Tal Pupko - Publications:

142. Glick, L., Castiglione, S., Loewenthal, G., Raia, P., Pupko, T., and Mayrose, I. 2024. Phylogenetic analysis of 590 species reveals distinctive evolutionary patterns of intron-exon gene structures across eukaryotic lineages. Mol. Biol. Evol. Accepted

141. Klirs, Y., Novosolov, M., Gissi, C., Garic, R., Pupko, T., Stach, T., and Huchon, D. 2024. Evolutionary insights from the mitochondrial genome of Oikopleura dioica: sequencing challenges, RNA editing, gene transfers to the nucleus, and tRNA loss. Genome Biol Evol. 16(9):evae181 [pdf] [abs]

140. Redelings, B.D., Holmes, I., Lunter, G., Pupko, T., and Anisimova, A. 2024. Indels: computational methods, evolutionary dynamics, and biological applications. Mol. Biol. Evol. 41(9):msae177 [pdf] [abs]

139. Azouri, D., Granit, O., Alburquerque, M., Mansour, Y., Pupko, T., and Mayrose, I. 2024. The tree reconstruction game: phylogenetic reconstruction using reinforcement learning. Mol. Biol. Evol. 41(6):msae105 [pdf] [abs]

138. Ecker, N., Huchon, D., Mansour, Y., Mayrose, I., and Pupko, T. 2024. A machine-learning based alternative to phylogenetic bootstrap. Bioinforamtics. 40(Supplement_1):i208-i217 [pdf] [abs]

137. Dotan, E., Jaschek, G., Pupko, T., and Belinkov, Y. 2024. Effect of tokenization on transformers for biological sequences. Bioinformatics. 40(4):btae196. [pdf] [abs]

136. Pena, M.M., Bhandari, R., Bowers, R.M., Weis. K., Newberry, E., Wagner. N., Pupko. T., Jones, J.B., Woyke, T., Vinatzer. B.A., Jacques. M.A., and Potnis, N. 2024. Genetic and functional diversity help explain pathogenic, weakly pathogenic, and commensal lifestyles in the genus Xanthomonas. Genome Biol Evol. 16(4):evae074. [pdf] [abs]

135. Milkewitz Sandberg, T.O., Yahalomi, D., Bracha, N., Haddas-Sasson, M., Pupko, T., Atkinson, S.D., Bartholomew, J.L., Zhang, J.Y., and Huchon, D. 2024. Evolution of myxozoan mitochondrial genomes: insights from myxobolids. BMC genomics 25(1):388. [pdf] [abs]

134. Wygoda, E., Loewenthal, G., Moshe, A., Alburquerque, M., Mayrose, I., and Pupko, T. 2024. Statistical framework to determine indel length distribution. Bioinformatics 40(2):btae043 [pdf] [abs]

133. Polonsky, K., Pupko, T., and Freund, N. 2023. Evaluation of the ability of AlphaFold to predict the three-dimensional structures of antibodies and epitopes. J Immunol. 211(10): 1578 1588. [pdf] [abs]

132. Geraffi, N., Gupta, P., Wagner, N., Barash, I., Pupko, T., and Sessa, G. 2023. Comparative sequence analysis of pPATH pathogenicity plasmids in Pantoea agglomerans gall-forming bacteria. Front. Plant. Sci. 14:1198160. [pdf] [abs]

131. Nagar, N., Tubiana, J., Loewenthal, G., Wolfson, H.J., Ben-Tal, N., and Pupko, T. 2023. EvoRator2: predicting site-specific amino acid substitutions based on protein structural information using deep learning Journal of Molecular Biology: 435(14):168155. [pdf] [abs]

130. Wagner, N., Ben-Meir, D., Teper, D., and Pupko, T. 2023. Complete genome sequence of an Israeli isolate of Xanthomonas hortorum pv. pelargonii strain 305 and novel type III effectors identified in Xanthomonas. Front. Plant Sci. 14:1155341. [pdf] [abs]

129. Dotan, E., Alburquerque, M., Wygoda, E., Huchon, D., and Pupko, T. 2023. GenomeFLTR: filtering reads made easy. Nucleic Acids Research: 51(Web Server issue):W232-W236. [pdf] [abs]

128. Dotan, E., Belinkov, Y., Avram, O., Wygoda, E., Ecker, N., Alburquerque, M., Keren, O., Loewenthal, G., and Pupko, T. 2023. Multiple sequence alignment as a sequence-to-sequence learning problem. The Eleventh International Conference on Learning Representations (ICLR 2023). [pdf]

127. Yariv, B., Yariv, E., Kessel, A., Masrati, G., Ben Chorin, A., Martz, E., Mayrose, I., Pupko, T., and Ben-Tal, N. 2023. Using evolutionary data to make sense of macromolecules with a face-lifted ConSurf. Protein Science. 32(3):e4582 [pdf] [abs]

126. Loewenthal, G., Wygoda, E., Nagar, N., Glick, L., Mayrose, I., and Pupko, T. 2022. The evolutionary dynamics that retain long neutral genomic sequences in face of indel deletion bias: a model and its application to human introns. Open Biology. 12:220223. [pdf] [abs]

