M. Krone, F. Frieß, K. Scharnowski, G. Reina, S. Fademrecht, T. Kulschewski, J. Pleiss, and T. Ertl. IEEE Visual Analytics Science & Technology Conference, IEEE Information Visualization Conference, and IEEE Scientifc Visualization Conference : proceedings 2016, 23, 1, page 701-710. New York, NY, IEEE Computer Society, (2017)
A. Jeltsch, P. Bashtrykov, and S. Adam. Dataset, (2023)Related to: Sabrina Adam, Viviane Klingel, Nicole E Radde, Pavel Bashtrykov, Albert Jeltsch (2023) On the accuracy of the epigenetic copy machine: comprehensive specificity analysis of the DNMT1 DNA methyltransferase. Nucleic Acids Research, gkad465. doi: 10.1093/nar/gkad465.
A. Jeltsch, P. Bashtrykov, and C. Albrecht. Dataset, (2024)Related to: Albrecht, C.; Rajaram, N.; Broche, J.; Bashtrykov, P.; Jeltsch, A. Locus specific and stable DNA demethylation at the H19/IGF2 ICR1 by epigenome editing using a dCas9-SunTag system and the catalytic domain of TET1. Genes 2024, 15(1), 80. doi: 10.3390/genes15010080.
A. Jeltsch, P. Bashtrykov, A. Bröhm, M. Dukatz, and S. Adam. Dataset, (2021)Related to: Bröhm et al., Methylation of recombinant mononucleosomes by DNMT3A demonstrates efficient linker DNA methylation and a role of H3K36me3. Commun. Biol. 5(1):192, 2022. doi: 10.1038/s42003-022-03119-z.
A. Jeltsch, P. Bashtrykov, L. Dossmann, and M. Emperle. Dataset, (2024)Related to: Dossmann et al.: Specific DNMT3C flanking sequence preferences facilitate methylation of young murine retrotransposons. Submitted for publication.
A. Jeltsch, P. Bashtrykov, M. Dukatz, and S. Adam. Dataset, (2022)Related to: Dukatz et al.: "DNA methyltransferase DNMT3A forms interaction networks with the CpG site and flanking sequence elements for efficient methylation", Journal of Biological Chemistry, 2022. doi: 10.1016/j.jbc.2022.102462.
A. Jeltsch, P. Bashtrykov, and N. Rajaram. Dataset, (2023)Related to: Rajaram N, Kouroukli AG, Bens S, Bashtrykov P, Jeltsch A. (2023) Development of super-specific epigenome editing by targeted allele-specific DNA methylation. Epigenetics & Chromatin 16, 41. doi: 10.1186/s13072-023-00515-5.
C. Lohoff. Dataset, (2020)Related to: Lohoff C., Buchholz P. C. F., Le Roes-Hill M. & Pleiss J. (2020). The Expansin Engineering Database: a navigation and classification tool for expansins and homologues. Proteins: Structure, Function, and Bioinformatics 89:2. doi: 10.1002/prot.26001.
C. Lohoff. Dataset, (2020)Related to: Lohoff C., Buchholz P. C. F., Le Roes-Hill M. & Pleiss J. (2020). The Expansin Engineering Database: a navigation and classification tool for expansins and homologues. Proteins: Structure, Function, and Bioinformatics 89:2. doi: 10.1002/prot.26001.
A. Jeltsch, P. Schnee, and J. Pleiss. Software, (2022)Related to: Alexandra Mack, Max Emperle, Philipp Schnee, Sabrina Adam, Jürgen Pleiss, Pavel Bashtrykov, & Albert Jeltsch: Preferential interaction of DNMT3A subunits containing the R882H cancer mutation leads to dominant changes of flanking sequence effects. Submitted for publication.
C. Lohoff, and P. Buchholz. Dataset, (2020)Related to: Lohoff C., Buchholz P. C. F., Le Roes-Hill M. & Pleiss J. (2020). The Expansin Engineering Database: a navigation and classification tool for expansins and homologues. Proteins: Structure, Function, and Bioinformatics 89:2. doi: 10.1002/prot.26001.
C. Zeil, and P. Buchholz. Dataset, (2020)Related to: Maike Gräff, Patrick C. F. Buchholz, Marilize Le Roes-Hill & Jürgen Pleiss (2020): Multicopper oxidases: Modular structure, sequence space and evolutionary relationships. (submitted).
M. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: D’Errico, G., Ortona, O., Capuano, F., & Vitagliano, V. (2004). Diffusion Coefficients for the Binary System Glycerol + Water at 25 °C. A Velocity Correlation Study. Journal of Chemical & Engineering Data, 49(6), 1665-1670. doi: 10.1021/je049917u.
M. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: Gültig, M., Range, J. P., Schmitz, B., & Pleiss, J. (2022). Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML. Journal of Chemical & Engineering Data,. doi: 10.1021/acs.jced.2c00391.
M. Gültig, J. Range, B. Schmitz, and J. Pleiss. Software, (2022)Related to: Gültig, M., Range, J. P., Schmitz, B., & Pleiss, J. (2022). Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML. Journal of Chemical & Engineering Data,. doi: 10.1021/acs.jced.2c00391.
P. Buchholz. Dataset, (2021)Related to: Orlando M., Buchholz P. C. F., Lotti M. & Pleiss J. (2020). The GH19 Engineering Database: an extended classification system for exploring the properties of sequence space and protein evolution. (submitted).