Publications
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Luca Galbusera, Gwendoline Bellement-Theroue, Thomas Julou, & . (2025). Data for the figures of the article “Transient transcription factor depletions explain diverse single-cell responses of LexA target promoters to mild DNA damage” (v1 ed.) [dataset]. https://doi.org/10.5281/zenodo.15547214
Luca Galbusera, Gwendoline Bellement-Theroue, Thomas Julou, & . (2025). Data for the figures of the article “Transient transcription factor depletions explain diverse single-cell responses of LexA target promoters to mild DNA damage” (v1 ed.) [dataset]. https://doi.org/10.5281/zenodo.15547214
Galbusera, Luca, Bellement-Theroue, Gwendoline, Julou, Thomas, & . (2025). Rapid Transcription Factor Fluctuations Drive Nonequilibrium Gene Regulatory Dynamics in Bacteria [Journal-article]. PRX Life, 3(3). https://doi.org/10.1103/nvk3-jsvm
Galbusera, Luca, Bellement-Theroue, Gwendoline, Julou, Thomas, & . (2025). Rapid Transcription Factor Fluctuations Drive Nonequilibrium Gene Regulatory Dynamics in Bacteria [Journal-article]. PRX Life, 3(3). https://doi.org/10.1103/nvk3-jsvm
de Groot, Daan Hugo, Leonardo, Sarah X Morillo, Pachkov, Mikhail, & . (2025). Bonsai: Tree representations for distortion-free visualization and exploratory analysis of single-cell omics data [Posted-content]. In bioRxiv . Cold Spring Harbor Laboratory. https://doi.org/10.1101/2025.05.08.652944
de Groot, Daan Hugo, Leonardo, Sarah X Morillo, Pachkov, Mikhail, & . (2025). Bonsai: Tree representations for distortion-free visualization and exploratory analysis of single-cell omics data [Posted-content]. In bioRxiv . Cold Spring Harbor Laboratory. https://doi.org/10.1101/2025.05.08.652944
Julou, Thomas, Gervais, Théo, de Groot, Daan, & . (2025). Growth rate controls the sensitivity of gene regulatory circuits [Journal-article]. Science Advances, 11(17). https://doi.org/10.1126/sciadv.adu9279
Julou, Thomas, Gervais, Théo, de Groot, Daan, & . (2025). Growth rate controls the sensitivity of gene regulatory circuits [Journal-article]. Science Advances, 11(17). https://doi.org/10.1126/sciadv.adu9279
. (2025, April 16). Why is it so hard to analyze single-cell RNA-seq data? https://doi.org/10.5281/zenodo.15230282
. (2025, April 16). Why is it so hard to analyze single-cell RNA-seq data? https://doi.org/10.5281/zenodo.15230282
Galbusera, Luca, Bellement, Gwendoline, Julou, Thomas, & . (2024). Transient transcription factor depletions explain diverse single-cell responses of LexA target promoters to mild DNA damage [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2024.11.28.625836
Galbusera, Luca, Bellement, Gwendoline, Julou, Thomas, & . (2024). Transient transcription factor depletions explain diverse single-cell responses of LexA target promoters to mild DNA damage [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2024.11.28.625836
Grobecker, Pascal, Sakoparnig, Thomas, & . (2024). Identifying cell states in single-cell RNA-seq data at statistically maximal resolution [Journal-article]. PLOS Computational Biology, 20(7). https://doi.org/10.1371/journal.pcbi.1012224
Grobecker, Pascal, Sakoparnig, Thomas, & . (2024). Identifying cell states in single-cell RNA-seq data at statistically maximal resolution [Journal-article]. PLOS Computational Biology, 20(7). https://doi.org/10.1371/journal.pcbi.1012224
