Bioinformatics (van Nimwegen)

de Groot, Daan Hugo et al. (2025) ‘Bonsai: Tree representations for distortion-free visualization and exploratory analysis of single-cell omics data’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv ). Available at: https://doi.org/10.1101/2025.05.08.652944.

Julou, Thomas et al. (2025) ‘Growth rate controls the sensitivity of gene regulatory circuits’, Science Advances, 11(17). Available at: https://doi.org/10.1126/sciadv.adu9279.

Alaball Pujol, M.-E. (2025) Quantifying bacterial responses to antibiotics at the single-cell level. Doctoral Thesis.

Galbusera, Luca et al. (2024) ‘Transient transcription factor depletions explain diverse single-cell responses of LexA target promoters to mild DNA damage’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv). Available at: https://doi.org/10.1101/2024.11.28.625836.

Grobecker, Pascal, Sakoparnig, Thomas and van Nimwegen, Erik (2024) ‘Identifying cell states in single-cell RNA-seq data at statistically maximal resolution’, PLOS Computational Biology, 20(7). Available at: https://doi.org/10.1371/journal.pcbi.1012224.

Bak, Maciej et al. (2024) ‘MAPP unravels frequent co-regulation of splicing and polyadenylation by RNA-binding proteins and their dysregulation in cancer’, Nature Communications, 15(1). Available at: https://doi.org/10.1038/s41467-024-48046-1.

Bak, Maciej et al. (2024) ‘Supplementary Results’. Available at: https://doi.org/10.5281/zenodo.10849750.

Bak, Maciej et al. (2024) ‘MAPP’, Nature Communications, 15(1). Available at: https://doi.org/10.5281/zenodo.10845501.

Gervais, Théo et al. (2024) ‘E. coli prepares for starvation by dramatically remodeling its proteome in the first hours after loss of nutrients’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv). Available at: https://doi.org/10.1101/2024.02.29.582700.

Banerjee, A. (2024) Role of Rpl39l in translation, and consequences for pluripotency and cancer. Doctoral Thesis.

Gervais, T. (2024) Bacterial gene expression dynamics in the regime of vanishing growth. Doctoral Thesis.

Sollier, Julie et al. (2024) ‘Revitalizing antibiotic discovery and development through in vitro modelling of in-patient conditions’, Nature Microbiology, 9(1), pp. 1–3. Available at: https://doi.org/10.1038/s41564-023-01566-w.

Grobecker, P. and van Nimwegen, E. (2023) ‘Identifying cell states in single-cell RNA-seq data at statistically maximal resolution’. Cold Spring Harbor Laboratory. Available at: https://doi.org/10.1101/2023.10.31.564980.

de Groot, Daan H. et al. (2023) ‘Effective bet-hedging through growth rate dependent stability’, Proceedings of the National Academy of Sciences of the United States of America, 120(8), p. e2211091120. Available at: https://doi.org/10.1073/pnas.2211091120.

Katsantoni, Maria, van Nimwegen, Erik and 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), p. 77. Available at: https://doi.org/10.1186/s13059-023-02913-0.

Relić, Đ. (2023) Modelling gene expression in terms of DNA sequence. Doctoral Thesis.

Grison, Alice et al. (2022) ‘The protooncogene Ski regulates the neuron-glia switch during development of the mammalian cerebral cortex’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv). Available at: https://doi.org/10.1101/2022.12.16.520470.

Kruglyak, L. et al. (2022) ‘No evidence that synonymous mutations in yeast genes are mostly deleterious’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv). Available at: https://doi.org/10.1101/2022.07.14.500130.

de Groot, D.H. et al. (2022) ‘Coupling phenotype stability to growth rate overcomes limitations of bet-hedging strategies’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv). Available at: https://doi.org/10.1101/2022.04.12.488059.

Julou, T., Gervais, T. and van Nimwegen, E. (2022) ‘Growth rate controls the sensitivity of gene regulatory circuits’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv). Available at: https://doi.org/10.1101/2022.04.03.486858.

Baranasic, Damir et al. (2022) ‘Multiomic atlas with functional stratification and developmental dynamics of zebrafish cis-regulatory elements’, Nature Genetics, 54(7), pp. 1037–1050. Available at: https://doi.org/10.1038/s41588-022-01089-w.

Kraemer, A.I. (2022) Genome-wide Prediction of Regulators Shaping Chromatin State and Gene Expression. Doctoral Thesis.

Mukhtar, Tanzila et al. (2022) ‘Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis’, The EMBO Journal, 41(24), p. e111132. Available at: https://doi.org/10.15252/embj.2022111132.