125. Moshe, A., Wygoda, E., Ecker, N., Loewenthal, G., Avram, O., Israeli, O., Hazkani-Covo, E., Pe'er, I., and Pupko, T. 2022. An approximate Bayesian computation approach for modeling genome rearrangements. Mol. Biol. Evol. 39(11):msac231 [pdf] [abs]

124. Wagner, N., Alburquerque, M., Ecker, N., Dotan, E., Pena, M.M., Potnis, N., and Pupko, T. 2022. Natural language processing approach to model the secretion signal of type III effectors. Front. Plant Sci. 13:1024405. [pdf] [abs]

123. Ecker, N., Azouri, D., Bettisworth, B., Stamatakis, A., Mansour, Y., Mayrose, Y., and Pupko, T. 2022. A LASSO-based approach to sample sites for phylogenetic tree search. Bioinformatics. 38:i118 i124. [pdf] [abs]

122. Nagar, N., Ben-Tal, N. and Pupko, T. 2022. EvoRator: prediction of residue-level evolutionary rates from protein structures using machine learning. J. Mol. Biol. 434(11):167538. [pdf] [abs]

121. Labes S., Stupp, D., Wagner, N., Bloch, I., Lotem, M., Lahad. E.L., Polak, P., Pupko, T., and Tabach, Y. 2022. Machine-learning of complex evolutionary signals improves classification of SNVs. NAR Genomics and Bioinformatics. 4(2):lqac025. [pdf] [abs]

120. Wagner, N., Avram, O., Gold-Binshtok, D., Teper, D., and Pupko, T. 2022. Effectidor: an automated machine-learning based web server for the prediction of type-III secretion system effectors. Bioinformatics. 38(8):2341 2343. [pdf] [abs]

119. Liyanapathiranage, P., Wagner, N., Avram, O., Pupko. T., and Potnis, N. 2022. Phylogenetic distribution and evolution of Type VI secretion system in the genus Xanthomonas. Frontiers in Microbiology. 13:840308. [pdf] [abs]

118. Loewenthal, G., Rapoport, D., Avram, O., Moshe, A., Wygoda, E., Itzkovitch, A., Israeli, O., Azouri, D., Cartwright, R.A., Mayrose, I., and Pupko, T. 2021. A probabilistic model for indel evolution: differentiating insertions from deletions. Mol. Biol. Evol. 38(12):5769 5781. [pdf] [abs]

117. Ashkenazy, H., Avram, O., Ryvkin, A., Roitburd-Berman, A., Weiss-Ottolenghi, Y., Hada-Neeman, S., Gershoni, J.M., and Pupko, T. 2021. Motifier: an IgOme profiler based on peptide-motifs using machine learning. J. Mol. Biol. 433(15):167071. [pdf] [abs]

116. Mahata, T., Molshanski-Mor, S., Goren, M.G., Jana, B., Kohen-Manor, M., Yosef, I., Avram, O., Pupko, T., Salomon, D., and Qimron, U. 2021. A novel phage mechanism for selective nicking of dUMP-containing DNA. Proc Natl Acad Sci USA. 118(23):e2026354118. [pdf] [abs]

115. Azouri, D., Abadi, S., Mansour, Y., Mayrose, I., and Pupko, T. 2021. Harnessing machine learning to guide phylogenetic-tree search algorithms. Nature Communications. 12:1983. [pdf] [abs]

114. Ruano-Gallego, D., Sanchez-Garrido, J., Kozik, Z., Nunez-Berrueco, E., Cepeda-Molero, M., Mullineaux-Sanders, C., Roumeliotis, T.I., Naemi-Baghshomali, J., Slater, S., Wagner, N., Glegola-Madejska, I., Pupko, T., Fernandez, L.A., Rodriguez-Paton, A., Choudhary, J.S., and Frankel, G. 2021. Type III secretion system effectors form robust and flexible intracellular virulence networks. Science. 371(6534):eabc9531. [pdf] [abs]

113. Avram, O., Kigel, A., Vaisman-Mentesh, A., Kligsberg, S., Rosenstein, S., Dror, Y., Pupko, T., and Wine, Y. 2021. PASA: proteomic analysis of serum antibodies web server. PLoS Comput Biol. 17(1):e1008607. [pdf] [abs]

112. Nagar, N., Ecker, N., Loewenthal, G., Avram, O., Ben Meir, D., Biran, D., Ron, E., and Pupko, T. 2021. Harnessing machine learning to unravel protein degradation in Escherichia coli. mSystems. 6(1): e01296-20. [pdf] [abs]

111. Hada-Neeman, S., Weiss-Ottolenghi, Y., Wagner, N., Avram, O., Ashkenazy, H., Maor, Y., Sklan, E., Shcherbakov, D., Pupko, T., and Gershoni, J.M. 2021. Domain-Scan: combinatorial sero-diagnosis of infectious diseases using machine learning. Frontiers in Immunology. 11:619896. [pdf] [abs]

110. Loewenthal, G., Abadi, S., Avram, O., Halabi, K., Ecker, N., Nagar, N., Mayrose, I., and Pupko, T. 2020. COVID-19 pandemic-related lockdown: response time is more important than its strictness. EMBO Molecular Medicine. 12:e13171. [pdf] [abs]