Bak, Maciej, , Kouzel, Ian U., Gur, Tamer, Schmidt, Ralf, Zavolan, Mihaela, & Gruber, Andreas J. (2024). MAPP unravels frequent co-regulation of splicing and polyadenylation by RNA-binding proteins and their dysregulation in cancer [Journal-article]. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-48046-1
Bak, Maciej, , Kouzel, Ian U., Gur, Tamer, Schmidt, Ralf, Zavolan, Mihaela, & Gruber, Andreas J. (2024). MAPP unravels frequent co-regulation of splicing and polyadenylation by RNA-binding proteins and their dysregulation in cancer [Journal-article]. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-48046-1
Bak, Maciej, , Kouzel, Ian U., Gur, Tamer, Schmidt, Ralf, Zavolan, Mihaela, & Gruber, Andreas J. (2024, March 21). Supplementary Results. https://doi.org/10.5281/zenodo.10849750
Bak, Maciej, , Kouzel, Ian U., Gur, Tamer, Schmidt, Ralf, Zavolan, Mihaela, & Gruber, Andreas J. (2024, March 21). Supplementary Results. https://doi.org/10.5281/zenodo.10849750
Bak, Maciej, , Kouzel, Ian U., Gur, Tamer, Schmidt, Ralf, Zavolan, Mihaela, & Gruber, Andreas J. (2024). MAPP. Nature Communications, 15(1). https://doi.org/10.5281/zenodo.10845501
Bak, Maciej, , Kouzel, Ian U., Gur, Tamer, Schmidt, Ralf, Zavolan, Mihaela, & Gruber, Andreas J. (2024). MAPP. Nature Communications, 15(1). https://doi.org/10.5281/zenodo.10845501
Gervais, Théo, Kscheschinski, Bjoern, Mell, Michael, Goepfert, Nevil, , & Julou, Thomas. (2024). E. coli prepares for starvation by dramatically remodeling its proteome in the first hours after loss of nutrients [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2024.02.29.582700
Gervais, Théo, Kscheschinski, Bjoern, Mell, Michael, Goepfert, Nevil, , & Julou, Thomas. (2024). E. coli prepares for starvation by dramatically remodeling its proteome in the first hours after loss of nutrients [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2024.02.29.582700
Sollier, Julie, Basler, Marek, Broz, Petr, Dittrich, Petra S., Drescher, Knut, Egli, Adrian, Harms, Alexander, Hierlemann, Andreas, Hiller, Sebastian, King, Carolyn G., McKinney, John D., Moran-Gilad, Jacob, Neher, Richard A., Page, Malcolm G. P., Panke, Sven, Persat, Alexandre, Picotti, Paola, Rentsch, Katharina M., Rivera-Fuentes, Pablo, et al. (2024). Revitalizing antibiotic discovery and development through in vitro modelling of in-patient conditions. Nature Microbiology, 9(1), 1–3. https://doi.org/10.1038/s41564-023-01566-w
Sollier, Julie, Basler, Marek, Broz, Petr, Dittrich, Petra S., Drescher, Knut, Egli, Adrian, Harms, Alexander, Hierlemann, Andreas, Hiller, Sebastian, King, Carolyn G., McKinney, John D., Moran-Gilad, Jacob, Neher, Richard A., Page, Malcolm G. P., Panke, Sven, Persat, Alexandre, Picotti, Paola, Rentsch, Katharina M., Rivera-Fuentes, Pablo, et al. (2024). Revitalizing antibiotic discovery and development through in vitro modelling of in-patient conditions. Nature Microbiology, 9(1), 1–3. https://doi.org/10.1038/s41564-023-01566-w
Grobecker, P., & van Nimwegen, E. (2023). Identifying cell states in single-cell RNA-seq data at statistically maximal resolution [Posted-content]. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2023.10.31.564980
Grobecker, P., & van Nimwegen, E. (2023). Identifying cell states in single-cell RNA-seq data at statistically maximal resolution [Posted-content]. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2023.10.31.564980