Bak, M. (2021) Computational analyses of RNA-Sequencing data to identify splicing and polyadenylation regulatory elements. Doctoral Thesis.

Bloom, Jesse D. et al. (2021) ‘Investigate the origins of COVID-19’, Science, 372(6543), pp. 694–694. Available at: https://doi.org/10.1126/science.abj0016.

Breda, Jérémie, Zavolan, Mihaela and van Nimwegen, Erik (2021) ‘Bayesian inference of gene expression states from single-cell RNA-seq data’, Nature Biotechnology, 39(8), pp. 1008–1016. Available at: https://doi.org/10.1038/s41587-021-00875-x.

Fiori, A. (2021) Stochastic gene expression and lag time in bacteria. Doctoral Thesis.

Grobecker, P. (2021) Bayesian methods in transcriptomics. Doctoral Thesis.

Meeuse, M.W.M. (2021) Functional dissection of a gene expression oscillator in C. elegans. Doctoral Thesis.

Sakoparnig, Thomas, Field, Chris and van Nimwegen, Erik (2021) ‘Whole genome phylogenies reflect the distributions of recombination rates for many bacterial species’, eLife, 10, p. e65366. Available at: https://doi.org/10.7554/elife.65366.

Urchueguía, Arantxa et al. (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), p. e3001491. Available at: https://doi.org/10.1371/journal.pbio.3001491.

Breda, J. (2020) Model-driven analysis of gene expression control. Doctoral Thesis.

Galbusera, Luca et al. (2020) ‘Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria’, PLoS ONE, 15(10), p. e0240233. Available at: https://doi.org/10.1371/journal.pone.0240233.

Julou, Thomas and vanNimwegen, Erik (2020) ‘Single-cell data on lac operon induction by lactose in E. coli’. Edited by Julou, Thomas; vanNimwegen, Erik; Available at: https://doi.org/10.5281/zenodo.3894719.

Julou, Thomas et al. (2020) ‘Subpopulations of sensorless bacteria drive fitness in fluctuating environments’, PLoS biology, 18(12), p. e3000952. Available at: https://doi.org/10.1371/journal.pbio.3000952.

Krämer, Anne (2020) Genome-wide Prediction of Regulators shaping Chromatin State and Gene Expression. Dissertation. Universität Basel.

Luca, G. (2020) Non equilibrium dynamics in Escherichia coli’s gene regulatory network. Doctoral Thesis.

Mukhtar, Tanzila et al. (2020) ‘Tead transcription factors differentially regulate cortical development’, Scientific Reports, 10(1), p. 4625. Available at: https://doi.org/10.1038/s41598-020-61490-5.

Witz, Guillaume, Julou, Thomas and an Nimwegen, Erik (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. Available at: https://doi.org/10.1101/2020.08.04.227694.

Urchueguía, A. et al. (2019) ‘Noise propagation shapes condition-dependent gene expression noise in Escherichia coli’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv). Available at: https://doi.org/10.1101/795369.

Galbusera, L. et al. (2019) ‘Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv). Available at: https://doi.org/10.1101/793976.

Berger, Severin et al. (2019) ‘Crunch: integrated processing and modeling of ChIP-seq data in terms of regulatory motifs’, Genome Research, 29(7), pp. 1164–1177. Available at: https://doi.org/10.1101/gr.239319.118.

Urchueguía-Fornes, A. (2019) Noise propagation in ‘Escherichia coli’s’ regulatory network. Doctoral Thesis. Available at: https://doi.org/10.5451/unibas-007213893.

Witz, Guillaume, van Nimwegen, Erik and Julou, Thomas (2019) ‘Initiation of chromosome replication controls both division and replication cycles in; E. coli; through a double-adder mechanism’, eLife, 8, p. e48063. Available at: https://doi.org/10.7554/elife.48063.

Gruber, Andreas J. et al. (2018) ‘Discovery of physiological and cancer-related regulators of 3′ UTR processing with KAPAC’, Genome biology, 19(1), p. 44. Available at: https://doi.org/10.1186/s13059-018-1415-3.

Kaiser, Matthias et al. (2018) ‘Monitoring single-cell gene regulation under dynamically controllable conditions with integrated microfluidics and software’, Nature Communications, 9(1), p. 212. Available at: https://doi.org/10.1038/s41467-017-02505-0.

Rzepiela, Andrzej J. et al. (2018) ‘Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction’, Molecular systems biology, 14(8), p. e8266. Available at: https://doi.org/10.15252/msb.20188266.

