Bioinformatics (van Nimwegen)

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. 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, T. et al. (2024) ‘E. coli leverages growth arrest to remodel its proteome upon entry into starvation’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory (bioRxiv). Available at: https://doi.org/10.1101/2024.02.29.582700.

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.

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. 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. 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. 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. 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.

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.

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.

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.

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. . Translated by van Nimwegen Erik; Handschin Christoph. Dissertation. Universität Basel.

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. 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. 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.

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. Available at: https://doi.org/10.1101/042903 .

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.

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, 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.

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.

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, 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.

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, 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.

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.

Burger, L. and van Nimwegen, E. (2010) ‘Disentangling direct from indirect co-evolution of residues in protein alignments’, PLoS Computational Biology, 6(1), p. e1000633. Available at: https://doi.org/10.1371/journal.pcbi.1000633.

Chen, Kevin et al. (2010) ‘Correlating gene expression variation with cis-regulatory polymorphism in Saccharomyces cerevisiae’, Genome biology and evolution, 2, pp. 697–707. Available at: https://doi.org/10.1093/gbe/evq054.

Ravasi, Timothy et al. (2010) ‘An atlas of combinatorial transcriptional regulation in mouse and man’, Cell, 140(5), pp. 744–52. Available at: https://doi.org/10.1016/j.cell.2010.01.044.

Summers, Kim M et al. (2010) ‘Co-expression of FBN1 with mesenchyme-specific genes in mouse cell lines : implications for phenotypic variability in Marfan syndrome’, European journal of human genetics, 18(11), pp. 1209–15. Available at: https://doi.org/10.1038/ejhg.2010.91.

van Nimwegen, Erik (2010) ‘Update of the FANTOM web resource: from mammalian transcriptional landscape to its dynamic regulation’, Nucleic Acids Research, 39. Available at: https://doi.org/ 10.1093/nar/gkq1112.

Balwierz, Piotr J et al. (2009) ‘Methods for analyzing deep sequencing expression data : constructing the human and mouse promoterome with deepCAGE data’, Genome biology, 10(7), p. R79. Available at: https://doi.org/10.1186/gb-2009-10-7-r79.

FANTOM Consortium et al. (2009) ‘The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line’, Nature genetics, 41(5), pp. 553–62. Available at: https://doi.org/10.1038/ng.375.

Mayoral, Ramon J et al. (2009) ‘MicroRNA-221-222 regulate the cell cycle in mast cells’, Journal of immunology, 182(1), pp. 433–45. Available at: https://doi.org/10.4049/jimmunol.182.1.433.

Molina, Nacho and van Nimwegen, Erik (2009) ‘Scaling laws in functional genome content across prokaryotic clades and lifestyles’, Trends in genetics, 25(6), pp. 243–7. Available at: https://doi.org/10.1016/j.tig.2009.04.004.

Severin, Jessica et al. (2009) ‘FANTOM4 EdgeExpressDB: : an integrated database of promoters, genes, microRNAs, expression dynamics and regulatory interactions’, Genome biology, 10(4), p. R39. Available at: https://doi.org/10.1186/gb-2009-10-4-r39.

Taddei, Angela et al. (2009) ‘The functional importance of telomere clustering : Global changes in gene expression result from SIR factor dispersion’, Genome research, 19(4), pp. 611–25. Available at: https://doi.org/10.1101/gr.083881.108.

Berninger, Philipp et al. (2008) ‘Computational analysis of small RNA cloning data’, Methods, 44(1), pp. 13–21. Available at: https://doi.org/10.1016/j.ymeth.2007.10.002.

Burger, Lukas and van Nimwegen, Erik (2008) ‘Accurate prediction of protein-protein interactions from sequence alignments using a Bayesian method’, Molecular systems biology, 4, p. 165. Available at: https://doi.org/10.1038/msb4100203.

Chern, Tzu-Ming et al. (2008) ‘Computational analysis of full-length cDNAs reveals frequent coupling between transcriptional and splicing programs’, DNA research, 15(2), pp. 63–72. Available at: https://doi.org/10.1093/dnares/dsm036.

Molina, Nacho and van Nimwegen, Erik (2008) ‘The evolution of domain-content in bacterial genomes’, Biology Direct, 3, p. 51. Available at: https://doi.org/10.1186/1745-6150-3-51.

Molina, Nacho and van Nimwegen, Erik (2008) ‘Universal patterns of purifying selection at noncoding positions in bacteria’, Genome research, 18(1), pp. 148–60. Available at: https://doi.org/10.1101/gr.6759507.

Schlecht, U. et al. (2008) ‘Genome-wide expression profiling, in vivo DNA binding analysis, and probabilistic motif prediction reveal novel Abf1 target genes during fermentation, respiration, and sporulation in yeast’, Molecular Biology of the Cell, 19(5), pp. 2193–207. Available at: https://doi.org/10.1091/mbc.e07-12-1242.

Gaidatzis, Dimos et al. (2007) ‘Inference of miRNA targets using evolutionary conservation and pathway analysis’, BMC bioinformatics, 8, p. 69. Available at: https://doi.org/10.1186/1471-2105-8-69.

Pachkov, M. et al. (2007) ‘SwissRegulon : a database of genome-wide annotations of regulatory sites’, Nucleic Acids Research, 35(Database issue), pp. D127–31. Available at: https://doi.org/10.1093/nar/gkl857.

Siddharthan, Rahul and van Nimwegen, Erik (2007) ‘Detecting regulatory sites using PhyloGibbs’, Methods in Molecular Biology, 395, pp. 381–402. Available at: https://doi.org/10.1007/978-1-59745-514-5_24.

van Nimwegen, Erik (2007) ‘Finding regulatory elements and regulatory motifs : a general probabilistic framework’, BMC Bioinformatics, 8, p. S4. Available at: https://doi.org/10.1186/1471-2105-8-s6-s4.

Zavolan, M. and van Nimwegen, E. (2006) ‘The types and prevalence of alternative splice forms [Current Opinion in Structural Biology 2006, 16:362-367] (DOI:10.1016/j.sbi.2006.05.002)’, Current Opinion in Structural Biology, 16(4). Available at: https://doi.org/10.1016/j.sbi.2006.06.010.

Burger, L. and Van Nimwegen, E. (2006) ‘A Bayesian algorithm for reconstructing two-component signaling networks’. Springer Verlag, pp. 44–55. Available at: https://doi.org/10.1007/11851561_5.

Chern, Tzu-Ming et al. (2006) ‘A simple physical model predicts small exon length variations’, PLoS genetics, 2(4), p. e45. Available at: https://doi.org/10.1371/journal.pgen.0020045.

van Nimwegen, E. (2006) ‘Influenza escapes immunity along neutral networks’, Science, Vol. 314, no. 5807, pp. 1884–1886. Available at: https://doi.org/10.1126/science.1137300.

van Nimwegen, Erik et al. (2006) ‘SPA : a probabilistic algorithm for spliced alignment’, PLoS genetics, 2(4), p. e24. Available at: https://doi.org/10.1371/journal.pgen.0020024.