Remo Rohs

10.6k total citations · 2 hit papers
91 papers, 7.2k citations indexed

About

Remo Rohs is a scholar working on Molecular Biology, Genetics and Plant Science. According to data from OpenAlex, Remo Rohs has authored 91 papers receiving a total of 7.2k indexed citations (citations by other indexed papers that have themselves been cited), including 86 papers in Molecular Biology, 10 papers in Genetics and 7 papers in Plant Science. Recurrent topics in Remo Rohs's work include Genomics and Chromatin Dynamics (55 papers), RNA and protein synthesis mechanisms (45 papers) and DNA and Nucleic Acid Chemistry (17 papers). Remo Rohs is often cited by papers focused on Genomics and Chromatin Dynamics (55 papers), RNA and protein synthesis mechanisms (45 papers) and DNA and Nucleic Acid Chemistry (17 papers). Remo Rohs collaborates with scholars based in United States, Israel and Germany. Remo Rohs's co-authors include Richard S. Mann, Barry Honig, Sean M. West, Tianyin Zhou, Lin Yang, Iris Dror, Ana Carolina Dantas Machado, Alona Sosinsky, Rohit Joshi and Peng Liu and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Remo Rohs

88 papers receiving 7.1k citations

Hit Papers

The role of DNA shape in protein–DNA recognition 2009 2026 2014 2020 2009 2010 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Remo Rohs United States 43 6.4k 945 653 510 391 91 7.2k
Guillermo Montoya Spain 44 5.0k 0.8× 865 0.9× 564 0.9× 404 0.8× 251 0.6× 134 5.8k
Diana R. Tomchick United States 54 6.6k 1.0× 571 0.6× 705 1.1× 605 1.2× 165 0.4× 115 9.6k
Ralf Ficner Germany 48 5.6k 0.9× 587 0.6× 458 0.7× 351 0.7× 323 0.8× 169 6.9k
Fabian Glaser Israel 34 4.6k 0.7× 707 0.7× 479 0.7× 311 0.6× 529 1.4× 76 6.2k
Di Xia United States 40 4.0k 0.6× 831 0.9× 393 0.6× 1.1k 2.1× 319 0.8× 154 6.0k
Sina Ghaemmaghami United States 27 8.1k 1.3× 799 0.8× 443 0.7× 230 0.5× 182 0.5× 57 9.1k
T. Kigawa Japan 46 6.1k 1.0× 717 0.8× 1.2k 1.9× 341 0.7× 382 1.0× 181 7.5k
Gilbert G. Privé Canada 48 6.5k 1.0× 1.1k 1.2× 492 0.8× 789 1.5× 296 0.8× 94 8.5k
O. Gileadi United Kingdom 44 4.3k 0.7× 568 0.6× 346 0.5× 749 1.5× 162 0.4× 108 5.5k
A.R. Ferré-D′Amaré United States 58 9.7k 1.5× 1.3k 1.3× 339 0.5× 259 0.5× 671 1.7× 144 10.5k

Countries citing papers authored by Remo Rohs

Since Specialization
Citations

This map shows the geographic impact of Remo Rohs'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 Remo Rohs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Remo Rohs more than expected).

Fields of papers citing papers by Remo Rohs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Remo Rohs. 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 Remo Rohs. The network helps show where Remo Rohs may publish in the future.