Kaiser, Matthias et al. (2017) ‘Analysis of lac operon induction with single cell resolution using the DIMM microfluidics chip and the MoMA software’. Edited by Kaiser, Matthias; Jug, Florian; Julou, Thomas; Deshpande, Siddharth; Pfohl, Thomas; Silander, Olin; Myers, Gene; van Nimwegen, Erik. Available at: https://doi.org/10.5281/zenodo.746230.

Omidi, Saeed et al. (2017) ‘Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors’, PLoS Computational Biology, 13(7), p. e1005176. Available at: https://doi.org/10.1371/journal.pcbi.1005176.

Artimo, Panu et al. (2016) ‘The ISMARA client’, F1000Research, 5(Elixir), p. 2851. Available at: https://doi.org/10.12688/f1000research.9794.1.

Berger, Severin et al. (2016) ‘Crunch: Completely Automated Analysis of ChIP-seq Data’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv). Available at: https://doi.org/10.1101/042903 .

Kaiser, M. (2016) A microfluidic setup for quantifying single-cell transcription regulatory dynamics. Doctoral Thesis. Available at: https://doi.org/10.5451/unibas-006808335.

Salatino, Silvia et al. (2016) ‘The Genomic Context and Corecruitment of SP1 Affect ERRα Coactivation by PGC-1α in Muscle Cells’, Molecular Endocrinology, 30(7), pp. 809–825. Available at: https://doi.org/10.1210/me.2016-1036.

Swiss Institute of Bioinformatics Members, SIB and Swiss Institute of Bioinformatics Members, SIB (2016) ‘The SIB Swiss Institute of Bioinformatics” resources: focus on curated databases’, Nucleic acids research, 44(D1), pp. D27–D37. Available at: https://doi.org/10.1093/nar/gkv1310.

van Nimwegen, Erik (2016) ‘Inferring Contacting Residues within and between Proteins: What Do the Probabilities Mean?’, PLoS Computational Biology, 12(5), p. e1004726. Available at: https://doi.org/10.1371/journal.pcbi.1004726.

Breda, Jeremie et al. (2015) ‘Quantifying the strength of miRNA-target interactions’, Methods, 85, pp. 90–9. Available at: https://doi.org/10.1016/j.ymeth.2015.04.012.

Klishami, S.O. (2015) Computational methods for dissecting transcription regulatory networks. Doctoral Thesis. Available at: https://doi.org/10.5451/unibas-006419033.

Pemberton-Ross, Peter J, Pachkov, Mikhail and van Nimwegen, Erik (2015) ‘ARMADA: Using motif activity dynamics to infer gene regulatory networks from gene expression data’, Methods, 85, pp. 62–74. Available at: https://doi.org/10.1016/j.ymeth.2015.06.024.

Schertel, Claus et al. (2015) ‘A large-scale, in vivo transcription factor screen defines bivalent chromatin as a key property of regulatory factors mediating Drosophila wing development’, Genome research, 25(4), pp. 514–523. Available at: https://doi.org/10.1101/gr.181305.114.

Wolf, Luise, Silander, Olin K and van Nimwegen, Erik (2015) ‘Expression noise facilitates the evolution of gene regulation’, eLife, 4(4), p. e05856. Available at: https://doi.org/10.7554/elife.05856.

Wolf, L., Silander, O.K. and van Nimwegen, E.J. (2014) ‘Expression noise facilitates the evolution of gene regulation’. Cold Spring Harbor Laboratory. Available at: https://doi.org/10.1101/007237.

Balwierz, Piotr J et al. (2014) ‘ISMARA: Automated modeling of genomic signals as a democracy of regulatory motifs’, Genome research, 24(5), pp. 869–84. Available at: https://doi.org/10.1101/gr.169508.113.

Baresic, Mario et al. (2014) ‘Transcriptional network analysis in muscle reveals AP-1 as a partner of PGC-1α in the regulation of the hypoxic gene program’, Molecular and cellular biology, 34(16), pp. 2996–3012. Available at: https://doi.org/10.1128/mcb.01710-13.

Bertels, Frederic et al. (2014) ‘Automated reconstruction of whole genome phylogenies from short sequence reads’, Molecular biology and evolution, 31(5), pp. 1077–88. Available at: https://doi.org/10.1093/molbev/msu088.

Blank, Diana et al. (2014) ‘The predictability of molecular evolution during functional innovation’, Proceedings of the National Academy of Sciences, 111(8), pp. 3044–3049. Available at: https://doi.org/10.1073/pnas.1318797111.

Diepenbruck, Maren et al. (2014) ‘Tead2 expression levels control the subcellular distribution of Yap and Taz, zyxin expression and epithelial-mesenchymal transition’, Journal of Cell Science, 127(Pt 7), pp. 1523–36. Available at: https://doi.org/10.1242/jcs.139865.

