TY - JOUR AU - Prosz, Aurel AU - Sahgal, Pranshu AU - Huffman, Brandon M AU - Sztupinszki, Zsofia AU - Morris, Clare X AU - Chen, David AU - Börcsök, Judit AU - Diossy, Miklos AU - Tisza, Viktoria AU - Spisák, Sándor AU - Likasitwatanakul, Pornlada AU - Rusz, Orsolya AU - Csabai, István AU - Cecchini, Michael AU - Baca, Yasmine AU - Elliot, Andrew AU - Enzinger, Peter AU - Singh, Harshabad AU - Ubellaker, Jessalyn AU - Lazaro, Jean-Bernard AU - Cleary, James M AU - Szállási, Zoltán AU - Sethi, Nilay S TI - Mutational signature-based identification of DNA repair deficient gastroesophageal adenocarcinomas for therapeutic targeting. JF - NPJ PRECISION ONCOLOGY J2 - NPJ PRECIS ONCOL VL - 8 PY - 2024 IS - 1 PG - 14 SN - 2397-768X DO - 10.1038/s41698-024-00561-6 UR - https://m2.mtmt.hu/api/publication/34779369 ID - 34779369 AB - Homologous recombination (HR) and nucleotide excision repair (NER) are the two most frequently disabled DNA repair pathways in cancer. HR-deficient breast, ovarian, pancreatic and prostate cancers respond well to platinum chemotherapy and PARP inhibitors. However, the frequency of HR deficiency in gastric and esophageal adenocarcinoma (GEA) still lacks diagnostic and functional validation. Using whole exome and genome sequencing data, we found that a significant subset of GEA, but very few colorectal adenocarcinomas, show evidence of HR deficiency by mutational signature analysis (HRD score). High HRD gastric cancer cell lines demonstrated functional HR deficiency by RAD51 foci assay and increased sensitivity to platinum chemotherapy and PARP inhibitors. Of clinical relevance, analysis of three different GEA patient cohorts demonstrated that platinum treated HR deficient cancers had better outcomes. A gastric cancer cell line with strong sensitivity to cisplatin showed HR proficiency but exhibited NER deficiency by two photoproduct repair assays. Single-cell RNA-sequencing revealed that, in addition to inducing apoptosis, cisplatin treatment triggered ferroptosis in a NER-deficient gastric cancer, validated by intracellular GSH assay. Overall, our study provides preclinical evidence that a subset of GEAs harbor genomic features of HR and NER deficiency and may therefore benefit from platinum chemotherapy and PARP inhibitors. LA - English DB - MTMT ER - TY - JOUR AU - Rahman, Nadim AU - O'Cathail, Colman AU - Zyoud, Ahmad AU - Sokolov, Alexey AU - Oude Munnink, Bas AU - Grüning, Björn AU - Cummins, Carla AU - Amid, Clara AU - Nieuwenhuijse, David F AU - Visontai, David AU - Yuan, David Yu AU - Gupta, Dipayan AU - Prasad, Divyae K AU - Gulyás, Gábor Máté AU - Rinck, Gabriele AU - McKinnon, Jasmine AU - Rajan, Jeena AU - Knaggs, Jeff AU - Skiby, Jeffrey Edward AU - Stéger, József AU - Szarvas, Judit AU - Gueye, Khadim AU - Papp, Krisztián AU - Hoek, Maarten AU - Kumar, Manish AU - Ventouratou, Marianna A AU - Bouquieaux, Marie-Catherine AU - Koliba, Martin AU - Mansurova, Milena AU - Haseeb, Muhammad AU - Worp, Nathalie AU - Harrison, Peter W AU - Leinonen, Rasko AU - Thorne, Ross AU - Selvakumar, Sandeep AU - Hunt, Sarah AU - Venkataraman, Sundar AU - Jayathilaka, Suran AU - Cezard, Timothée AU - Maier, Wolfgang AU - Waheed, Zahra AU - Iqbal, Zamin AU - Aarestrup, Frank Møller AU - Csabai, István AU - Koopmans, Marion AU - Burdett, Tony AU - Cochrane, Guy TI - Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses. JF - MICROBIAL GENOMICS J2 - MICROB GENOM VL - 10 PY - 2024 IS - 2 SN - 2057-5858 DO - 10.1099/mgen.0.001188 UR - https://m2.mtmt.hu/api/publication/34599260 ID - 34599260 AB - The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learnt. This paper describes a component of the Platform, the SARS-CoV-2 Data Hubs, which enable the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness. LA - English DB - MTMT ER - TY - JOUR AU - Prosz, Aurel AU - Pipek, Orsolya Anna AU - Börcsök, Judit AU - Palla, Gergely AU - Szallasi, Zoltan AU - Spisák, Sándor AU - Csabai, István TI - Biologically informed deep learning for explainable epigenetic clocks JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 14 PY - 2024 IS - 1 PG - 10 SN - 2045-2322 