Bioinformatics Platform Head

Dr. Patricio Yankilevich
See CV

PhD Students

Daniel Koile, Martín Palazzo.

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.


  • 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)