Dill, Michael T. et al. (2014) ‘Pegylated IFN-α regulates hepatic gene expression through transient Jak/STAT activation’, Journal of Clinical Investigation, 124(4), pp. 1568–81. Available at: https://doi.org/10.1172/jci70408.

Dill, M.T. et al. (2014) ‘O47 PEGYLATED INTERFERON-ALPHA INDUCES SUSTAINED TRANSCRIPTIONAL RESPONSE IN LIVER INFILTRATING IMMUNE CELLS BUT NOT IN HEPATOCYTES IN THE LIVER OF PATIENTS WITH CHRONIC HEPATITIS C’, in Journal of Hepatology. London (Journal of Hepatology), pp. S1–S22. Available at: https://doi.org/10.1016/s0168-8278(14)60049-0.

Forrest, Alistair R. R. et al. (2014) ‘A promoter-level mammalian expression atlas’, Nature, 507(7493), pp. 462–470. Available at: https://doi.org/10.1038/nature13182.

Gruber, Andreas J. et al. (2014) ‘Embryonic stem cell-specific microRNAs contribute to pluripotency by inhibiting regulators of multiple differentiation pathways’, Nucleic Acids Research, 42(14), pp. 9313–26. Available at: https://doi.org/10.1093/nar/gku544.

Gruber, Andreas R. et al. (2014) ‘Global 3′ UTR shortening has a limited effect on protein abundance in proliferating T cells’, Nature Communications, 5(5465), p. 5465. Available at: https://doi.org/10.1038/ncomms6465.

Luisier, R. (2014) Towards a better understanding of early drug-induced regulatory mechanisms of liver tumorigenesis. Doctoral Thesis. Available at: https://doi.org/10.5451/unibas-006266379.

Luisier, Raphaëlle et al. (2014) ‘Computational modeling identifies key gene regulatory interactions underlying phenobarbital-mediated tumor promotion’, Nucleic Acids Research, 42(7), pp. 4180–95. Available at: https://doi.org/10.1093/nar/gkt1415.

Morikawa, Hiromasa et al. (2014) ‘Differential roles of epigenetic changes and Foxp3 expression in regulatory T cell-specific transcriptional regulation’, Proceedings of the National Academy of Sciences of the United States of America, 111(14), pp. 5289–94. Available at: https://doi.org/10.1073/pnas.1312717110.

Stockinger, Heinz et al. (2014) ‘Fifteen years SIB Swiss Institute of Bioinformatics : life science databases, tools and support’, Nucleic Acids Research, 42(Web Server issue), pp. W436–41. Available at: https://doi.org/10.1093/nar/gku380.

Vigano, Maria Alessandra et al. (2014) ‘An epigenetic profile of early T-cell development from multipotent progenitors to committed T-cell descendants.’, European journal of immunology, 44(4), pp. 1181–93. Available at: https://doi.org/10.1002/eji.201344022.

Wolf, L. (2014) Evolution of transcriptional regulation in ‘Escherichia coli’. Doctoral Thesis. Available at: https://doi.org/10.5451/unibas-006336555.

Jug, Florian et al. (2014) ‘Optimal joint segmentation and tracking of Escherichia coli in the mother machine’, in Cardoso, M. Jorge; Simpson, Ivor; Arbel, Tal; Precup, Doina; Ribbens, Annemie (ed.) Bayesian and grAphical models for biomedical imaging : First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014 ; revised selected papers. Cham: Springer (Bayesian and grAphical models for biomedical imaging : First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014 ; revised selected papers), pp. 25–36. Available at: https://doi.org/10.1007/978-3-319-12289-2_3.

Arnold, P. (2013) A framework to identify epigenome and transcription factor crosstalk. Doctoral Thesis. Available at: https://doi.org/10.5451/unibas-006145372.

Arnold, Phil et al. (2013) ‘Modeling of epigenome dynamics identifies transcription factors that mediate Polycomb targeting’, Genome research, 23(1), pp. 60–73. Available at: https://doi.org/10.1101/gr.142661.112.

Hausser, Jean et al. (2013) ‘Timescales and bottlenecks in miRNA-dependent gene regulation’, Molecular systems biology, 9, p. 711. Available at: https://doi.org/10.1038/msb.2013.68.

Hofsteenge, Niels, van Nimwegen, Erik and Silander, Olin K (2013) ‘Quantitative analysis of persister fractions suggests different mechanisms of formation among environmental isolates of E. coli’, BMC microbiology, 13(1), p. 25. Available at: https://doi.org/10.1186/1471-2180-13-25.