DO - 10.1038/s41598-023-50495-5 UR - https://m2.mtmt.hu/api/publication/34527502 ID - 34527502 N1 - Danish Cancer Institute, Copenhagen, Denmark Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark Department of Biological Physics, ELTE Eötvös Loránd University, Budapest, Hungary Health Services Management Training Centre, Semmelweis University, Budapest, Hungary Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary Export Date: 13 February 2024; Cited By: 0; Correspondence Address: S. Spisak; Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary; email: spisak.sandor@ttk.hu AB - Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model. LA - English DB - MTMT ER - TY - JOUR AU - Pipek, Orsolya Anna AU - Medgyes-Horváth, Anna AU - Stéger, József AU - Papp, Krisztián AU - Visontai, David AU - Koopmans, M. AU - Nieuwenhuijse, D. AU - Oude, Munnink B.B. AU - Cochrane, G. AU - Rahman, N. AU - Cummins, C. AU - Yuan, D.Y. AU - Selvakumar, S. AU - Mansurova, M. AU - O’Cathail, C. AU - Sokolov, A. AU - Thorne, R. AU - Worp, N. AU - Amid, C. AU - Csabai, István TI - Systematic detection of co-infection and intra-host recombination in more than 2 million global SARS-CoV-2 samples JF - NATURE COMMUNICATIONS J2 - NAT COMMUN VL - 15 PY - 2024 IS - 1 SN - 2041-1723 DO - 10.1038/s41467-023-43391-z UR - https://m2.mtmt.hu/api/publication/34525724 ID - 34525724 N1 - Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Pázmány P. s. 1A, Budapest, 1117, Hungary Department of Viroscience, Erasmus University Medical Center, Rotterdam, Netherlands European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, Hinxton, CB10 1SD, United Kingdom Export Date: 23 January 2024; Cited By: 0; Correspondence Address: A. Medgyes-Horváth; Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Pázmány P. s. 1A, 1117, Hungary; email: horvath.anna@ttk.elte.hu LA - English DB - MTMT ER - TY - JOUR AU - Olar, Alex AU - Tyler, Teadora AU - Hoppa, Paulina AU - Frank, Erzsébet AU - Csabai, István AU - Adorján, István AU - Pollner, Péter TI - Annotated dataset for training deep learning models to detect astrocytes in human brain tissue JF - SCIENTIFIC DATA J2 - SCI DATA VL - 11 PY - 2024 IS - 1 PG - 9 SN - 2052-4463 DO - 10.1038/s41597-024-02908-x UR - https://m2.mtmt.hu/api/publication/34518530 ID - 34518530 N1 - Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary Eötvös Loránd University, Doctoral School of Informatics, Budapest, Hungary Semmelweis University, Department of Anatomy, Histology and Embryology, Budapest, Hungary Semmelweis University, Data-Driven Health Division of National Laboratory for Health Security, Health Services Management Training Centre, Budapest, Hungary Export Date: 6 February 2024 Correspondence Address: Adorjan, I.; Semmelweis University, Hungary; email: adorjan.istvan@semmelweis.hu Correspondence Address: Pollner, P.; Semmelweis University, Hungary; email: peter.pollner@emk.semmelweis.hu AB - Astrocytes, a type of glial cell, significantly influence neuronal function, with variations in morphology and density linked to neurological disorders. Traditional methods for their accurate detection and density measurement are laborious and unsuited for large-scale operations. We introduce a dataset from human brain tissues stained with aldehyde dehydrogenase 1 family member L1 (ALDH1L1) and glial fibrillary acidic protein (GFAP). The digital whole slide images of these tissues were partitioned into 8730 patches of 500 × 500 pixels, comprising 2323 ALDH1L1 and 4714 GFAP patches at a pixel size of 0.5019/pixel, furthermore 1382 ADHD1L1 and 311 GFAP patches at 0.3557/pixel. Sourced from 16 slides and 8 patients our dataset promotes the development of tools for glial cell detection and quantification, offering insights into their density distribution in various brain areas, thereby broadening neuropathological study horizons. These samples hold value for automating detection methods, including deep learning. Derived from human samples, our dataset provides a platform for exploring astrocyte functionality, potentially guiding new diagnostic and treatment strategies for