Bioinformatics Platform Head

Dr. Patricio Yankilevich
See CV

PhD Student

Cotarelo, María.

The Bioinformatics Platform works in the organization and analysis of genomes, in the analysis of gene and protein sequences, in the identification and interpretation of genetic variants, in the analysis of gene expression experiments, in the functional analysis of genes and proteins, and its interactions in regulatory processes and metabolic pathways. The goal is to identify biomarkers associated with diseases, in collaboration with the experimental projects developed in the IBioBA. The analysis of molecular and genetic variation within the population could be the basis of an individualized treatment.

The Bioinformatics Platform provides services not only to IBioBA researchers but collaborates with external scientists and organizations for data analysis, and custom software and databases development.

The professionals working in the Bioinformatics Platform have experience in the computational analysis of massive sequencing data (Next Generation Sequencing – NGS), such as analysis of gene expression profiles, identification of variants, gene regulation, comparative genomics, assembly of genomes, and the analysis and visual interpretation of the clinical genome.

All our projects are tailor made, and include from the development of databases, software and algorithms to the study of genome, transcriptome, metabolome and proteome data, and their statistical analysis. We have the know how to adapt and participate in different research projects that require collecting, systematizing, analyzing and integrating different levels of information.

GenIO: Clinical Genomics Assistant Tool.

INSECT 2.0: IN-silico SEarch for Co-occurring Transcription factors.


  • Malena Manzi, Martín Palazzo, María Elena Knott, Pierre Beauseroy, Patricio Yankilevich, María Isabel Giménez, and María Eugenia Monge.
    Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma.
    Journal of Proteome Research Article. 20:841-857 (2021).
  • María Sol Ruiz, María Belén Sánchez, Simone Bonecker, Carolina Furtado, Daniel Isaac Koile, Patricio Yankilevich, Santiago Cranco, María del Rosario Custidiano, Josefina Freitas, Beatriz Moiraghi, Mariel Ana Pérez, Carolina Pavlovsky, Ana Inés Varela, Verónica Ventriglia, Julio César Sánchez Ávalos, Irene Larripa, Ilana Zalcberg, José Mordoh, Peter Valent, Michele Bianchini.
    miRNome profiling of LSC-enriched CD34+CD38-CD26+ fraction in Ph+ CML-CP from Argentinean patients: a potential new pharmacogenomic tool.
    Frontiers in Pharmacology 11: 2231 (2020).
  • Martín Palazzo, P. Beauseroy and Patricio Yankilevich.
    Unsupervised feature selection for tumor profiles using autoencoders and kernel methods.
    2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Viña del Mar, 2020, pp. 1-8.
  • Podaza E, Carri I, Aris M, von Euw E, Bravo AI, Blanco P, Ortiz Wilczyñski JM, Koile D, Yankilevich P, Nielsen M, Mordoh J, Barrio MM.
    Evaluation of T-Cell Responses Against Shared Melanoma Associated Antigens and Predicted Neoantigens in Cutaneous Melanoma Patients Treated With the CSF-470 Allogeneic Cell Vaccine Plus BCG and GM-CSF.
    Front Immunol. 11:1147 (2020)
  • Vogl AM, Phu L, Becerra R, Giusti SA, Verschueren E, Hinkle TB, Bordenave MD, Adrian M, Heidersbach A, Yankilevich P, Stefani FD, Wurst W, Hoogenraad CC, Kirkpatrick DS, Refojo, D., Sheng, M.
    Global site-specific neddylation profiling reveals that NEDDylated cofilin regulates actin dynamics.
    Nature Structural and Molecular Biology 27:210-20 (2020).
  • Palazzo M, Beauseroy P, Yankilevich P.
    A Pan-cancer Somatic Mutation Embedding using Autoencoders.
    BMC Bioinformatics 20:655 (2019).
  • Pazos Obregón F, Palazzo M, Soto P, Guerberoff G, Yankilevich P, Cantera R.
    An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach.
    BMC Genomics 20:1011 (2019).
  • Koile, D; Cordoba, M; de Sousa Serro, M; Kauffman, M; Yankilevich, P.
    GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases.
    BMC Bioinformatics 19:25 (2018)
  • Parra, R.G.; Rohr, C.O.; Koile, D.; Perez-Castro, C.; Yankilevich, P.
    INSECT 2.0: a web-server for genome-wide cis-regulatory modules prediction.
    Bioinformatics 32:1229-31 (2016)
  • Rohr, C.O.; Parra, R.G.; Yankilevich, P.;Perez Castro, C.
    INSECT: In silico search for co-occurring transcription factors.
    Bioinformatics 29:2852-2858 (2013)
  • Díaz Flaqué, C.M., Galigniana, M. N., Béguelin, W., Vicario, R., Proietti, J.C., Cordo Russo, R., Rivas, A.M., Tkach, M., Guzmán, P., Roa, C. J., Maronna, E., Pineda, V., Muñoz S., Mercogliano, F.M., Charreau, H.E., Yankilevich, P., Schillaci, R., Elizalde V.P.
    Progesterone receptor assembly of a transcriptional complex along with activator protein 1, signal transducer and activator of transcription 3 and ErbB-2 governs breast cancer growth and predicts response to endocrine therapy.
    Breast Cancer Research 15:R118 (2013)
  • Andreu Alibés; Patricio Yankilevich; Andrés Cañada; Ramón Díaz-Uriarte.
    IDconverter and IDClight: Conversion and annotation of gene and protein IDs.
    BMC Bioinformatics 8:9 (2007)
  • Ribas G, Gonzalez-Neira A, Salas A, Milne RL, Vega A, Carracedo B, Gonzalez E, Barroso E, Fernandez LP, Yankilevich P, Robledo M, Carracedo A, Benitez J.
    Evaluating HapMap SNP data transferability in a large-scale genotyping project involving 175 cancer associated genes.
    Human Genetics 118:669-679 (2006)
  • Vaquerizas JM, Conde L, Yankilevich P, Cabezon A, Minguez P, Diaz-Uriarte R, Al-Shahrour F, Herrero J, Dopazo J.
    GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data.
    Nucleic Acids Res Vol. 33, Web Server issue (2005)