de Groot, Daan H., Tjalma, Age J., Bruggeman, Frank J., & . (2023). Effective bet-hedging through growth rate dependent stability. Proceedings of the National Academy of Sciences of the United States of America, 120(8), e2211091120. https://doi.org/10.1073/pnas.2211091120
de Groot, Daan H., Tjalma, Age J., Bruggeman, Frank J., & . (2023). Effective bet-hedging through growth rate dependent stability. Proceedings of the National Academy of Sciences of the United States of America, 120(8), e2211091120. https://doi.org/10.1073/pnas.2211091120
Katsantoni, Maria, , & Zavolan, Mihaela. (2023). Improved analysis of (e)CLIP data with RCRUNCH yields a compendium of RNA-binding protein binding sites and motifs. Genome Biology, 24(1), 77. https://doi.org/10.1186/s13059-023-02913-0
Katsantoni, Maria, , & Zavolan, Mihaela. (2023). Improved analysis of (e)CLIP data with RCRUNCH yields a compendium of RNA-binding protein binding sites and motifs. Genome Biology, 24(1), 77. https://doi.org/10.1186/s13059-023-02913-0
Grison, Alice, Karimaddini, Zahra, Breda, Jeremie, Mukhtar, Tanzila, Boareto, Marcelo, Eschbach, Katja, Beisel, Christian, Iber, Dagmar, , Taylor, Verdon, & Atanasoski, Suzana. (2022). The protooncogene Ski regulates the neuron-glia switch during development of the mammalian cerebral cortex [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.12.16.520470
Grison, Alice, Karimaddini, Zahra, Breda, Jeremie, Mukhtar, Tanzila, Boareto, Marcelo, Eschbach, Katja, Beisel, Christian, Iber, Dagmar, , Taylor, Verdon, & Atanasoski, Suzana. (2022). The protooncogene Ski regulates the neuron-glia switch during development of the mammalian cerebral cortex [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.12.16.520470
Kruglyak, L., Beyer, A., Bloom, J. S., Grossbach, J., Lieberman, T. D., Mancuso, C. P., Rich, M. S., Sherlock, G., van Nimwegen, E., & Kaplan, C. D. (2022). No evidence that synonymous mutations in yeast genes are mostly deleterious. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.07.14.500130
Kruglyak, L., Beyer, A., Bloom, J. S., Grossbach, J., Lieberman, T. D., Mancuso, C. P., Rich, M. S., Sherlock, G., van Nimwegen, E., & Kaplan, C. D. (2022). No evidence that synonymous mutations in yeast genes are mostly deleterious. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.07.14.500130
de Groot, D. H., Tjalma, A. J., Bruggeman, F. J., & van Nimwegen, E. (2022). Coupling phenotype stability to growth rate overcomes limitations of bet-hedging strategies [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.04.12.488059
de Groot, D. H., Tjalma, A. J., Bruggeman, F. J., & van Nimwegen, E. (2022). Coupling phenotype stability to growth rate overcomes limitations of bet-hedging strategies [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.04.12.488059
Julou, T., Gervais, T., & van Nimwegen, E. (2022). Growth rate controls the sensitivity of gene regulatory circuits [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.04.03.486858
Julou, T., Gervais, T., & van Nimwegen, E. (2022). Growth rate controls the sensitivity of gene regulatory circuits [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.04.03.486858