neurological disorders. LA - English DB - MTMT ER - TY - JOUR AU - Barták, Barbara Kinga AU - Nagy, Zoltán AU - Farkas, Eszter Alexandra AU - Bányai, F AU - Szakállas, Nikolett AU - Valcz, Gábor AU - Pipek, Orsolya Anna AU - Csabai, István AU - Takács, István AU - Molnár, Béla TI - FOLSAV-PÓTLÁS HATÁSÁNAK VIZSGÁLATA HYPERHOMOCYSTEINAEMIÁBAN SZENVEDŐ IBD-S BETEGEKBEN JF - MAGYAR BELORVOSI ARCHIVUM J2 - MBA VL - 76 PY - 2023 IS - 5-6 SP - 300 EP - 300 PG - 1 SN - 0133-5464 UR - https://m2.mtmt.hu/api/publication/34558223 ID - 34558223 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Prosz, Aurel AU - Duan, Haohui AU - Tisza, Viktoria AU - Sahgal, Pranshu AU - Topka, Sabine AU - Klus, Gregory T. AU - Börcsök, Judit AU - Sztupinszki, Zsofia AU - Hanlon, Timothy AU - Diossy, Miklos AU - Vízkeleti, Laura AU - Stormoen, Dag Rune AU - Csabai, István AU - Pappot, Helle AU - Vijai, Joseph AU - Offit, Kenneth AU - Ried, Thomas AU - Sethi, Nilay AU - Mouw, Kent W. AU - Spisák, Sándor AU - Pathania, Shailja AU - Szállási, Zoltán TI - Nucleotide excision repair deficiency is a targetable therapeutic vulnerability in clear cell renal cell carcinoma JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 13 PY - 2023 IS - 1 PG - 10 SN - 2045-2322 DO - 10.1038/s41598-023-47946-4 UR - https://m2.mtmt.hu/api/publication/34398764 ID - 34398764 N1 - Danish Cancer Institute, Copenhagen, Denmark Center for Personalized Cancer Therapy, University of Massachusetts, Boston, MA, United States Department of Biology, University of Massachusetts, Boston, MA, United States Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, United States Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States Broad Institute of Massachusetts Institute of Technology (MIT), Harvard University, Cambridge, MA, United States Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, United States Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States Niehaus Center for Inherited Cancer Genomics, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, United States Genetics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, United States Biotech Research & Innovation Centre, University of Copenhagen, Copenhagen, Denmark Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA, United States Department of Bioinformatics, Semmelweis University, Budapest, Hungary Department of Oncology, Rigshospitalet, University Hospital of Copenhagen, Copenhagen, Denmark Department of Physics of Complex Systems, Eötvös Loránd University, Budapest, Hungary Department of Medicine, Weill Cornell Medical College, New York, NY, United States Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering, New York, NY, United States Department of Radiation Oncology, Brigham & Women’s Hospital, Boston, MA, United States Harvard Medical School, Boston, MA, United States Export Date: 30 January 2024 Correspondence Address: Szallasi, Z.; Danish Cancer InstituteDenmark; email: zoltan.szallasi@childrens.harvard.edu Correspondence Address: Pathania, S.; Center for Personalized Cancer Therapy, United States; email: Shailja.Pathania@umb.edu Correspondence Address: Spisak, S.; Institute of Enzymology, Hungary; email: spisak.sandor@ttk.hu Chemicals/CAS: irofulven, 158440-71-2; ERCC2 protein, human; irofulven; Sesquiterpenes; Xeroderma Pigmentosum Group D Protein Funding details: National Institutes of Health, NIH, CA221745, P30 CA008748 Funding details: U.S. Department of Defense, DOD Funding details: National Cancer Institute, NCI, R01CA272657 Funding details: Breast Cancer Research Foundation, BCRF, BCRF-21–159 Funding details: Pfizer Funding details: Merck Funding details: Kræftens Bekæmpelse, DCS, R281-A16566, R340-A19380 Funding details: Sundhed og Sygdom, Det Frie Forskningsråd, FSS, DFF, 7016-00345B Funding details: Velux Fonden, 00018310, R15 CA 235436–01 Funding details: DOD Prostate Cancer Research Program, PCRP, W81XWH-18–2-0056 Funding details: Novo Nordisk Fonden, NNF, NNF15OC0016584 Funding details: Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFI, FK142835 Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA, KTIA_NAP_13-2014–0021 