Tal Pupko

21.9k total citations · 8 hit papers
142 papers, 15.1k citations indexed

About

Tal Pupko is a scholar working on Molecular Biology, Genetics and Plant Science. According to data from OpenAlex, Tal Pupko has authored 142 papers receiving a total of 15.1k indexed citations (citations by other indexed papers that have themselves been cited), including 119 papers in Molecular Biology, 46 papers in Genetics and 17 papers in Plant Science. Recurrent topics in Tal Pupko's work include Genomics and Phylogenetic Studies (73 papers), RNA and protein synthesis mechanisms (36 papers) and Genetic diversity and population structure (24 papers). Tal Pupko is often cited by papers focused on Genomics and Phylogenetic Studies (73 papers), RNA and protein synthesis mechanisms (36 papers) and Genetic diversity and population structure (24 papers). Tal Pupko collaborates with scholars based in Israel, United States and Japan. Tal Pupko's co-authors include Nir Ben‐Tal, Eric Martz, Haim Ashkenazy, Itay Mayrose, Fabian Glaser, Shiran Abadi, Elona Erez, Dan Graur, Inbal Paz and Ofir Cohen and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Tal Pupko

140 papers receiving 14.9k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules

2016 2.2k citations Haim Ashkenazy, Shiran Abadi et al. Nucleic Acids Research profile →
ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids

2010 1.5k citations Haim Ashkenazy, Eric Martz et al. Nucleic Acids Research profile →
ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures

2005 1.1k citations Meytal Landau, Itay Mayrose et al. Nucleic Acids Research profile →
ConSurf: Identification of Functional Regions in Proteinsby Surface-Mapping of Phylogenetic Information

2002 993 citations Fabian Glaser, Tal Pupko et al. Bioinformatics profile →
GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters

2015 634 citations Haim Ashkenazy, Tal Pupko et al. Nucleic Acids Research profile →
GUIDANCE: a web server for assessing alignment confidence scores

2010 537 citations Haim Ashkenazy, Tal Pupko et al. Nucleic Acids Research profile →
Model selection may not be a mandatory step for phylogeny reconstruction

2019 261 citations Shiran Abadi, Dana Azouri et al. profile →
Using evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf

2023 186 citations Elon Yariv, Amit Kessel et al. Protein Science profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Tal Pupko Israel	50	10.7k	2.7k	1.7k	1.5k	1.1k	142	15.1k
Michael Remmert Germany	11	9.1k 0.9×	1.7k 0.6×	2.2k 1.2×	1.6k 1.1×	957 0.9×	14	14.0k
Lawrence A. Kelley United Kingdom	22	10.0k 0.9×	1.9k 0.7×	2.6k 1.5×	1.4k 0.9×	1.2k 1.1×	40	15.3k
Fabian Sievers Ireland	13	8.9k 0.8×	1.7k 0.6×	2.3k 1.4×	1.6k 1.1×	719 0.7×	16	14.2k
Kevin Karplus United States	36	12.5k 1.2×	1.9k 0.7×	2.3k 1.3×	1.7k 1.1×	1.7k 1.6×	83	18.8k
Lakshminarayan M. Iyer United States	56	10.7k 1.0×	2.3k 0.8×	1.8k 1.0×	1.6k 1.1×	409 0.4×	115	14.8k
Boris Maček Germany	55	10.7k 1.0×	1.7k 0.6×	1.4k 0.8×	995 0.7×	665 0.6×	203	14.7k
Andreas Heger United Kingdom	34	10.7k 1.0×	2.2k 0.8×	3.3k 1.9×	1.7k 1.2×	569 0.5×	55	15.9k
John‐Marc Chandonia United States	18	9.2k 0.9×	1.6k 0.6×	2.0k 1.1×	1.1k 0.7×	1.2k 1.1×	41	12.0k
Michael Y. Galperin United States	70	12.6k 1.2×	3.6k 1.3×	2.2k 1.2×	3.1k 2.1×	1.1k 1.0×	209	17.9k
Martin Steinegger South Korea	25	9.7k 0.9×	1.3k 0.5×	1.4k 0.8×	2.2k 1.4×	1.2k 1.1×	57	13.5k

Countries citing papers authored by Tal Pupko

Since Specialization

Citations

This map shows the geographic impact of Tal Pupko's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tal Pupko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tal Pupko more than expected).

Fields of papers citing papers by Tal Pupko

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tal Pupko. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tal Pupko. The network helps show where Tal Pupko may publish in the future.