Baranasic, Damir, Hörtenhuber, Matthias, Balwierz, Piotr J., Zehnder, Tobias, Mukarram, Abdul Kadir, Nepal, Chirag, Várnai, Csilla, Hadzhiev, Yavor, Jimenez-Gonzalez, Ada, Li, Nan, Wragg, Joseph, D’Orazio, Fabio M., Relic, Dorde, Pachkov, Mikhail, Díaz, Noelia, Hernández-Rodríguez, Benjamín, Chen, Zelin, Stoiber, Marcus, Dong, Michaël, et al. (2022). Multiomic atlas with functional stratification and developmental dynamics of zebrafish cis-regulatory elements. Nature Genetics, 54(7), 1037–1050. https://doi.org/10.1038/s41588-022-01089-w
Baranasic, Damir, Hörtenhuber, Matthias, Balwierz, Piotr J., Zehnder, Tobias, Mukarram, Abdul Kadir, Nepal, Chirag, Várnai, Csilla, Hadzhiev, Yavor, Jimenez-Gonzalez, Ada, Li, Nan, Wragg, Joseph, D’Orazio, Fabio M., Relic, Dorde, Pachkov, Mikhail, Díaz, Noelia, Hernández-Rodríguez, Benjamín, Chen, Zelin, Stoiber, Marcus, Dong, Michaël, et al. (2022). Multiomic atlas with functional stratification and developmental dynamics of zebrafish cis-regulatory elements. Nature Genetics, 54(7), 1037–1050. https://doi.org/10.1038/s41588-022-01089-w
Mukhtar, Tanzila, Breda, Jeremie, Adam, Manal A., Boareto, Marcelo, Grobecker, Pascal, Karimaddini, Zahra, Grison, Alice, Eschbach, Katja, Chandrasekhar, Ramakrishnan, Vermeul, Swen, Okoniewski, Michal, Pachkov, Mikhail, Harwell, Corey C., Atanasoski, Suzana, Beisel, Christian, Iber, Dagmar, , & Taylor, Verdon. (2022). Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis. The EMBO Journal, 41(24), e111132. https://doi.org/10.15252/embj.2022111132
Mukhtar, Tanzila, Breda, Jeremie, Adam, Manal A., Boareto, Marcelo, Grobecker, Pascal, Karimaddini, Zahra, Grison, Alice, Eschbach, Katja, Chandrasekhar, Ramakrishnan, Vermeul, Swen, Okoniewski, Michal, Pachkov, Mikhail, Harwell, Corey C., Atanasoski, Suzana, Beisel, Christian, Iber, Dagmar, , & Taylor, Verdon. (2022). Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis. The EMBO Journal, 41(24), e111132. https://doi.org/10.15252/embj.2022111132
Bloom, Jesse D., Chan, Yujia Alina, Baric, Ralph S., Bjorkman, Pamela J., Cobey, Sarah, Deverman, Benjamin E., Fisman, David N., Gupta, Ravindra, Iwasaki, Akiko, Lipsitch, Marc, Medzhitov, Ruslan, Neher, Richard A., Nielsen, Rasmus, Patterson, Nick, Stearns, Tim, , Worobey, Michael, & Relman, David A. (2021). Investigate the origins of COVID-19. Science, 372(6543), 694. https://doi.org/10.1126/science.abj0016
Bloom, Jesse D., Chan, Yujia Alina, Baric, Ralph S., Bjorkman, Pamela J., Cobey, Sarah, Deverman, Benjamin E., Fisman, David N., Gupta, Ravindra, Iwasaki, Akiko, Lipsitch, Marc, Medzhitov, Ruslan, Neher, Richard A., Nielsen, Rasmus, Patterson, Nick, Stearns, Tim, , Worobey, Michael, & Relman, David A. (2021). Investigate the origins of COVID-19. Science, 372(6543), 694. https://doi.org/10.1126/science.abj0016
Breda, Jérémie, Zavolan, Mihaela, & . (2021). Bayesian inference of gene expression states from single-cell RNA-seq data. Nature Biotechnology, 39(8), 1008–1016. https://doi.org/10.1038/s41587-021-00875-x
Breda, Jérémie, Zavolan, Mihaela, & . (2021). Bayesian inference of gene expression states from single-cell RNA-seq data. Nature Biotechnology, 39(8), 1008–1016. https://doi.org/10.1038/s41587-021-00875-x
Sakoparnig, Thomas, Field, Chris, & . (2021). Whole genome phylogenies reflect the distributions of recombination rates for many bacterial species. eLife, 10, e65366. https://doi.org/10.7554/elife.65366