Co-authorship network of co-authors of Remo Rohs

This figure shows the co-authorship network connecting the top 25 collaborators of Remo Rohs. A scholar is included among the top collaborators of Remo Rohs 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 Remo Rohs. Remo Rohs is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hong, Yoo S., et al.. (2025). RNAproDB: A Webserver and Interactive Database for Analyzing Protein–RNA Interactions. Journal of Molecular Biology. 437(15). 169012–169012. 2 indexed citations
2.
Singh, Jaideep, et al.. (2025). PAM-adjacent DNA flexibility tunes CRISPR-Cas12a off-target binding. Scientific Reports. 15(1). 4930–4930. 2 indexed citations
3.
Weller, J. & Remo Rohs. (2025). BPS2025 - Automated structure-based drug design with generative deep learning. Biophysical Journal. 124(3). 323a–323a. 1 indexed citations
4.
Bonnell, Victoria A., Yuning Zhang, Alan S. Brown, et al.. (2024). DNA sequence and chromatin differentiate sequence-specific transcription factor binding in the human malaria parasite Plasmodium falciparum. Nucleic Acids Research. 52(17). 10161–10179. 6 indexed citations
5.
Rohs, Remo, et al.. (2024). Structure-Based Drug Design with a Deep Hierarchical Generative Model. Journal of Chemical Information and Modeling. 64(16). 6450–6463. 11 indexed citations
6.
7.
Machado, Ana Carolina Dantas, et al.. (2023). DNA binding specificity of all four Saccharomyces cerevisiae forkhead transcription factors. Nucleic Acids Research. 51(11). 5621–5633. 3 indexed citations
8.
Zhao, Yongqian, Vincy Wing Sze Ho, Remo Rohs, et al.. (2021). Humanizing the yeast origin recognition complex. Nature Communications. 12(1). 33–33. 33 indexed citations
9.
Poul, Yann Le, Rita Jaenichen, David Hörl, et al.. (2020). Regulatory encoding of quantitative variation in spatial activity of a Drosophila enhancer. Science Advances. 6(49). 19 indexed citations
10.
Wu, Xiaolin, Bo Cao, Tsu-Pei Chiu, et al.. (2020). Epigenetic competition reveals density-dependent regulation and target site plasticity of phosphorothioate epigenetics in bacteria. Proceedings of the National Academy of Sciences. 117(25). 14322–14330. 28 indexed citations
11.
Jiang, Yining, et al.. (2018). Experimental maps of DNA structure at nucleotide resolution distinguish intrinsic from protein-induced DNA deformations. Nucleic Acids Research. 46(5). 2636–2647. 19 indexed citations
12.
Ma, Wenxiu, Lin Yang, Remo Rohs, & William Stafford Noble. (2017). DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding. Bioinformatics. 33(19). 3003–3010. 24 indexed citations
13.
Felice, Rosa Di, Xiaojun Zhang, Ian M. Slaymaker, et al.. (2017). CRISPR–Cas9 Mediated DNA Unwinding Detected Using Site-Directed Spin Labeling. ACS Chemical Biology. 12(6). 1489–1493. 23 indexed citations
14.
Mathelier, Anthony, et al.. (2016). DNA Shape Features Improve Transcription Factor Binding Site Predictions In Vivo. Cell Systems. 3(3). 278–286.e4. 91 indexed citations
15.
Zhou, Tianyin, Ning Shen, Lin Yang, et al.. (2015). Quantitative modeling of transcription factor binding specificities using DNA shape. Proceedings of the National Academy of Sciences. 112(15). 4654–4659. 178 indexed citations
16.
Zhang, Xiaojun, et al.. (2015). An Integrated Spin-Labeling/Computational-Modeling Approach for Mapping Global Structures of Nucleic Acids. Methods in enzymology on CD-ROM/Methods in enzymology. 564. 427–453. 16 indexed citations
17.
Zentner, Gabriel E., Sivakanthan Kasinathan, Beibei Xin, Remo Rohs, & Steven Henikoff. (2015). ChEC-seq kinetics discriminates transcription factor binding sites by DNA sequence and shape in vivo. Nature Communications. 6(1). 8733–8733. 127 indexed citations
18.
Zhou, Tianyin, Lin Yang, Yan Lü, et al.. (2013). DNAshape: a method for the high-throughput prediction of DNA structural features on a genomic scale. Nucleic Acids Research. 41(W1). W56–W62. 227 indexed citations
19.
Rohs, Remo, Xiangshu Jin, Sean M. West, et al.. (2010). Origins of Specificity in Protein-DNA Recognition. Annual Review of Biochemistry. 79(1). 233–269. 707 indexed citations breakdown →
20.
Rohs, Remo, et al.. (1999). Unraveling Proteins: A Molecular Mechanics Study. Biophysical Journal. 76(5). 2760–2768. 91 indexed citations

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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