Co-authorship network of co-authors of Tal Pupko

This figure shows the co-authorship network connecting the top 25 collaborators of Tal Pupko. A scholar is included among the top collaborators of Tal Pupko based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tal Pupko. Tal Pupko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Avram, Oren, et al.. (2024). BetaAlign: a deep learning approach for multiple sequence alignment. Bioinformatics. 41(1). 3 indexed citations

Mayrose, Itay, et al.. (2024). Statistical framework to determine indel-length distribution. Bioinformatics. 40(2). 3 indexed citations

Novosolov, Maria, Carmela Gissi, Rade Garić, et al.. (2024). Evolutionary Insights from the Mitochondrial Genome of Oikopleura dioica : Sequencing Challenges, RNA Editing, Gene Transfers to the Nucleus, and tRNA Loss. Genome Biology and Evolution. 16(9). 2 indexed citations

Huchon, Dorothée, et al.. (2024). A machine-learning-based alternative to phylogenetic bootstrap. Bioinformatics. 40(Supplement_1). i208–i217. 1 indexed citations

Bowers, Robert M., Eric A. Newberry, Tal Pupko, et al.. (2024). Genetic and Functional Diversity Help Explain Pathogenic, Weakly Pathogenic, and Commensal Lifestyles in the Genus Xanthomonas. Genome Biology and Evolution. 16(4). 9 indexed citations

Pupko, Tal, et al.. (2023). Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes. The Journal of Immunology. 211(10). 1578–1588. 13 indexed citations

Huchon, Dorothée, et al.. (2023). GenomeFLTR: filtering reads made easy. Nucleic Acids Research. 51(W1). W232–W236. 4 indexed citations

Yariv, Elon, Amit Kessel, Gal Masrati, et al.. (2023). Using evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf. Protein Science. 32(3). e4582–e4582. 186 indexed citations breakdown →

Glick, Lior, et al.. (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(12). 220223–220223. 5 indexed citations

10.

Stupp, Doron, et al.. (2022). Machine-learning of complex evolutionary signals improves classification of SNVs. NAR Genomics and Bioinformatics. 4(2). lqac025–lqac025. 4 indexed citations

11.

Avram, Oren, et al.. (2021). A Probabilistic Model for Indel Evolution: Differentiating Insertions from Deletions. Molecular Biology and Evolution. 38(12). 5769–5781. 17 indexed citations

12.

Avram, Oren, et al.. (2021). Harnessing Machine Learning To Unravel Protein Degradation in Escherichia coli. mSystems. 6(1). 16 indexed citations

13.

Ruano‐Gallego, David, Julia Sanchez‐Garrido, Caroline Mullineaux-Sanders, et al.. (2021). Type III secretion system effectors form robust and flexible intracellular virulence networks. Science. 371(6534). 70 indexed citations

14.

Abadi, Shiran, Oren Avram, Saharon Rosset, Tal Pupko, & Itay Mayrose. (2020). ModelTeller: Model Selection for Optimal Phylogenetic Reconstruction Using Machine Learning. Molecular Biology and Evolution. 37(11). 3338–3352. 31 indexed citations

15.

Abadi, Shiran, et al.. (2020). COVID ‐19 pandemic‐related lockdown: response time is more important than its strictness. EMBO Molecular Medicine. 12(11). e13171–e13171. 31 indexed citations

16.

Avram, Oren, et al.. (2019). M1CR0B1AL1Z3R—a user-friendly web server for the analysis of large-scale microbial genomics data. Nucleic Acids Research. 47(W1). W88–W92. 98 indexed citations

17.

Ashkenazy, Haim, Shiran Abadi, Eric Martz, et al.. (2016). ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Research. 44(W1). W344–W350. 2170 indexed citations breakdown →

18.

Chalupowicz, Laura, et al.. (2016). Revealing the inventory of type III effectors in Pantoea agglomerans gall‐forming pathovars using draft genome sequences and a machine‐learning approach. Molecular Plant Pathology. 19(2). 381–392. 24 indexed citations

19.

Mayrose, Itay, Adi Doron‐Faigenboim, Eran Bacharach, & Tal Pupko. (2007). Towards realistic codon models: among site variability and dependency of synonymous and non-synonymous rates. Bioinformatics. 23(13). i319–i327. 47 indexed citations

20.

Landau, Meytal, Itay Mayrose, Y. Rosenberg, et al.. (2005). ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures. Nucleic Acids Research. 33(Web Server). W299–W302. 1144 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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