Khorshid, Mohsen et al. (2013) ‘A biophysical miRNA-mRNA interaction model infers canonical and noncanonical targets’, Nature methods, 10(3), pp. 253–5. Available at: https://doi.org/10.1038/nmeth.2341.

Meier-Abt, Fabienne et al. (2013) ‘Parity induces differentiation and reduces Wnt/Notch signaling ratio and proliferation potential of basal stem/progenitor cells isolated from mouse mammary epithelium’, Breast cancer research, 15(2), p. R36. Available at: https://doi.org/10.1186/bcr3419.

Ozonov, E.A. (2013) Modeling nucleosome mediated mechanisms of gene regulation. Doctoral Thesis. Available at: https://doi.org/10.5451/unibas-006173534.

Ozonov, Evgeniy A. and van Nimwegen, Erik (2013) ‘Nucleosome free regions in yeast promoters result from competitive binding of transcription factors that interact with chromatin modifiers’, PLoS Computational Biology, 9(8), p. e1003181. Available at: https://doi.org/10.1371/journal.pcbi.1003181.

Tiwari, Neha et al. (2013) ‘Sox4 is a master regulator of epithelial-mesenchymal transition by controlling ezh2 expression and epigenetic reprogramming’, Cancer cell, 23(6), pp. 768–83. Available at: https://doi.org/10.1016/j.ccr.2013.04.020.

Tiwari, N. et al. (2013) ‘Klf4 is a transcriptional regulator of genes critical for EMT, including Jnk1 (Mapk8)’, PLoS ONE, 8(2), p. e57329. Available at: https://doi.org/10.1371/journal.pone.0057329.

Aceto, Nicola et al. (2012) ‘Tyrosine phosphatase SHP2 promotes breast cancer progression and maintains tumor-initiating cells via activation of key transcription factors and a positive feedback signaling loop’, Nature medicine, 18(4), pp. 529–37. Available at: https://doi.org/10.1038/nm.2645.

Arner, Erik et al. (2012) ‘Adipose tissue microRNAs as regulators of CCL2 production in human obesity’, Diabetes, 61(8), pp. 1986–93. Available at: https://doi.org/10.2337/db11-1508.

Arnold, Phil et al. (2012) ‘MotEvo: integrated Bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences’, Bioinformatics, 28(4), pp. 487–94. Available at: https://doi.org/10.1093/bioinformatics/btr695.

Balwierz, P.J. (2012) Inference of gene regulatory interactions from deep sequencing data. Doctoral Thesis. Available at: https://doi.org/10.5451/unibas-006077371.

Pachkov, Mikhail et al. (2012) ‘SwissRegulon : a database of genome-wide annotations of regulatory sites: recent updates’, Nucleic Acids Research, 41(Database issue), pp. D214–20. Available at: https://doi.org/10.1093/nar/gks1145.

Pérez-Schindler, Joaquín et al. (2012) ‘The Corepressor NCoR1 Antagonizes PGC-1α and Estrogen-Related Receptor α in the Regulation of Skeletal Muscle Function and Oxidative Metabolism’, Molecular and cellular biology, 32(24), pp. 4913–24. Available at: https://doi.org/10.1128/mcb.00877-12.

Tippmann, Sylvia C et al. (2012) ‘Chromatin measurements reveal contributions of synthesis and decay to steady-state mRNA levels’, Molecular systems biology, 8, p. 593. Available at: https://doi.org/10.1038/msb.2012.23.

Erb, Ionas and van Nimwegen, Erik (2011) ‘Transcription Factor Binding Site Positioning in Yeast : Proximal Promoter Motifs Characterize TATA-Less Promoters’, PLoS ONE, 6(9), p. e24279. Available at: https://doi.org/10.1371/journal.pone.0024279.

Kawaji, H. et al. (2011) ‘Update of the FANTOM web resource : from mammalian transcriptional landscape to its dynamic regulation’, Nucleic Acids Research, 39(Database issue), pp. D856–60. Available at: https://doi.org/10.1093/nar/gkq1112.

Stadler, Michael B et al. (2011) ‘DNA-binding factors shape the mouse methylome at distal regulatory regions’, Nature, 480(7378), pp. 490–5. Available at: https://doi.org/10.1038/nature10716.

van Nimwegen, Erik (2011) ‘Update of the FANTOM web resource: From mammalian transcriptional landscape to its dynamic regulation’, Nucleic Acids Research, 39(SUPPL. 1). Available at: https://doi.org/10.1093/nar/gkq1112.

Burger, L.J. (2010) Inference of biomolecular interactions from sequence data. Doctoral Thesis. Available at: https://doi.org/10.5451/unibas-005184854.