109. Abadi, S, Avram, O., Rosset, S., Pupko, T., and Mayrose, I. 2020. ModelTeller: model selection for optimal phylogenetic reconstruction using machine learning. Mol. Biol. Evol. 37(11):3338 3352. [pdf] [abs]

108. Guerrero, IJ, Perez-Montano, F., Mateus da Silva, G., Wagner, N., Shkedy, D., Zhao, M., Pizarro, L.,Bar, M., Walcott, R., Sessa, G., Pupko, T., and Burdman, S. 2020. Show me your secret(ed) weapons: a multifaceted approach reveals a wide arsenal of type III-secreted effectors in the cucurbit pathogenic bacterium Acidovorax citrulli and novel effectors in the Acidovorax genus. Molecular Plant Pathology. 21(1):17 37. [pdf] [abs]

107. Sugis, E., Dauvillier, J., Leontjeva, A., Adler, P., Hindie, V., Moncion, T., Collura, V., Daudin, R., Loe-Mie, Y., Herault, Y., Lambert, J.C., Hermjakob, H., Pupko, T., Rain, J.C., Xenarios, I., Vilo, J., Simonneau, M., and Peterson, H. 2019. HENA, Heterogeneous network-based data set for Alzheimer's disease. Scientific Data. 6(1):151. [pdf] [abs]

106. Avram, O., Rapoport, D., Portugez, S., and Pupko, T. 2019. M1CR0B1AL1Z3R - a user-friendly web server for the analysis of large-scale microbial genomics data. Nucleic Acids Research. 47(Web Server issue):W88-W92. [pdf] [abs]

105. Abadi, S., Azouri, D., Pupko, T., and Mayrose, I. 2019. Model selection may not be a mandatory step for phylogeny reconstruction. Nature Communications. 10(1):934. [pdf] [abs]

104. Moshe, A., and Pupko, T. 2019. Ancestral sequence reconstruction: accounting for structural information by averaging over replacement matrices. Bioinformatics. 35(15):2562-2568. [pdf] [abs]

103. Levy Karin, E., Ashkenazy, H., Jotun, H., and Pupko, T. 2019. A simulation-based approach to statistical alignment. Systematics Biology. 68(2):252-266. [pdf] [abs]

102. Ashkenazy, H., Sela, I., Levy Karin, E., Landan, G., and Pupko, T. 2019. Multiple sequence alignment averaging improves phylogeny reconstruction. Systematics Biology. 68(1):117-130. [pdf] [abs]

101. Davis, E.W., Tabima, J.F., Weisberg, A.J., Lopes, L.C., Wiseman, M.S., Pupko, T., Belcher, M.S., Sechler, A.J., Tancos, M.A., Schroeder, B.K., Murray, T.D., Luster, D.G., Schneider, W.L., Rogers, E.E., Andreote, F.D., Grunwald, N.J., Putnam, M.D., and Chang, J.H. 2018. Evolution of the US biological select agent, Rathayibacter toxicus. mBio. 9:e01280-18. [pdf] [abs]

100. Avram, O., Vaisman-Mentesh A, Yehezkel, D., Ashkenazy, H., Pupko, T., and Wine, Y. 2018. ASAP, a webserver for immunoglobulin-sequencing analysis pipeline. Front. Immunol. 9:1686 [pdf] [abs]

99. Bar, L., Levy Karin, E., Pupko, T., and Hazkani-Covo, E. 2018. The prevalence and evolutionary conservation of inverted repeats in proteobacteria. Genome Biol Evol. 10(3):918-927. [pdf] [abs]

98. Ryvkin, A., Ashkenazy, H., Weiss-Ottolenghi, Y., Piller, C., Pupko, T., and Gershoni, J. 2018. Phage display peptide libraries: deviations from randomness and correctives. Nucleic Acids Research. 46(9):e52. [pdf] [abs]

97. Xue, Y.A., DiPizio, A., Levit, A., Yarnitzky, T., Penn, O., Pupko, T., and Niv, M.Y. 2018. Independent evolution of strychnine recognition by bitter taste receptor subtypes. Frontiers in Molecular Biosciences. 5:9. [pdf] [abs]

96. Danziger, O., Pupko, T., Bacharach, E., and Ehrlich, E. 2018. Interleukin-6 and interferon-alpha signaling via JAK1-STAT differentially regulate oncolytic versus cytoprotective antiviral states. Front. Immunol. 9:94. [pdf] [abs]

95. Nissan, G., Gershovits, M., Morozov, M., Chalupowicz, L., Sessa, G., Manulis-Sasson, S., Barash, I., and Pupko, T. 2018. Revealing the inventory of type III effectors in Pantoea agglomerans gall-forming pathovars by using draft genome sequences and a machine-learning approach. Mol Plant Pathol. 19(2):381-392. [pdf] [abs]

94. Mushegian, A., Levy Karin, E., and Pupko, T. 2018. Sequence analysis of malacoherpesvirus proteins: Pan-herpesvirus capsid module and replication enzymes with an ancient connection to "Megavirales". Virology. 513:114-128. [pdf] [abs]

93. Ashkenazy, H., Levy Karin, E., Mertens, Z., Cartwright, R.A., and Pupko, T. 2017. SpartaABC: a web server to simulate sequences with indel parameters inferred using an approximate Bayesian computation algorithm. Nucleic Acids Research. 45(W1):W453-W457. [pdf] [abs]

