People:

Amoutzias GD

Laboratory Director: Grigoris Amoutzias, Assistant Professor of Bioinformatics in Genomics.

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Current Lab Members:

Panayotis Vlastaridis, PhD student: Organization and analysis of biological high-throughput data from multiple sources. In collaboration with Prof. Oliver, Cambridge University, UK, Prof. Van de Peer, VIB/UGent, Belgium, Dr Stratikos, Demokritos Research Center, Greece. Panos’ expertise is on database and web-front development. Technologies used: Java, Spring Framework, Angular, Matlab, Neo4j Graph Databases, Solr, MySQL, Linux

Chrysoula Doudoumi, undergraduate student: Detection and analysis of aminoacid repeats with Bioinformatics methods, in Prokaryotes and Eukaryotes .

Marios Nikolaidis, undergraduate student: Evolution and recombination of Enteroviruses (in collaboration with Prof. Markoulatos). Technologies used: Linux, Perl.

Efi Douka, undergraduate student: Evolution and recombination of Hepatitis A viruses (in collaboration with Prof. Markoulatos). Technologies used: Linux, Perl.

Georgios Bachoumis, undergraduate student: Evolution and recombination of Noroviruses (in collaboration with Prof. Markoulatos). Technologies used: Linux, Perl.

Popi Mimouli, undergraduate student: Evolution and recombination of pico-RNA viruses (in collaboration with Prof. Markoulatos). Technologies used: Linux, Perl.

Georgia Pateraki, undergraduate student: Investigation of metabolic control at the allosteric level with Bioinformatics methods. Technologies used: Linux, Perl

Previous Lab Members:

Athanasios Papakyriakou, Post-doctoral Researcher (2015-ARISTEIA II): In collaboration with Dr. Stratikos, Demokritos Research Center, Greece.

Alexandra Diakogeorgiou, undergraduate student: Evolution and Bioinformatics analyses of sugar transporters. Technologies used: Linux, Perl

Vicky Fliatoura, undergraduate student: Bioinformatics analysis of post-translational modification data. Technologies used: Linux, Perl.

Undergraduate Theses completed:


Tsionos G. 2018. Prediction of phosphorylation sites in rat proteins with machine learning methods.

Tsimpidis M. 2014. Genomic and evolutionary analysis of human Enteroviruses with Bioinformatics methods.

Chaliotis A. 2012. Detection of microbial tRNA-synthetases with bioinformatics methods.


Postgraduate Theses completed:


Ntountoumi C. 2018. Bioinformatic analysis of Low Complexity Regions in prokaryotes.

Flatoura V. 2018. Prediction of methylation sites in eukaryotic proteins with machine learning algorithms.

Diakogeorgiou A. 2018. Bioinformatic and evolutionary analysis of the Major Facilitator Superfamily Sugar Transporters.

Spetsarias S. 2018. Development of a Bioinformatics protocol for genome analysis of microbes with toxicological and forensic interest, using Illumina and Pacific Biosciences technologies

Chaliotis A. 2017. The complex evolutionary history of aminoacyl-tRNA synthetases.

Tsimpidis M. 2016. T-RECS: Rapid and large-scale detection of recombination events among different evolutionary lineages of viral genomes

Kyriakidou P. 2016. Literature mining of phospho-proteomic data and bioinformatics analysis.

Tsouhlou P. 2015. Bioinformatics and evolutionary analysis of RNA-Sequencing data from Bivalvia.

Chalyvopoulou P. 2014. Bioinformatic analysis of microbial metagenomes from Next-Generation Sequencing data.

Sini C. 2013. Bioinformatics analysis of Human Exome data from Next-Generation Sequencing technologies.

Doxara A. 2012. Bioinformatics analysis of Mitochondrial gene networks and related diseases from Genomic and Systems Biology data.



In the Bioinformatics laboratory, undergraduate and postgraduate students learn to work primarily with Linux and Perl. Other programming languages may also be used, depending on the project and informatics expertise of the student, such as Java, Javascript, Python, Visual Basic, SQL, PHP, Graph Databases, Matlab. Student projects vary from analyzing large and diverse biological data (phylogenetics, phylogenomics, genomic recombination, RNA-Seq, High-throughput phosphoproteomic data) to developing computational tools (viral genotyping and recombination tools, protein motif detection, databases, prediction of phosphorylation sites with machine learning) or a combination of data analysis and software development.