Research and projectsResearch groups

Research groups at IBA FM MU Bioinformatics in Translational Research (BTR)

Leader of the research group Leader of the research group: Eva Budinská, Ph.D.

The group of Bioinformatics in Translational Research at the Institute of Biostatistics and Analyses at the Faculty of Medicine of the Masaryk University (IBA FM MU) is dedicated to the analysis of high-throughput genomic and proteomic data (microarrays, NGS, mass spectrometry, etc.), mainly in the translational research.

Leader of the research group:

Eva Budinská, Ph.D.

Eva Budinská, Ph.D.


The group is composed of experts with a combined training in biology, mathematics, programming and data analysis. We focus on these research areas:

  • applied analysis in cancer translational research (molecular subtyping, predictive and prognostic models, pathway analysis, differential gene/protein mutation/expression analysis...)
  • development of methods and tools for complex data analysis from high-throughput molecular experiments
  • tools for the analysis of the next-gen sequencing data

For more information, visit

List of projects

  • Integrative development of multimodal risk score for the estimate of relapse in patients with breast carcinoma (Internal Grant Agency of Ministry of Health, Czech Republic IGA MZ ČZ NT / 14134)
  • MErCuRIC - A phase Ib/II study of MEK1/2 inhibitor PD-0325901 with cMET inhibitor PF-02341066 in KRASMT and KRASWT (with aberrant c-MET) colorectal cancer, Project ID: 602901, Funding: EU FP7

Membership / activities in international organisations

EuroPDX consortium

  • Sharing patient derived tumour xenografts for collaborative research projects and multicentre preclinical trials
  • Function in the project: Leader of Bio-info-statistics group

Selected publications

  • Hidalgo M, Amant F, Biankin AV, Budinská E, Byrne AT, Caldas C, Clarke RB, de Jong S, Jonkers J, Mælandsmo GM, Roman-Roman S, Seoane J, Trusolino L, Villanueva A. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov 2014, 4(9): 998–1013. doi: 10.1158/2159-8290.CD-14-0001.
  • Belmont PJ, Budinska E, Jiang P, Sinnamon MJ, Coffee E, Roper J, Xie T, Rejto PA, Derkits S, Sansom OJ, Delorenzi M, Tejpar S, Hung KE, Martin ES. Cross-species analysis of genetically engineered mouse models of MAPK-driven colorectal cancer identifies hallmarks of the human disease. Dis Model Mech 2014, 7(6): 613–623. doi: 10.1242/dmm.013904.
  • Budinska E, Popovici V, Tejpar S, D'Ario G, Lapique N, Sikora KO, Di Narzo AF, Yan P, Hodgson JG, Weinrich S, Bosman F, Roth A, Delorenzi M. Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer. J Pathol 2013, 231(1): 63–76. doi: 10.1002/path.4212.
  • Popovici V, Budinska E, Bosman FT, Tejpar S, Roth AD, Delorenzi M. Context-dependent interpretation of the prognostic value of BRAF and KRAS mutations in colorectal cancer. BMC Cancer 2013, 13: 439. doi: 10.1186/1471-2407-13-439.
  • Budinska E, Gelnarova E, Schimek MG. MSMAD: a computationally efficient method for the analysis of noisy array CGH data. Bioinformatics 2009, 25(6): 703–713. doi: 10.1093/bioinformatics/btp022.

Our methods and software

  • method for breakpoint detection in arrayCGH data MSMAD
  • R-package for meta-analysis of microarray experiments MAMA
  • R-package for topology-based pathway analysis of microarray and RNA-seq ToPASeq
  • R-package TopKLists for the analysis of multiple ranked input lists (full or partial) representing the same set of N objects.