92. Levy Karin, E., Ashkenazy, H., Wilcke, S., Pupko, T., and Mayrose, I. 2017. TraitRateProp: a web server for the detection of trait-dependent evolutionary rate shifts in sequence sites. Nucleic Acids Research. 45(W1):W260-W264. [pdf] [abs]

91. Levy Karin, E., Shkedy, D., Ashkenazy, H., Cartwright, R.A., and Pupko, T. 2017. Inferring rates and length-distributions of indels using Approximate Bayesian Computation. Genome Biol Evol. 9(5):1280-1294. [pdf] [abs]

90. Levy Karin, E., Wilcke, S., Pupko, T., and Mayrose, I. 2017. An integrated model of phenotypic trait changes and site-specific sequence evolution. Systematics Biology. 66(6):917-933. [pdf] [abs]

89. Preisner, H., Levy Karin, E., Poschmann, G., Stuhler, K., Pupko, T., and Gould SB. 2016. The cytoskeleton of parabasalian parasites comprises proteins that share properties common to intermediate filament proteins. Protist. 167(6):526-543. [pdf] [abs]

88. McNally, A., Oren, Y., Kelly, D., Pascoe, B., Dunn, S., Sreecharan, T., Vehkala, M., Valimaki, N., Prentice, M.B., Ashour ,A., Avram, O., Pupko, T., Dobrindt, U., Literak, I., Guenther, S., Schaufler, K., Wieler, L.H., Zhiyong, Z., Sheppard, S.K., McInerney, J.O., Corander, J. 2016. Combined analysis of variation in core, accessory and regulatory genome regions provides a super-resolution view into the evolution of bacterial populations. PLoS Genet. 12(9):e1006280. [pdf] [abs]

87. Ashkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., Pupko, T., and Ben-Tal, N. 2016. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Research. 44(Web Server issue):W344-W350. [pdf] [abs]

86.Eckshtain-Levi, N., Shkedy, D., Gershovits, M., Da Silva, G.M., Tamir-Ariel, D., Walcott, R., Pupko, T., and Burdman, S. 2016. Insights from the genome sequence of Acidovorax citrulli M6, a group I strain of the causal agent of bacterial Fruit Blotch of cucurbits. Front Microbiol. 7:430. [pdf] [abs]

85. Burstein, D., Amaro, F., Zusman, T., Lifshitz, Z., Cohen, O., Gilbert, J.A., Pupko, T., Shuman, H.A., and Segal, G. 2016. Genomic analysis of 38 Legionella species reveals large and diverse effector repertoires . Nature Genetics. 48(2):167-75. [pdf] [abs]

84. Teper, D., Burstein, D., Salomon, D., Gershovitz, M., Pupko, T., and Sessa, G. 2016. Identification of novel Xanthomonas euvesicatoria type III effector proteins by a machine-learning approach. Mol Plant Pathol. 17(3):398-411. [pdf] [abs]

83. Faigenbloom, L., Rubinstein, N.D., Kloog, Y., Mayrose, I., Pupko, T., and Stein, R. 2015. Regulation of alternative splicing at the single-cell level . Mol Syst Biol. 11(12):845. [pdf] [abs]

82. Levy Karin, E., Rabin, A., Ashkenazy, H., Shkedy, D., Avram, O., Cartwright, R.A., and Pupko, T. 2015. Inferring indel parameters using a simulation-based approach . Genome Biol Evol. 7(12):3226-38. [pdf] [abs]

81. Bar-Rogovsky, H., Stern, A., Penn, O., Kobl, I., Pupko, T., and Tawfik, D.S. 2015. Assessing the prediction fidelity of ancestral reconstruction by a library approach. Protein Engineering, Design & Selection. 28(11):507-518. [pdf] [abs]

80. Sela, I., Ashkenazy, H., Kazutaka, K., and Pupko, T. 2015. GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters. Nucleic Acids Research. 43(W1):W7-W14. [pdf] [abs]

79. Burstein, D., Satanower, S., Simovitch, M., Belnik ,Y., Zehavi, M., Yerushalmi, G., Ben-Aroya, S., Pupko, T., and Banin, E. 2015. Novel type III effectors in Pseudomonas aeruginosa. mBio. 6(2):e00161-15. [pdf] [abs]

78. Molshanski-Mor, S., Yosef, I., Kiro, R., Edgar, R., Manor, M., Gershovits, M., Laserson, M., Pupko, T., and Qimron, U. 2014. Revealing bacterial targets of growth inhibitors encoded by bacteriophage T7. Proc Natl Acad Sci USA. 111(52):18715-18720. [pdf] [abs]

77. Ashkenazy, H., Cohen, O., Pupko, T., and Huchon, D. 2014. Indel reliability in indel-based phylogenetic inference. Genome Biol Evol.6(12):3199-3209. [pdf] [abs]

76. Oren, Y., Smith, M.B., Johns, N.I., Kaplan Zeevi, M., Biran. D., Ron, E.Z., Corander, J., Wang, H.H., Alm. E.J., and Pupko, T. 2014. Transfer of noncoding DNA drives regulatory rewiring in bacteria. Proc Natl Acad Sci USA. 111(45):16112-16117. [pdf] [abs]