Sakoparnig, Thomas, Field, Chris, & . (2021). Whole genome phylogenies reflect the distributions of recombination rates for many bacterial species. eLife, 10, e65366. https://doi.org/10.7554/elife.65366
Urchueguía, Arantxa, Galbusera, Luca, Chauvin, Dany, Bellement, Gwendoline, Julou, Thomas, & . (2021). Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network. PLoS Biology, 19(12), e3001491. https://doi.org/10.1371/journal.pbio.3001491
Urchueguía, Arantxa, Galbusera, Luca, Chauvin, Dany, Bellement, Gwendoline, Julou, Thomas, & . (2021). Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network. PLoS Biology, 19(12), e3001491. https://doi.org/10.1371/journal.pbio.3001491
Galbusera, Luca, Bellement-Theroue, Gwendoline, Urchueguia, Arantxa, Julou, Thomas, & . (2020). Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria. PLoS ONE, 15(10), e0240233. https://doi.org/10.1371/journal.pone.0240233
Galbusera, Luca, Bellement-Theroue, Gwendoline, Urchueguia, Arantxa, Julou, Thomas, & . (2020). Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria. PLoS ONE, 15(10), e0240233. https://doi.org/10.1371/journal.pone.0240233
Julou, Thomas, & . (2020). Single-cell data on lac operon induction by lactose in E. coli (Julou, Thomas; ;, Ed.) [dataset]. https://doi.org/10.5281/zenodo.3894719
Julou, Thomas, & . (2020). Single-cell data on lac operon induction by lactose in E. coli (Julou, Thomas; ;, Ed.) [dataset]. https://doi.org/10.5281/zenodo.3894719
Julou, Thomas, Zweifel, Ludovit, Blank, Diana, Fiori, Athos, & . (2020). Subpopulations of sensorless bacteria drive fitness in fluctuating environments. PLoS Biology, 18(12), e3000952. https://doi.org/10.1371/journal.pbio.3000952
Julou, Thomas, Zweifel, Ludovit, Blank, Diana, Fiori, Athos, & . (2020). Subpopulations of sensorless bacteria drive fitness in fluctuating environments. PLoS Biology, 18(12), e3000952. https://doi.org/10.1371/journal.pbio.3000952
Mukhtar, Tanzila, Breda, Jeremie, Grison, Alice, Karimaddini, Zahra, Grobecker, Pascal, Iber, Dagmar, Beisel, Christian, , & Taylor, Verdon. (2020). Tead transcription factors differentially regulate cortical development. Scientific Reports, 10(1), 4625. https://doi.org/10.1038/s41598-020-61490-5
Mukhtar, Tanzila, Breda, Jeremie, Grison, Alice, Karimaddini, Zahra, Grobecker, Pascal, Iber, Dagmar, Beisel, Christian, , & Taylor, Verdon. (2020). Tead transcription factors differentially regulate cortical development. Scientific Reports, 10(1), 4625. https://doi.org/10.1038/s41598-020-61490-5
Witz, Guillaume, Julou, Thomas, & . (2020). Response to comment on textquoteleftInitiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanismtextquoteright. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.08.04.227694
Witz, Guillaume, Julou, Thomas, & . (2020). Response to comment on textquoteleftInitiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanismtextquoteright. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.08.04.227694
Urchueguía, A., Galbusera, L., Bellement, G., Julou, T., & Nimwegen, E. v. (2019). Noise propagation shapes condition-dependent gene expression noise in Escherichia coli [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/795369
Urchueguía, A., Galbusera, L., Bellement, G., Julou, T., & Nimwegen, E. v. (2019). Noise propagation shapes condition-dependent gene expression noise in Escherichia coli [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/795369
