פרופ' דוד [דודו] בורשטיין

We strive to better understand key interaction mechanisms within microbial communities and to promote their application in biotechnology and medicine. By combining state-of-the-art machine learning, molecular biology, and metagenomic techniques, we decipher functions in the nexus of microbial interactions, such as CRISPR-Cas, anti-CRISPRs, antibiotic resistance genes, and bacterial secretion systems. More information about our exploration of large language models (LLMs) to “read” microbial genomes, how plasmids protect themselves against systems such as CRISPR-Cas, and more, is found on the lab website and the publications below.

(full publication list in Google Scholar)

Rannon E, Burstein D*. Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics. 
arXiv preprint arXiv:2506.02212 (2025) DOI: https://doi.org/10.48550/arXiv.2506.02212

Rannon E, Shaashua S, Burstein D^✉. DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.
Microbiome 13, 67 (2025);  https://doi.org/10.1186/s40168-025-02055-4

Samuel B, Mittelman K, Shirely C, Ben-Haim M, Burstein D^✉. Diverse anti-defense systems are encoded in the leading region of plasmids.
Nature 635, 186–192 (2024); DOI: https://doi.org/10.1038/s41586-024-07994-w

Miller D, Arias O, Burstein D^✉. GeNLP: An interactive web application for microbial gene exploration and prediction.
Bioinformatics 40, btae034 (2024); DOI: https://doi.org/10.1093/bioinformatics/btae034

Harari S, Miller D, Fleishon S, Burstein D, Stern A^✉. Using big sequencing data to identify chronic SARS-Coronavirus-2 infections.
Nature Communications 15, 648 (2024); DOI: https://doi.org/10.1038/s41467-024-44803-4

Alon DM, Mittelman K, Stibbe E, Countryman S, Stodieck L, Doraisingam S, Martin DML, Hamo H, Pines G*, Burstein D^✉. CRISPR-based genetic diagnostics in microgravity.
Biosensors & Bioelectronics 237, 115479 (2023); DOI: https://doi.org/10.1016/j.bios.2023.115479

Miller D, Stern A, Burstein D^✉. Deciphering microbial gene function using natural language processing.
Nature Communications 13, 5731 (2022); DOI: https://doi.org/10.1038/s41467-022-33397-4

Méheust R, Burstein D, Castelle CJ, Banfield JF. The distinction of CPR bacteria from other bacteria based on protein family content.
Nature Communications 10, 4173 (2019); DOI: https://doi.org/10.1038/s41467-019-12171-z

Harrington LB*,  Burstein D*, Chen JS, Paez-Espino D, Ma E, Witte IP, Cofsky JC, Kyrpides NC, Banfield JF, Doudna JA. Programmed DNA destruction by miniature CRISPR-Cas14 enzymes
Science 362 (6416), 839-842; DOI: https://doi.org/10.1126/science.aav4294

Burstein D*, Harrington LB*, Strutt SC*, Probst AJ, Anantharaman K, Thomas BC, Doudna JA, Banfield JF. New CRISPR–Cas systems from uncultivated microbes.
Nature 542, 237–241 (2017); DOI: https://doi.org/10.1038/nature21059

Paul BG, Burstein D, Castelle CJ, Handa S, Arambula D, Czornyj E, Thomas BC, Ghosh P, Miller JF, Banfield JF, Valentine DL. Retroelement-guided protein diversification abounds in vast lineages of Bacteria and Archaea.
Nature Microbiology 2, 17045 (2017); DOI: https://doi.org/10.1038/nmicrobiol.2017.45

East-Seletsky A, O’Connell MR, Burstein D, Knott GJ, Doudna JA. RNA targeting by functionally orthogonal type VI-A CRISPR-Cas enzymes.
Molecular Cell 66, 373–383 (2017); DOI: https://doi.org/10.1016/j.molcel.2017.04.008

East-Seletsky A, O’Connell MR, Knight SC, Burstein D, Cate JHD, Tjian R, Doudna JA. Two distinct RNase activities of CRISPR-C2c2 enable guide-RNA processing and RNA detection.
Nature 538, 270–273 (2016); DOI: https://doi.org/10.1038/nature19802

Burstein D, Sun CL, Brown CT, Sharon I, Anantharaman K, Probst AJ, Thomas BC, Banfield JF. Major bacterial lineages are essentially devoid of CRISPR-Cas viral defence systems.
Nature Communications 7, 10613 (2016); DOI: https://doi.org/10.1038/ncomms10613

Burstein D, Amaro F, Zusman T, Lifshitz Z, Cohen O, Gilbert JA, Pupko T, Shuman HA, Segal G. Genomic analysis of 38 Legionella species identifies large and diverse effector repertoires.
Nature Genetics 48, 167–175 (2016); DOI: https://doi.org/10.1038/ng.3481

Teper D*, Burstein D*, Salomon D, Gershovitz M, Pupko T, Sessa G. Identification of novel Xanthomonas euvesicatoria type III effector proteins by a machine-learning approach.
Molecular Plant Pathology 17, 398–411 (2016); DOI: https://doi.org/10.1111/mpp.12289

Burstein D*, Satanower S*, Simovitch M*, Belnik Y, Zehavi M, Yerushalmi G, Ben-Aroya S, Pupko T, Banin E. Novel type III effectors in Pseudomonas aeruginosa.
mBio 6, e00161-15 (2015); DOI: https://doi.org/10.1128/mBio.00161-15

Lifshitz Z*, Burstein D*, Peeri M, Zusman T, Schwartz K, Shuman HA, Pupko T,
Segal G. Computational modeling and experimental validation of the Legionella and Coxiella virulence-related type-IVB secretion signal.
PNAS 110, E707–E715 (2013); DOI: https://doi.org/10.1073/pnas.1215278110

Burstein D*, Gould SB*, Zimorski V, Kloesges T, Kiosse F, Major P, Martin WF,
Pupko T, Dagan T. A machine learning approach to identify hydrogenosomal proteins in Trichomonas vaginalis.
Eukaryotic Cell 11, 217–228 (2012); DOI: https://doi.org/10.1128/EC.05225-11

Gelfman S, Burstein D, Penn O, Savchenko A, Amit M, Schwartz S, Pupko T, Ast G. Changes in exon–intron structure during vertebrate evolution affect the splicing pattern of exons.
Genome Research 22, 35–50 (2012); DOI: https://doi.org/10.1101/gr.119834.110

Burstein D, Zusman T, Degtyar E, Viner R, Segal G, Pupko T. Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.
PLoS Pathogens 5, e1000508 (2009); DOI: https://doi.org/10.1371/journal.ppat.1000508

Schwartz S, Silva J, Burstein D, Pupko T, Eyras E, Ast G. Large-scale comparative analysis of splicing signals and their corresponding splicing factors in eukaryotes.
Genome Research 18, 88–103 (2008); DOI: https://doi.org/10.1089/cmb.2006.13.336

Ulitsky I, Burstein D, Tuller T, Chor B. The average common substring approach to phylogenomic reconstruction.
Journal of Computational Biology 13, 336–350 (2006); DOI: https://doi.org/10.1089/cmb.2006.13.336

Burstein D, Ulitsky I, Tuller T, Chor B. Information theoretic approaches to whole genome phylogenomics.
Proceedings of RECOMB 2005. Lecture Notes in Computer Science Vol. 3500, pp. 283-295; DOI: https://doi.org/10.1007/11415770_22