Prof. Itay Mayrose

ביולוגיה מול.ואקול.צמחים סגל אקדמי בכיר
Prof. Itay Mayrose
Phone: 03-6407212
Office: Britannia-Porter, 60

Research Interests

Our main research interests are concerned with the broad field of plant evolution and phylogenetics. We use comparative, bioinformatics approaches to gain insights into the fascinating evolutionary dynamics of plant genomes. We are also interested in developing computational tools that can be used to gain novel biological insights from various kinds of data. Current projects in the lab include:


Studying the consequences of whole genome duplications on patterns of diversification and the effect of polyploidy on rates of molecular sequence evolution.

Methods development

We are currently developing computational tools and apply them to genomics data to enhance our understanding regarding the relationship between the genotype and the phenotype. By applying these methods we aim to understand the factors that influence the rate of genome evolution.

Plant domestication

Using a combination of computational tools with large scale genomics data we hope to gain novel insights into the evolutionary consequences of plant domestication and the evolutionary selection forces that shaped their genomes. By identifying unique patterns of selection acting on certain genes, we aim to reveal common genomics characteristics of plant crops that are important for human consumption.

Recent Publications

The Tree of Sex Consortium - Bachtrog D, Mank JE, Peichel CL, Kirkpatrick M, Otto SP, Ashman T-L, Hahn MW, Kitano J, Mayrose I, Ming R, Perrin N, Ross L, Valenzuela N, Vamosi JC. 2014. Sex Determination: Why so many ways of doing it? PLoS Biology. 12(7): e1001899. [pdf], [abs] Science Made Easy coverage


Glick L, Mayrose I. 2014. ChromEvol: Assessing the Pattern of Chromosome Number Evolution and the Inference of Polyploidy along a Phylogeny. Mol Biol Evol. 31(7): 1914-1922 [pdf], [abs]


The Tree of Sex Consortium - Ashman T-L, Bachtrog D, Blackmon H, Goldberg EE, Hahn MW, Kirkpatrick M, Kitano J, Mank JE, Mayrose I, Ming R, Otto SP, Peichel CL, Pennell MW, Perrin N, Valenzuela N, Vamosi JC. 2014. Tree of Sex: A database of sexual systems. Scientific Data. 1:140015 [pdf], [abs]


Zhan SH, Glick L, Tsigenopoulos CS, Otto SP, Mayrose I. 2014. Comparative analysis reveals that polyploidy does not decelerate diversification in fish. J Evol Biol. 27(2):391-403. [pdf], [abs]


Escudero M, Martin-Bravo S, Mayrose I, Fernandez-Mazuecos M, Fiz-Palacios O, Hipp A, Pimentel M, Jimenez-Mejias P, Valcarcel V, Vargas P, luceño M. 2014. Karyotypic changes through dysploidy persist longer over evolutionary time than polyploid changes. Plos One. 9(1):e58266. [pdf], [abs]


Mayrose I, Stern A, Burdelova EO, Sabo Y, Laham-Karam N, Zamostiano R, Bacharach E, Pupko T. 2013. Synonymous site conservation in the HIV-1 genome. BMC Evolutionary Biology. 13:164. [pdf], [abs]


Celniker G, Nimrod G, Ashkenazy H, Glaser F, Martz E, Mayrose I, Pupko T, Ben-Tal N. 2013. ConSurf: Using Evolutionary Data to Raise Testable Hypotheses about Protein Function. Isr J Chem. 53(3-4):199–206. [pdf], [abs]


Mayrose I, Zhan SH, Rothfels CJ, Magnuson-Ford K, Barker MS, Rieseberg LH, Otto SP. 2011. Recently-formed polyploid plants diversify at lower rates. Science. 333(6047):1257. [pdf], [abs], Faculty of 1000, media release


Mayrose M, Kane N, Mayrose I, Rieseberg L. 2011 Increased vigor in invasive and domesticated sunflower correlates with impaired response to biotic and a-biotic stress. Mol Ecol. 20(22):4683-4694 [pdf] [abs]


Rubinstein ND, Doron-Faigenboim A, Mayrose I, Pupko T. 2011. Evolutionary models accounting for layers of selection in protein coding genes and their impact on the inference of positive selection. Mol Biol Evol. 28(12):3297-3308 [pdf] [abs]


Mayrose I, Otto SP. 2011. A Likelihood Method for Detecting Trait-Dependent Shifts in the Rate of Molecular Evolution. Mol Biol Evol. 28:781-791. [pdf] [abs] Selected for Faculty of 1000, Treethinkers