Funding text 1: K.W.M—Consulting or Advisory Role: EMD Serono, Pfizer. Research Funding: Pfizer. Patents: Institutional patents filed on ERCC2 mutations and chemotherapy response (KW.M, Z.S., J.B, Zs. Sz. and M.D.). JV, ST and KO are inventors on a patent application for use of Illudin class of alkylating agents in patients harboring mutations in the ERCC3 gene (PCT/US2018/022588). D.R.S: Research Funding: Pfizer, EMD Serono.H.P.: Research funding from Pfizer and Merck Z.S: Research funding from Lantern Pharma Inc.. Other authors do not have competing interest. Funding text 2: This work was supported by the Research and Technology Innovation Fund (KTIA_NAP_13-2014–0021 to Z.S.), Breast Cancer Research Foundation (BCRF-21–159 to Z.S.), the Novo Nordisk Foundation Interdisciplinary Synergy Programme Grant (NNF15OC0016584 to Z.S.), Kræftens Bekæmpelse (R281-A16566 to Z.S. and R340-A19380 to J.B.), Department of Defense through the Prostate Cancer Research Program (W81XWH-18–2-0056 to Z.S.), Det Frie Forskningsråd Sundhed og Sygdom (7016-00345B to Z.S.), the National Cancer Institute (R01CA272657 to K.W.M), and the Velux Foundation (00018310 to Zs.S. and J.B.). This work was also supported by a grant from The National Cancer Institute, R15 CA 235436–01 (S.P.). We acknowledge the support of the NIH core grant to MSKCC (P30 CA008748), the MSKCC bladder SPORE (CA221745), the breast cancer research foundation (BCRF) grant and the Kate and Robert Niehaus Foundation providing funding to the Robert and Kate Niehaus Center for Inherited Cancer Genomics at Memorial Sloan Kettering Cancer (KO). S.S. received funding from National Research Development and Innovation Office Hungary, under grant no. FK142835. AB - Due to a demonstrated lack of DNA repair deficiencies, clear cell renal cell carcinoma (ccRCC) has not benefitted from targeted synthetic lethality-based therapies. We investigated whether nucleotide excision repair (NER) deficiency is present in an identifiable subset of ccRCC cases that would render those tumors sensitive to therapy targeting this specific DNA repair pathway aberration. We used functional assays that detect UV-induced 6–4 pyrimidine-pyrimidone photoproducts to quantify NER deficiency in ccRCC cell lines. We also measured sensitivity to irofulven, an experimental cancer therapeutic agent that specifically targets cells with inactivated transcription-coupled nucleotide excision repair (TC-NER). In order to detect NER deficiency in clinical biopsies, we assessed whole exome sequencing data for the presence of an NER deficiency associated mutational signature previously identified in ERCC2 mutant bladder cancer. Functional assays showed NER deficiency in ccRCC cells. Some cell lines showed irofulven sensitivity at a concentration that is well tolerated by patients. Prostaglandin reductase 1 (PTGR1), which activates irofulven, was also associated with this sensitivity. Next generation sequencing data of the cell lines showed NER deficiency-associated mutational signatures. A significant subset of ccRCC patients had the same signature and high PTGR1 expression. ccRCC cell line-based analysis showed that NER deficiency is likely present in this cancer type. Approximately 10% of ccRCC patients in the TCGA cohort showed mutational signatures consistent with ERCC2 inactivation associated NER deficiency and also substantial levels of PTGR1 expression. These patients may be responsive to irofulven, a previously abandoned anticancer agent that has minimal activity in NER-proficient cells. LA - English DB - MTMT ER - TY - JOUR AU - Spisak, S. AU - Tisza, V. AU - Nuzzo, P.V. AU - Seo, J.