75. Levy Karin, E., Susko, E., and Pupko, T. 2014. Alignment errors strongly impact likelihood-based tests for comparing topologies. Mol Biol Evol. 31(11):3057-3067. [pdf] [abs]

74. Lifshitz, Z., Burstein, D., Schwartz, K., Shuman, H.A., Pupko, T., and Segal, G. 2014. Identification of novel Coxiella burnetii Icm/Dot effectors and genetic analysis of their involvement in modulating a mitogen-activated protein kinase pathway. Infect Immun. 82(9):3740-3752. [pdf] [abs]

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

72. Yossef, I., Shitrit, D., Goren, M.G., Burstein, D., Pupko, T., and Qimron, U. 2013. DNA motifs determining the efficiency of adaptation into the Escherichia coli CRISPR array. Proc Natl Acad Sci USA. 110(35):14396-14401. [pdf] [abs]

71. Anisimova, M., Liberles, D.A., Philippe, H., Provan, J., Pupko, T., and von Haeseler, A. 2013. State-of the art methodologies dictate new standards for phylogenetic analysis. BMC Evolutionary Biology. 13:161. [pdf] [abs]

70. Cohen, O., Ashkenazy, H., Levy Karin, E., Burstein, D., and Pupko, T. 2013. CoPAP: Co-evolution of presence-absence patterns. Nucleic Acids Research. 41(Web Server issue):W232-W237. [pdf] [abs]

69. Celniker, G., Nimrod, G., Ashkenazy, H., Glaser, F., Martz, E., Mayrose, I., Pupko, T., and Ben-Tal, N. 2013. ConSurf: using evolutionary data to raise testable hypotheses about protein function. Israel Journal of Chemistry. 53(3-4):199-206. [pdf] [abs]

68. Lifshitza, Z., Burstein, D., Peeri, M., Zusman, T., Schwartz, K., Shuman, H.A., Pupko, T., and Segal, G. 2013. Computational modeling and experimental validation of the Legionella and Coxiella virulence-related Type-IVB secretion signal. Proc Natl Acad Sci USA. 110(8):E707-715. [pdf] [abs]

67. Cohen, O., Ashkenazy, H., Burstein, D., and Pupko, T. 2012. Uncovering the co-evolutionary network among prokaryotic genes. Bioinformatics. 28 ECCB 2012:i389-i394. [pdf] [abs]

66. Ryvkin, A., Ashkenazy, H., Smelyanski, L., Kaplan, G., Penn, O., Weiss-Ottolenghi, Y., Privman, E., Ngam, P.B., Woodward, J.E., May, G.D., Bell, C., Pupko, T., and Gershoni, J.M. 2012. Deep panning: steps towards probing the IgOme. PLoS ONE. 7(8): e41469. [pdf] [abs]

65. Ashkenazy, H., Penn, O., Doron-Faigenboim, A., Cohen, O., Cannarozzi, G., Zomer, O., and Pupko, T. 2012. FastML: a web server for probabilistic reconstruction of ancestral sequences. Nucleic Acids Research. 40(Web Server issue):W580-W584. [pdf] [abs]

64. Liberles, D.A, Teichmann, S.A., Bahar, I., Bastolla, U., Bloom, J., Bornberg-Bauer, E., Colwell, L.J., de Koning, A.P., Dokholyan, N.V., Echave, J., Elofsson, A., Gerloff, D.L., Goldstein, R.A., Grahnen, J.A., Holder, M.T., Lakner, C., Lartillot, N., Lovell, S.C., Naylor, G., Perica, T., Pollock, D.D, Pupko, T., Regan, L., Roger, A., Rubinstein, N., Shakhnovich, E., Sjolander, K., Sunyaev, S., Teufel, A.I., Thorne, J.L., Thornton, J.W., Weinreich, D.M., Whelan, S. 2012. The interface of protein structure, protein biophysics, and molecular evolution. Protein Sci. 21(6):769-785. [pdf] [abs]

63. Amit, M., Donyo, M., Hollander, D., Goren, Aa., Kim, E., Gelfman, S., Lev-Maor, G., Burstein, D., Schwartz, S., Postolsky, B., Pupko, T., and Ast, G. 2012. Differential GC content between exons and introns establishes distinct strategies of splice-site recognition. Cell Reports. 1(5):543-556. [pdf] [abs]

62. Gelfman, S., Burstein, D., Penn, O., Schwartz, S., Pupko, T., and Ast, G. 2012. Changes in exon-intron structure during vertebrate evolution affect the splicing pattern of exons. Genome Res. 22(1):35-50. [pdf] [abs]

61. Privman, E, Penn, O. and Pupko, T. 2012. Improving the performance of positive selection inference by filtering unreliable alignment regions. Mol. Biol. Evol. 29(1):1-5. [pdf] [abs]

60. Burstein, D., Gould, S.B., Zimorski, V., Klosges, T., Kiosse, F., Major, P., Martin, W., Pupko, T., Dagan, T. 2012. A machine-learning approach to identify hydrogenosomal proteins in Trichomonas vaginalis. Eukaryotic Cell 11:217-228. [pdf] [abs]