Mayrose I, Barker MS, Otto SP. 2010.Probabilistic Models of Chromosome Number Evolution and the Inference of Polyploidy. Systematic Biology. 59(2):132-144 [pdf] [abs]


Stern A, Mayrose I, Shaul S, Gophna U, Pupko T. 2010. On the evolution of thymidine synthesis: a tale of two enzymes and a virus. Systematic Biology. 59(2):212-225. [pdf] [abs]


Rubinstein ND, Mayrose I, Martz E, Pupko T. 2009. Epitopia: a web-server for predicting B-cell epitopes. BMC Bioinformatics. 10:287. [pdf] [abs]


Wood TE, Takebayashi N, Barker MS, Mayrose I, Greenspoon PE, Rieseberg LH. 2009. The frequency of polyploid speciation in vascular plants. Proceedings of the National Academy of Sciences USA. [pdf] [abs]. Selected forFaculty of 1000, Nature's Research Highlight, IU Press Release


Rubinstein ND, Mayrose I, Pupko T. 2009. A machine learning approach for predicting B-cell epitopes. Molecular Immunology. 46(5):840-847. [pdf] [abs]


Rubinstein ND, Mayrose I, Halperin D, Yekutieli D, Gershoni JM, Pupko T. 2008. Computational characterization of B-cell epitopes. Molecular Immunology. 45(12):3477-89. [pdf] [abs]


Mayrose I, Penn O, Erez E, Rubinstein ND, Shlomi T, Tarnovitski-Freund N, Bublil EM, Ruppin E, Sharan R, Gershoni JM, Martz E, Pupko T. 2007. Pepitope: Inferring epitopes based on affinity-selected peptides. Bioinformatics. 23(23):3244-3246. [pdf] [abs]


Mayrose I, Doron-Faigenboim A, Bacharach E, Pupko T. 2007. Towards realistic codon models: among site variability and dependency of synonymous and nonsynonymous rates. ECCB/ISMB 2007. Bioinformatics. 23:i319-i327. [pdf][abs]. Selected for Faculty of 1000.


Bublil EM, Tarnovitski N, Mayrose I, Penn O, Roitburd A, Pupko T, Gershoni JM. 2007. Stepwise prediction of conformational discontinuous B-cell epitopes using the Mapitope algorithm. Proteins. 10;68(1):294-304 [pdf] [abs]


Mayrose I, Shlomi T, Rubinstein ND, Gershoni JM, Ruppin E, Sharan R, Pupko T. 2007. A graph-based algorithm for epitope mapping using combinatorial phage-display libraries. Nucleic Acid Research. 35(1):69-78. [pdf] [abs]


Mayrose I, Friedman N, Pupko T. 2005. A Gamma mixture model better accounts for among site rate heterogeneity. ECCB 2005. Bioinformatics. 21:ii151-ii158. [pdf] [abs]


Landau M, Mayrose I, Pupko T. Ben-Tal N. 2005. ConSurf 2005: Presenting the evolutionary rate of amino acid sites on protein structures. Nucleic Acid Research. 33: W299-W302. [pdf] [abs]


Faigenboim DA, Stern A, Mayrose I, Bacharach E, Pupko T. 2005. Selecton: a server for detecting evolutionary forces at a single amino-acid site. Bioinformatics. 21(9): 2101-2103. [pdf] [abs]


Mayrose I, Mitchell A, Pupko T. 2005. Site-specific evolutionary rate inference: taking phylogenetic uncertainty into account. J Mol Evol. 60(3):345-353. [pdf] [abs]


Mayrose I, Graur D, Ben-Tal N, Pupko T. 2004. Comparison of site-specific rate-inference methods for protein sequences: Bayesian methods are superior. Mol Biol Evol. 21:1781-1791. [pdf] [abs]


Weiss S, Gottfried I, Mayrose I, Khare SL, Xiang M, Dawson SJ, Avraham KB. 2003. The DFNA15 deafness mutation affects POU4F3 protein stability, localization, and transcriptional activity. Mol Cell Biol. 23(22):7957-64. [pdf] [abs]


Pupko T, Bell RE, Mayrose I, Glaser F, Ben-Tal N. 2002. Rate4Site: an algorithmic tool for the identification of functional regions on proteins by surface mapping of Evolutionary Determinants within their Homologues. Bioinformatics. 18 Suppl: S71-S77. [pdf] [abs]


Chapters in Books


Pupko T and Mayrose I. 2009. Probabilistic methods and rate heterogeneity. In Lodhi H and Muggleton S. (editors). Element of Computational Systems Biology, Wiley book series on Bioinformatics: Computational Techniques and Engineering. John Wiley and Sons Inc.



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