-H. AU - Pataki, Bálint Ármin AU - Ribli, Dezső AU - Sztupinszki, Z. AU - Bell, C. AU - Rohanizadegan, M. AU - Stillman, D.R. AU - Alaiwi, S.A. AU - Bartels, A.H. AU - Papp, M. AU - Shetty, A. AU - Abbasi, F. AU - Lin, X. AU - Lawrenson, K. AU - Gayther, S.A. AU - Pomerantz, M. AU - Baca, S. AU - Solymosi, Norbert AU - Csabai, István AU - Szallasi, Z. AU - Gusev, A. AU - Freedman, M.L. TI - Author Correction: A biallelic multiple nucleotide length polymorphism explains functional causality at 5p15.33 prostate cancer risk locus (Nature Communications, (2023), 14, 1, (5118), 10.1038/s41467-023-40616-z) JF - NATURE COMMUNICATIONS J2 - NAT COMMUN VL - 14 PY - 2023 IS - 1 SN - 2041-1723 DO - 10.1038/s41467-023-42515-9 UR - https://m2.mtmt.hu/api/publication/34226088 ID - 34226088 N1 - Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, 02215, MA, United States Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, 02215, MA, United States Computational Health Informatics Program (CHIP) Boston Children’s Hospital Harvard Medical School, Boston, 02215, MA, United States Institute of Enzymology, Research Centre for Natural Sciences, Budapest, 1117, Hungary Department of Internal Medicine, School of Medicine, University of Genoa, Lgo R. Benzi 10, Genoa, 16132, Italy Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Pázmány P. s. 1A, Budapest, 1117, Hungary Centre for Bioinformatics, University of Veterinary Medicine, Istvan str. 2, Budapest, 1078, Hungary Division of Genetics, Brigham & Women’s Hospital, Boston, MA, United States Women’s Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, 90048, CA, United States Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, 90048, CA, United States Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, 90048, CA, United States The Eli and Edythe L. Broad Institute, Cambridge, 02142, MA, United States Department of Bioinformatics, Forensic and Insurance Medicine Semmelweis University, Budapest, Hungary Danish Cancer Society Research Center, Strandboulevarden 49, Copenhagen, 2100, Denmark National Korányi Institute of Pulmonology, Budapest, 1112, Hungary Export Date: 30 October 2023; Cited By: 0; Correspondence Address: M.L. Freedman; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, 02215, United States; email: matthew_freedman@dfci.harvard.edu LA - English DB - MTMT ER - TY - JOUR AU - Spisák, Sándor AU - Tisza, Viktoria AU - Nuzzo, P.V. AU - Seo, J.-H. AU - Pataki, Bálint Ármin AU - Ribli, Dezső AU - Sztupinszki, Z. AU - Bell, C. AU - Rohanizadegan, M. AU - Stillman, D.R. AU - Alaiwi, S.A. AU - Bartels, A.B. AU - Papp, Márton AU - Shetty, A. AU - Abbasi, F. AU - Lin, X. AU - Lawrenson, K. AU - Gayther, S.A. AU - Pomerantz, M. AU - Baca, S. AU - Solymosi, Norbert AU - Csabai, István AU - Szallasi, Z. AU - Gusev, A. AU - Freedman, M.L. TI - A biallelic multiple nucleotide length polymorphism explains functional causality at 5p15.33 prostate cancer risk locus JF - NATURE COMMUNICATIONS J2 - NAT COMMUN VL - 14 PY - 2023 IS - 1 SN - 2041-1723 DO - 10.1038/s41467-023-40616-z UR - https://m2.mtmt.hu/api/publication/34117214 ID - 34117214 LA - English DB - MTMT ER - TY - JOUR AU - Makrai, László AU - Fodróczy, Bettina AU - Nagy, Sára Ágnes AU - Czeiszing, Péter AU - Csabai, István AU - Szita, Géza AU - Solymosi, Norbert TI - Annotated dataset for deep-learning-based bacterial colony detection. JF - SCIENTIFIC DATA J2 - SCI DATA VL - 10 PY - 2023 IS - 1 SN - 2052-4463 DO - 10.1038/s41597-023-02404-8 UR - https://m2.mtmt.hu/api/publication/34086719 ID - 34086719 N1 - Dataset; Journal Article AB - Quantifying bacteria per unit mass or volume is a common task in various fields of microbiology (e.g., infectiology and food hygiene). Most bacteria can be grown on culture media. The unicellular bacteria reproduce by dividing into two cells, which increases the number of bacteria in the population. Methodologically, this can be followed by culture procedures, which mostly involve determining the number of bacterial colonies on the solid culture media that are visible to the naked eye. However, it is a time-consuming and laborious professional activity. Addressing the automation of colony counting by convolutional neural networks in our work, we have cultured 24 bacteria species of veterinary importance with different concentrations on solid media. A total of 56,865 colonies were annotated manually by bounding boxes on the 369 digital images of bacterial cultures. The published dataset will help developments that use artificial intelligence to automate the counting of bacterial colonies. LA - English DB - MTMT ER -