Bioinformatics research group

Group leader: Eszter Ari

Students

PhD

Active students: András Asbóth (50%), Balázs Bohár (50%), Ágoston Hunya (50%), Tamás Kadlecsik (50%)
Graduated students: Amanda Demeter (2020, 50%), Dániel Gerber (2024, 50%)

Scientific Student Association

Active students: Dániel Hegedűs, Márk Hunor Juhász, Ákos Kimpián
Former students: Balázs Bohár, Orsolya Liska, Márton Ölbei

Master

Active students: Csenge Gazsó
Graduated students: András Asbóth, Balázs Bohár, Misshelle Bustamante, Ágoston Hunya, Orsolya Liska, Márton Ölbei

Bachelor

Active students: Norbert Kada, Ákos Kimpián
Graduated students: Eszter Bozsó, Kata Ferenc, Leila Gul, Ágoston Hunya, Krisztina Martonosi, Klaudia Saru, Rebeka Sóskuthy, Mónika Szabó, Laura Tamási, Réka Vajda, Bálint Vásárhelyi

Main research areas

Investigating the mobility of antimicrobial resistance and virulence genes

Horizontal gene transfer between bacterial lineages is widespread and plays a key role in the evolution of antimicrobial resistance and virulence. 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 (HUN-REN Biological Research Centre, Institute of Biochemistry, 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 in Nature Microbiology.

Creating and maintaining the TFLink database, 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. We are working on to make the database organ, tissue, and cell specific using data obtained by various high-throughput methods. We published our results in the Database journal.

Developing an R package, called mulea for functional enrichment analyses

We developped the mulea (multi enrichment analysis) and the muleaData R packages, an extensive analytical tool using diverse databases (e.g. Gene Ontology, pathways, miRNAs, transcription factors 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.

Investigating the 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. We published our results in the Virus Evolition journal.