10.1007/s11357-021-00334-0 Frailomic Consortia paper
  • Ewing E, Planell-Picola N, Jagodic M, Gomez-Cabrero D. GeneSetCluster: a tool for summarizing and integrating gene-set analysis results. BMC Bioinformatics. 2020;21(1):443. link
  • Ruiz-Villalba A, Romero JP, Hernandez SC, et al. Single-Cell RNA-seq Analysis Reveals a Crucial Role for Collagen Triple Helix Repeat Containing 1 (CTHRC1) Cardiac Fibroblasts after Myocardial Infarction. Circulation. 2020.
  • Tarazona S, Balzano-Nogueira L, Gómez-Cabrero D, et al. Harmonization of quality metrics and power calculation in multi-omic studies. Nat Commun. Springer US; 2020;11: 1–13.
  • Carr VR, Witherden E, Lee S, Shoaie S, Mullany P, Proctor GB, et al. Abundance and diversity of resistomes differ between healthy human oral cavities and gut. Nature Communications; 2020; 11:693. link
  • International Multiple Sclerosis Genetics Consortium, Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility, Science, 2019, 365:6460.
  • Carlström KE, Ewing E, Granqvist M, Gyllenberg A, Aeinehband S, Enoksson SL, et al. Therapeutic efficacy of dimethyl fumarate in relapsing-remitting multiple sclerosis associates with ROS pathway in monocytes. Nat Commun; 2019;10: 3081.
  • Ferreirós-Vidal I, Carroll T, Zhang T, Lagani V, Ramirez RN, Ing-Simmons E, et al. Feedforward regulation of Myc coordinates lineage-specific with housekeeping gene expression during B cell progenitor cell differentiation. PLOS Biol. 2019;17.
  • Ewing E, Kular L, Fernandes SJ, Karathanasis N, Lagani V, Ruhrmann S, et al. Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression. EBioMedicine. Elsevier; 2019.