Galbusera, L., Bellement-Theroue, G., Urchueguia, A., Julou, T., & Nimwegen, E. v. (2019). Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/793976
Galbusera, L., Bellement-Theroue, G., Urchueguia, A., Julou, T., & Nimwegen, E. v. (2019). Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/793976
Berger, Severin, Pachkov, Mikhail, Arnold, Phil, Omidi, Saeed, Kelley, Nicholas, Salatino, Silvia, & . (2019). Crunch: integrated processing and modeling of ChIP-seq data in terms of regulatory motifs. Genome Research, 29(7), 1164–1177. https://doi.org/10.1101/gr.239319.118
Berger, Severin, Pachkov, Mikhail, Arnold, Phil, Omidi, Saeed, Kelley, Nicholas, Salatino, Silvia, & . (2019). Crunch: integrated processing and modeling of ChIP-seq data in terms of regulatory motifs. Genome Research, 29(7), 1164–1177. https://doi.org/10.1101/gr.239319.118
Witz, Guillaume, , & Julou, Thomas. (2019). Initiation of chromosome replication controls both division and replication cycles in; E. coli; through a double-adder mechanism. eLife, 8, e48063. https://doi.org/10.7554/elife.48063
Witz, Guillaume, , & Julou, Thomas. (2019). Initiation of chromosome replication controls both division and replication cycles in; E. coli; through a double-adder mechanism. eLife, 8, e48063. https://doi.org/10.7554/elife.48063
Gruber, Andreas J., Schmidt, Ralf, Ghosh, Souvik, Martin, Georges, Gruber, Andreas R., , & Zavolan, Mihaela. (2018). Discovery of physiological and cancer-related regulators of 3′ UTR processing with KAPAC. Genome Biology, 19(1), 44. https://doi.org/10.1186/s13059-018-1415-3
Gruber, Andreas J., Schmidt, Ralf, Ghosh, Souvik, Martin, Georges, Gruber, Andreas R., , & Zavolan, Mihaela. (2018). Discovery of physiological and cancer-related regulators of 3′ UTR processing with KAPAC. Genome Biology, 19(1), 44. https://doi.org/10.1186/s13059-018-1415-3
Kaiser, Matthias, Jug, Florian, Julou, Thomas, Deshpande, Siddharth, Pfohl, Thomas, Silander, Olin K., Myers, Gene, & . (2018). Monitoring single-cell gene regulation under dynamically controllable conditions with integrated microfluidics and software. Nature Communications, 9(1), 212. https://doi.org/10.1038/s41467-017-02505-0
Kaiser, Matthias, Jug, Florian, Julou, Thomas, Deshpande, Siddharth, Pfohl, Thomas, Silander, Olin K., Myers, Gene, & . (2018). Monitoring single-cell gene regulation under dynamically controllable conditions with integrated microfluidics and software. Nature Communications, 9(1), 212. https://doi.org/10.1038/s41467-017-02505-0
Rzepiela, Andrzej J., Ghosh, Souvik, Breda, Jeremie, Vina-Vilaseca, Arnau, Syed, Afzal P., Gruber, Andreas J., Eschbach, Katja, Beisel, Christian, , & Zavolan, Mihaela. (2018). Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction. Molecular Systems Biology, 14(8), e8266. https://doi.org/10.15252/msb.20188266
Rzepiela, Andrzej J., Ghosh, Souvik, Breda, Jeremie, Vina-Vilaseca, Arnau, Syed, Afzal P., Gruber, Andreas J., Eschbach, Katja, Beisel, Christian, , & Zavolan, Mihaela. (2018). Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction. Molecular Systems Biology, 14(8), e8266. https://doi.org/10.15252/msb.20188266
