@misc{MTMT:34718081,
	title = {mulea - an R package for enrichment analysis using multiple ontologies and empirical FDR correction},
	url = {https://m2.mtmt.hu/api/publication/34718081},
	author = {Turek, Cezary and Olbei, Marton and Stirling, Tamás and Fekete, Gergely and Tasnádi, Ervin Áron and Gul, Leila and Bohár, Balázs and Papp, Balázs and Jurkowski, Wiktor and Ari, Eszter},
	unique-id = {34718081},
	abstract = {Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs an innovative empirical false discovery rate (eFDR) correction method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, expanding its applicability across diverse research areas. Availability and Implementation: Software for the tools demonstrated in this article is available as an R package on GitHub: https://github.com/ELTEbioinformatics/mulea.},
	year = {2024},
	orcid-numbers = {Stirling, Tamás/0000-0002-8964-6443; Ari, Eszter/0000-0001-7774-1067}
}

@article{MTMT:2924952,
	title = {Collateral sensitivity of antibiotic-resistant microbes},
	url = {https://m2.mtmt.hu/api/publication/2924952},
	author = {Pál, Csaba and Papp, Balázs and Lázár, Viktória},
	doi = {10.1016/j.tim.2015.02.009},
	journal-iso = {TRENDS MICROBIOL},
	journal = {TRENDS IN MICROBIOLOGY},
	volume = {23},
	unique-id = {2924952},
	issn = {0966-842X},
	abstract = {Understanding how evolution of microbial resistance towards a given antibiotic influences susceptibility to other drugs is a challenge of profound importance. By combining laboratory evolution, genome sequencing, and functional analyses, recent works have charted the map of evolutionary trade-offs between antibiotics and have explored the underlying molecular mechanisms. Strikingly, mutations that caused multidrug resistance in bacteria simultaneously enhanced sensitivity to many other unrelated drugs (collateral sensitivity). Here, we explore how this emerging research sheds new light on resistance mechanisms and the way it could be exploited for the development of alternative antimicrobial strategies.},
	year = {2015},
	eissn = {1878-4380},
	pages = {401-407}
}