Prof. David[Dudu] Burstein

We aim to better understand key interaction mechanisms within microbial communities and to promote their application in biotechnology and medicine. Specifically, we are interested in discovering novel antibiotic resistance genes, CRISPR-Cas systems, and bacterial secretion systems. We study microorganisms in a variety of natural environments and combine cutting-edge metagenomic techniques with tailored machine-learning and molecular biology methods to predict and experimentally validate the functions of the studied systems.

(full publication list in Google Scholar)

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

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

Ulitsky I, Burstein D, Tuller T, Chor B. The average common substring approach to phylogenomic reconstruction.
Journal of Computational Biology 13, 336–350 (2006)

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