Bioinformatic research group

Group leader: Eszter Ari

Students

PhD

Active PhD students: Tamás Kadlecsik (50%), Dániel Gerber (50%), Balázs Bohár (50%), András Asbóth (50%)

Graduated PhD students: Amanda Demeter (50%)

Master

Active master students: Ágoston Hunya, Gergely Tarján (ic)
Graduated master students: András Asbóth, Balázs Bohár, Misshelle Bustamante, Luca Csabai (ic), Benedek Dankó (ic), Amanda Demeter (ic), Anna Küllői (ic), Orsolya Liska, Márton Ölbei, Bence Siklósi (ic)

Bachelor

Active bachelor students: Rebeka Sóskuthy, Laura Tamási
Graduated bachelor students: Ágoston Hunya, Kata Ferenc, Leila Gul, Klaudia Saru, Mónika Szabó, Réka Vajda, Bálint Vásárhelyi

Main research areas

Mobility of antimicrobial resistance genes

Horizontal gene transfer between bacterial lineages is widespread and plays a key role in the evolution of antimicrobial resistance. Despite its clinical importance, however, we have only a limited understanding of (i) the general trends and impacts of gene exchange between virulent pathogens and multidrug resistant commensal bacteria. We – together with the groups of Balázs Papp and Bálint Kintses (Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network, Szeged, Hungary) – address these issues by analyzing the gene exchange networks of human microbiota, multidrug resistant and pathogenic bacteria alike. We have published our previous work at Nature Microbiology.

TFLink: an integrated gateway to access transcription factor - target gene interactions for multiple species

We created and maintain the TFLink database that uniquely provides comprehensive and highly accurate information on transcription factor - target gene interactions, nucleotide sequences and genomic locations of transcription factor binding sites for human and six model organisms. We integrated the results of small- and large-scale approaches from ten different databases.

Genomic epidemiology of the Hungarian Sars-CoV-2 genomes

We compare the virus genomes of Hungarian samples to genomes from other countries and infer a time-scaled phylogenetic tree. Based on this tree we can ascertain the relatives – and potential origins – of the Hungarian clusters, the time of its emergence, and the extensiveness of each clade.

MulEA: an R package to for functional enrichment analyses

We are developing the MulEA (Multi Enrichment Analysis) R package that is an extensive analytical tool using diverse databases (e.g. Gene ontology, pathways, miRNAs or protein domains) and provides statistical models and p-value correction procedures that can extend our understanding of the results of various high-throughput analyses. MulEA uniquely provides a permutation based, empirical false discovery rate correction of the p-values making the gene set overrepresentation analyses more reliable.