Bioinformatics and Big Data


The Bioinformatics and Big Data group is an essential part of the Laboratory of Pharmacogenomics and Individualised Therapy. Undoubtedly, analysis of large scale ‘-omics’ data with a variety of bioinformatics tools, scripts and databases has led to the identification of candidate genes and variants, which have been subsequently associated with a variety of complex genetic disorders. Moreover, whole exome and whole genome sequencing analysis of large cohorts has not only led to the identification of risk variants for complex genetic disorders, but has also unraveled the genetic background of genetic disorders which may be characterized by similar (endo)phenotypes (i.e. bipolar disorder, schizophrenia, major depressive disorder). 

Our group performs bioinformatics and big data analysis by exploring the following research strategies:

a). Developing in silico tools and bioinformatics pipelines for the identification of pharmacogenomics biomarkers by assessing NGS data of Greek cohorts,

b). Developing end-to-end NGS analysis pipelines (WES, WGS and targeted sequencing data) to assess for genomics differences in individuals with psychiatric and other complex genetic disorders,

c). Exploiting human genome informatics tools and large-scale databases,

d). Developing machine learning tools on pharmacogenomics data 

e). Using bioinformatics tools to study epigenetic modifications and their potential impact on gene regulation.

f). Developing biomedical literature mining for pharmacogenomics data


Group Leader: Dr Maria Koromina


Selected Publications

Koromina M, Pandi MT, Patrinos GP. Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics. OMICS. 2019;23(11):539‐548. 

Koromina M, Koutsilieri S, Patrinos GP. Delineating significant genome-wide associations of variants with antipsychotic and antidepressant treatment response: implications for clinical pharmacogenomics. Hum Genomics. 2020;14(1):4. Published 2020 Jan 15. doi:10.1186/s40246-019-0254-y

Kounelis F, Kanterakis A, Kanavos A, et al. Documentation of clinically relevant genomic biomarker allele frequencies in the next-generation FINDbase worldwide database. Hum Mutat. 2020;41(6):1112‐1122. doi:10.1002/humu.24018

Pandi MT, Williams M.S., Schuldiner A., van der Spek P., Koromina M., Patrinos G.P.  Exome-wide variant analysis from the DiscovEHR cohort identifies novel candidate pharmacogenomics variants. (2020, Genes, accepted).

Mizzi C, Peters B, Mitropoulou C, et al. Personalized pharmacogenomics profiling using whole-genome sequencing. Pharmacogenomics. 2014;15(9):1223‐1234. doi:10.2217/pgs.14.102

Mizzi C, Dalabira E, Kumuthini J, et al. A European Spectrum of Pharmacogenomic Biomarkers: Implications for Clinical Pharmacogenomics [published correction appears in PLoS One. 2017 Feb 16;12 (2):e0172595]. PLoS One. 2016;11(9):e0162866. Published 2016 Sep 16. doi:10.1371/journal.pone.0162866

Lakiotaki K, Kartsaki E, Kanterakis A, Katsila T, Patrinos GP, Potamias G. ePGA: A Web-Based Information System for Translational Pharmacogenomics. PLoS One. 2016;11(9):e0162801. Published 2016 Sep 15. doi:10.1371/journal.pone.0162801