Kaiser, Matthias, Jug, Florian, Julou, Thomas, Deshpande, Siddharth, Pfohl, Thomas, Silander, Olin, Myers, Gene, & . (2017). Analysis of lac operon induction with single cell resolution using the DIMM microfluidics chip and the MoMA software (Kaiser, Matthias; Jug, Florian; Julou, Thomas; Deshpande, Siddharth; Pfohl, Thomas; Silander, Olin; Myers, Gene; van Nimwegen, Erik, Ed.) [dataset]. https://doi.org/10.5281/zenodo.746230
Kaiser, Matthias, Jug, Florian, Julou, Thomas, Deshpande, Siddharth, Pfohl, Thomas, Silander, Olin, Myers, Gene, & . (2017). Analysis of lac operon induction with single cell resolution using the DIMM microfluidics chip and the MoMA software (Kaiser, Matthias; Jug, Florian; Julou, Thomas; Deshpande, Siddharth; Pfohl, Thomas; Silander, Olin; Myers, Gene; van Nimwegen, Erik, Ed.) [dataset]. https://doi.org/10.5281/zenodo.746230
Omidi, Saeed, Zavolan, Mihaela, Pachkov, Mikhail, Breda, Jeremie, Berger, Severin, & . (2017). Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors. PLoS Computational Biology, 13(7), e1005176. https://doi.org/10.1371/journal.pcbi.1005176
Omidi, Saeed, Zavolan, Mihaela, Pachkov, Mikhail, Breda, Jeremie, Berger, Severin, & . (2017). Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors. PLoS Computational Biology, 13(7), e1005176. https://doi.org/10.1371/journal.pcbi.1005176
Artimo, Panu, Duvaud, Séverine, Pachkov, Mikhail, Ioannidis, Vassilios, , & Stockinger, Heinz. (2016). The ISMARA client. F1000Research, 5(Elixir), 2851. https://doi.org/10.12688/f1000research.9794.1
Artimo, Panu, Duvaud, Séverine, Pachkov, Mikhail, Ioannidis, Vassilios, , & Stockinger, Heinz. (2016). The ISMARA client. F1000Research, 5(Elixir), 2851. https://doi.org/10.12688/f1000research.9794.1
Berger, Severin, Omidi, Saeed, Pachkov, Mikhail, Arnold, Phil, Kelley, Nicholas, Salatino, Silvia, & . (2016). Crunch: Completely Automated Analysis of ChIP-seq Data. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/042903
Berger, Severin, Omidi, Saeed, Pachkov, Mikhail, Arnold, Phil, Kelley, Nicholas, Salatino, Silvia, & . (2016). Crunch: Completely Automated Analysis of ChIP-seq Data. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/042903
Salatino, Silvia, Kupr, Barbara, Baresic, Mario, , & Handschin, Christoph. (2016). The Genomic Context and Corecruitment of SP1 Affect ERRα Coactivation by PGC-1α in Muscle Cells. Molecular Endocrinology, 30(7), 809–825. https://doi.org/10.1210/me.2016-1036
Salatino, Silvia, Kupr, Barbara, Baresic, Mario, , & Handschin, Christoph. (2016). The Genomic Context and Corecruitment of SP1 Affect ERRα Coactivation by PGC-1α in Muscle Cells. Molecular Endocrinology, 30(7), 809–825. https://doi.org/10.1210/me.2016-1036
. (2016). Inferring Contacting Residues within and between Proteins: What Do the Probabilities Mean? PLoS Computational Biology, 12(5), e1004726. https://doi.org/10.1371/journal.pcbi.1004726
. (2016). Inferring Contacting Residues within and between Proteins: What Do the Probabilities Mean? PLoS Computational Biology, 12(5), e1004726. https://doi.org/10.1371/journal.pcbi.1004726
Breda, Jeremie, Rzepiela, Andrzej J, Gumienny, Rafal, , & Zavolan, Mihaela. (2015). Quantifying the strength of miRNA-target interactions. Methods, 85, 9–90. https://doi.org/10.1016/j.ymeth.2015.04.012
Breda, Jeremie, Rzepiela, Andrzej J, Gumienny, Rafal, , & Zavolan, Mihaela. (2015). Quantifying the strength of miRNA-target interactions. Methods, 85, 9–90. https://doi.org/10.1016/j.ymeth.2015.04.012
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