59. Turner, D., Amit, S., Chalom, S., Penn, O., Pupko T., Katchman, E., Matus, N., Tellio, H., Katzir, M., Avidor, B. 2012. Emergence of an HIV-1 cluster harboring the major protease L90M mutation among treatment-naive patients in Tel-Aviv, Israel. HIV Medicine 13:202-206. [pdf] [abs]

58. Cohen, O. and Pupko, T. 2011. Inference of gain and loss events from phyletic patterns using stochastic mapping and maximum parsimony - a simulation study. Genome Biol Evol 3: 1265-1275. [pdf] [abs]

57. Rubinstein, N.D., Zeevi, D., Oren, Y., Segal, G., and Pupko, T. 2011. The operonic location of auto-transcriptional repressors is highly conserved in bacteria. Mol. Biol. Evol. 28(12):3309-3318. [pdf] [abs]

56. Rubinstein, N.D., Mayrose, I., Doron-Faigenboim, A., and 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]

55. Barzel, A., Privman, E., Peeri, M., Naor, A., Shachar, E., Burstein, D., Lazary, R., Gophna, U., Pupko, T., and Kupiec, M. 2011. Native homing endonucleases can target conserved genes in humans and in animal models. Nucleic Acids Research. 39(15):6646-6659. [pdf] [abs]

54. Cohen, O., and Pupko, T. 2011. The complexity hypothesis revisited: connectivity rather than function constitutes a barrier to horizontal gene transfer. Mol. Biol. Evol. 28(4):1481-1489. [pdf] [abs]

53. Pupko, T. 2011. Evolution after gene dupliction. Book review. Trends in Evolutionary Biology 3:e1. [pdf]

52. Keren, H., Donyo, M., Zeevi, D., Maayan, C., Pupko, T., and Ast, G. 2010. Phosphatidylserine increases IKBKAP levels in familial dysautonomia cells. PLoS ONE 5(12):e15884. [pdf] [abs]

51. Cohen, O., Ashkenazy, H., Belinky, F., Huchon, D., and Pupko, T. 2010. GLOOME: gain loss mapping engine. Bioinformatics 26(22):2914-2915. [pdf] [abs]

50. Penn, O., Privman, E., Ashkenazy, H., Landan, G., Graur, D., and Pupko, T. 2010. GUIDANCE: a web server for assessing alignment confidence scores. Nucleic Acids Research. 38(Web Server issue):W23-W28. [pdf] [abs]

49. Ashkenazy, H., Erez, E.,Martz, E., Pupko, T., and Ben-Tal, N. 2010. ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Research. 38(Web Server issue):W529-W533. [pdf] [abs]

48. Loe-Mie, Y., Lepagnol-Bestel, A.D., Maussion, G., Doron-Faigenboim, A., Imbeaud, S., Delacroix, H., Aggerbeck, L., Pupko, T., Gorwood, P., Simonneau, M., and Moalic, J.M. 2010. SMARCA2 and other genome-wide supported schizophrenia-associated genes: regulation by REST/NRSF, network organization and primate-specific evolution. Human Molecular Genetics 19(14):2841-2857. [pdf] [abs]

47. Penn, O., Privman, E., Landan, G., Graur, D., and Pupko, T. 2010. An alignment confidence score capturing robustness to guide-tree uncertainty. Mol. Biol. Evol. 27(8):1759-1767. [pdf] [abs]

46. Cohen, O., and Pupko T. 2010. Inference and characterization of horizontally transferred gene families using stochastic mapping. Mol. Biol. Evol. 27(3):703-713. [pdf] [abs]

45. Stern, A., Mayrose, I., Penn, O., Shaul, S., Gophna, U., and Pupko, T. 2010. An evolutionary analysis of lateral gene transfer in thymidylate synthase enzymes. Systematic Biology. 59(2):212-225. [pdf] [abs]

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

43. Burstein, D., Zusman, T., Degtyar, E., Viner, R., Segal, G., and Pupko, T. 2009. Genome-scale identification of Legionella pneumophila effectors using a machine learning approach. PLoS Pathog 5(7):e1000508. [pdf] [abs]

42. Blanga-Kanfi, S., Miranda, H., Penn, O., Pupko, T., DeBry, R.W., and Huchon, D. 2009. Rodent phylogeny revised: Analysis of six nuclear genes from all major rodent clades. BMC Evolutionary Biology 9:71. [pdf] [abs]

41. Rubinstein, N.D., Mayrose, I., and Pupko, T. 2009. A machine-learning approach for predicting B-cell epitopes. Mol. Immunol. 46(5):840-847. [pdf] [abs]

40. Penn, O., Stern, A., Rubinstein, N.D., Dutheil, J., Bacharach, E., Galtier, N., and Pupko, T. 2008. Evolutionary modeling of rate shifts reveals specificity determinants in HIV-1 subtypes. PLoS Comput Biol. 4(11):e1000214. [pdf] [abs]

39. Sela, N., Stern, A., Makalowski, W., Pupko. T., and Ast, G. 2008. Transduplication resulted in the incorporation of two protein-coding sequences into the Turmoil-1 transposable element of C. elegans. Biology Direct. 3:41. [pdf] [abs]

