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 (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.

I supervised the creation and maintainance 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 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.

We are developing the mulea (multi enrichment analysis) R package, 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.

DNA or protein sequence data used for reconstructing phylogenetic trees can contain various errors due to contamination, low-quality genome assembly, or misclassification of taxa. While these errors are generally identified at the sequence level, undetected errors often result in leaves that appear as unusually long branches on the inferred phylogenetic tree. Therefore, pruning is key to detecting and removing erroneous tips from the phylogenetic trees. We implemented treepruner in R and Python with three novel pruning algorithms along with flexible combined workflows, allowing users to remove extremely long branches under customizable retention thresholds. By optimising tree radius and improving root‑to‑tip regression, treepruner enhances phylogenetic accuracy while preserving most of the input taxa.