38. Cohen, O., Rubinstein, N.D., Stern, A., Gophna, U., and Pupko, T. 2008. A likelihood framework to analyse phyletic patterns. Philos Trans R Soc Lond B Biol Sci. 363:3903-3911. [pdf] [abs]

37. Rubinstein, N.D., Mayrose, I., Halperin, D., Yekutieli, D., Gershoni, J.M., and Pupko, T. 2008. Computational characterization of B-cell epitopes. Mol. Immunol. 45:3477-3489. [pdf] [abs]

36. Schwartz, S., Silva, J., Burstein, D., Pupko, T., Eyras, E., and Ast, G. 2008. Large scale comparative analysis of splicing signals and their corresponding splicing factors in eukaryotes. Genome Res. 18(1):88-103. [pdf] [abs]

35. Mayrose, I., Penn, O., Erez, E., Rubinstein, N.D., Shlomi, T., Tarnovitski Freund, N., Bublil, E., Rupin, E., Sharan, R., Gershoni, J.M., Martz, E., and Pupko, T. 2007. Pepitope: epitope mapping from affinity-selected peptides. Bioinformatics 23(23):3244-3246. [pdf] [abs]

34. Lev-Maor, G., Goren, A., Sela, A., Kim, E., Keren, H., Doron-Faigenboim, A., Leibman-Barak, S., Pupko, T., and Ast, G. 2007. The "alternative" choice of constitutive exons through evolution. PLoS Genet. 3(11):e203. [pdf] [abs]

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

32. Stern, A., Doron-Faigenboim, A., Bacharach, E., and Pupko, T. 2007. Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach. Nucleic Acids Research. 35:W506-W511. [pdf] [abs]

31. Bublil, E.M., Freund, N.T., Mayrose, I., Penn, O., Roitburd-Berman, A., Rubinstein, N.D., Pupko, T., and Gershoni, J.M. 2007. Stepwise prediction of conformational discontinuous B-cell epitopes using the Mapitope algorithm. Proteins 68(1):293-304. [pdf] [abs]

30. Doron-Faigenboim, A., and Pupko, T. 2007. A combined empirical and mechanistic codon model. Mol. Biol. Evol. 24(2):388-397. [pdf] [abs]

29. Mayrose, I., Shlomi, T., Rubinstein, N., Gershoni, J.M., Ruppin, E., Sharan, R., and Pupko, T. 2007. Epitope mapping using combinatorial phage-display libraries: A graph-based algorithm. Nucleic Acids Research. 35(1):69-78. [pdf] [abs]

28. Ninio, M., Privman, E., Pupko, T., and Friedman, N. 2007. Phylogeny reconstruction: increasing the accuracy of pairwise distance estimation using Bayesian inference of evolutionary rates. Bioinformatics. 23:e136-e141. [pdf] [abs]

27. Stern, A., Privman, E., Rasis, M., Lavi, S., and Pupko, T. 2007. Evolution of the metazoan protein phosphatase 2C superfamily. J. Mol. Evol. 64(1):61-70. [pdf] [abs]

26. Goren, A., Ram, O., Amit, M., Keren, H., Lev-Maor, G., Vig, I., Pupko, T., and Ast, G. 2006. Comparative analysis identifies exonic splicing regulatory sequences - the complex definition of enhancers and silencers. Mol. Cell 22(6):769-781. [pdf] [abs]

25. Shaul, S., Nussinov, R., and Pupko, T. 2006. Paths of lateral gene transfer of lysyl-aminoacyl-tRNA synthetases with a unique evolutionary transition stage of prokaryotes coding for class I and II varieties by the same organisms. BMC Evol. Biol. 6:12. [pdf] [abs]

24. Stern, A., and Pupko, T. 2006. An evolutionary space-time model with varying among-site dependencies. Mol. Biol. Evol. 23(2):392-400. [pdf] [abs]

23. Mayrose, I., Friedman, N., and Pupko, T. 2005. A Gamma mixture model better accounts for among site rate heterogeneity. Bioinformatics. 21:Suppl 2:ii151-ii158. [pdf] [abs]

22. Nimrod, G., Glaser, F., Steinberg, D., Ben-Tal, N., and Pupko, T. 2005. In silico identification of functional regions in proteins. Bioinformatics. 21 Suppl 1:i328-i337. [pdf] [abs]

21. Landau, M., Mayrose, I., Rosenberg, Y., Glaser, F., Martz, E., Pupko, T., and Ben-Tal, N. 2005. ConSurf 2005: The projection of evolutionary conservation scores of residues on protein structures. Nucleic Acid Research. 33:W299-W302. [pdf] [abs]

20. Dutheil, J., Pupko, T., Jean-Marie, A., and Galtier, N. 2005. A model-based approach for detecting co-evolving positions in a molecule. Mol. Biol. Evol. 22(9):1919-1928. [pdf] [abs]

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

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

17. Glaser, F., Rosenberg, Y., Kessel, A., Pupko, T., and Ben-Tal, N. 2005. The ConSurf-HSSP database: the mapping of evolutionary conservation among homologs onto PDB Structures. Proteins. 58(3):610-617. [pdf] [abs]

16. Mayrose, I., Graur, D., Ben-Tal, N., and Pupko, T. 2004. Comparison of site-specific rate-inference methods: Bayesian methods are superior. Mol. Biol. Evol. 21(9):1781-1791. [pdf] [abs]

15. Melamed, D., Mark-Danieli, M., Kenan-Eichler, M., Kraus, O., Castiel, A., Laham, N., Pupko, T., Glaser, F., Ben-Tal, N., and Bacharach, E. 2004. The conserved carboxy-terminus of the human immunodeficiency virus type 1 Gag protein is important for virion assembly and release. J Virol. 78(18): 9675-9688. [pdf] [abs]

14. Berezin, C., Glaser, F., Rosenberg, J., Paz, I., Pupko, T., Fariselli, P., Casadio, R., and Ben-Tal, N. 2004. ConSeq: The identification of functionally and structurally important residues in protein sequences. Bioinformatics 20(8):1322-1324. [pdf] [abs]

13. Pe'er, I., Pupko, T., Shamir, R., and Sharan, R. 2004. Incomplete directed perfect phylogeny. SIAM J. on Computing. 33(3):590-607. [pdf] [abs]

12. Pupko, T., Sharan, R., Hasegawa, M., Shamir, R., and Graur, D. 2003. Detecting excess radical replacements in phylogenetic trees. Gene. 13(319):127-135. [pdf] [abs]

11. Glaser, F., Pupko, T., Paz, I., Bechor, D., Martz, E., and Ben-Tal, N. 2003. ConSurf: A server for the identification of functional regions in proteins by surface-mapping of phylogenetic information. Bioinformatics 19(1):163-164. [pdf] [abs]

10. Pupko, T., Huchon, D., Cao, Y., Okada, N., and Hasegawa, M. 2002. Combining multiple datasets in a likelihood analysis: which models are best. Mol. Biol. Evol. 19(12):2294-2307. [pdf] [abs]

9. Pupko, T., Bell, R.E., Mayrose, I., Glaser, F., and 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]

8. Pupko, T., and Galtier, N. 2002. A covarion-based method for detecting molecular adaptation: application to the evolution of primate mitochondrial genomes. Proc R Soc Lond B Biol Sci. 269(1498):1313-1316. [pdf] [abs]

7. Pupko, T., Pe'er, I., Graur, D., Hasegawa, M., and Friedman,s N. 2002. A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites: application to the evolution of five gene families. Bioinformatics 18(8):1116-1123. [pdf] [abs]

6. Friedman, N., Ninio, M., Pe'er, I., and Pupko, T. 2002. A structural EM algorithm for phylogenetic inference. J. Comput. Biol. 9(2):331-353. [pdf] [abs]

5. Pupko, T., and Graur, D. 2002. Fast computation of maximum likelihood trees by numerical approximation of amino-acid replacement probabilities. Computational Statistics and Data Analysis 40:285-291. [pdf] [abs]

4. Pupko, T., Sharan, R., Hasegawa, M., Shamir, R., and Graur, D. 2001. A chemical-distance-based test for positive Darwinian selection. Lecture Notes in Computer Science 2149:142-155.

3. Graur, D., and Pupko, T. 2001. The Permian bacterium that isn't. Mol. Biol. Evol. 18(6):1143-1146. [pdf]

2. Pupko, T., Pe'er, I., Shamir, R., and Graur, D. 2000. A fast algorithm for joint reconstruction of ancestral amino-acid sequences. Mol. Biol. Evol. 17(6):890-896. [pdf] [abs]

1. Pupko, T., and Graur, D. 1999. Evolution of microsatellites in the yeast Saccharomyces cerevisiaei: role of length and number of repeated units. J. Mol. Evol. 48:313-316. [pdf] [abs]

Book chapters:

1. Pupko, T., Doron-Faigenboim, A., Liberles, DA., and Cannarozzi, GM. 2007. Probabilistic models and their impact on the accuracy of reconstructed ancestral protein sequences. In Liberles DA (Editor). Ancestral Sequence Reconstruction. Oxford University Press. [pdf] [abs]

2. Pupko, T., and Mayrose, I. 2010. Probabilistic methods and rate heterogeneity. In Lodhi., H., and Muggleton, S (Editors). Element of Computational Systems Biology. Wiley Book Series on Bioinformatics. [pdf] [abs]

3. Rubinstein, N.D., and Pupko, T. 2012. Detection and analysis of conservation at synonymous sites. In Cannarozzi GM and Schneider A (Editors). Codon Evolution: Mechanisms and Models. Oxford University Press. [abs]

4. Cohen, O., Gophna, O., and Pupko, T. 2013. The complexity hypothesis and other connectivity barriers to lateral gene transfer. In Gophna O (Editor). Lateral Gene Transfer in Evolution. Springer Science. [abs]

5. Pupko, T., and Mayrose, I. 2020. A gentle introduction to probabilistic evolutionary models. In Scornavacca, C., Delsuc, F., and Galtier, N. (Editors). Phylogenetics in the Genomic Era. A book completely handled by researchers. [book]

6. Wagner, N., Teper, D., and Pupko, T. 2022. Predicting type III effector proteins using the Effectidor web server. In Gal-Mor, O. (Editor). Methods in Molecular Biology, Vol. 2427: Bacterial Virulence. Published by Springer Nature.