Computational Modeling of Biological Processes (Neher)

Goya, S. et al. (2024) ‘The unified proposal for classification of human respiratory syncytial virus below the subgroup level’, bioRxiv [Preprint]. Cold Spring Harbor Laboratory. Available at: https://doi.org/10.1101/2024.02.13.24302237.

Lustig, Gila et al. (2024) ‘SARS-CoV-2 infection in immunosuppression evolves sub-lineages which independently accumulate neutralization escape mutations’, Virus Evolution, 10. Available at: https://doi.org/10.1093/ve/vead075.

O’Toole, Á. et al. (2023) ‘APOBEC3 deaminase editing in mpox virus as evidence for sustained human transmission since at least 2016’, Science, 382(6670), pp. 595–600. Available at: https://doi.org/10.1126/science.adg8116.

Borges, V. et al. (2023) ‘Viral genetic clustering and transmission dynamics of the 2022 mpox outbreak in Portugal’, Nature Medicine, 29(10), pp. 2509–2517. Available at: https://doi.org/10.1038/s41591-023-02542-x.

Shaw, L.P. and Neher, R.A. (2023) ‘Visualizing and quantifying structural diversity around mobile AMR genes’. bioRxiv. Available at: https://doi.org/10.1101/2023.08.07.551646.

Noll, N. et al. (2023) ‘PanGraph: scalable bacterial pan-genome graph construction’, Microbial Genomics, 9(6). Available at: https://doi.org/10.1099/mgen.0.001034.

Bloom, J.D. et al. (2023) ‘Evolution of the SARS-CoV-2 Mutational Spectrum’, Molecular Biology and Evolution, 40(4). Available at: https://doi.org/10.1093/molbev/msad085.

Dadonaite, B. et al. (2023) ‘A pseudovirus system enables deep mutational scanning of the full SARS-CoV-2 spike’, Cell, 186(6), pp. 1263–1278.e20. Available at: https://doi.org/10.1016/j.cell.2023.02.001.

Bloom, J.D. and Neher, R.A. (2023) ‘Fitness effects of mutations to SARS-CoV-2 proteins’. bioRxiv. Available at: https://doi.org/10.1101/2023.01.30.526314.

O’Toole, Á. et al. (2023) ‘Putative APOBEC3 deaminase editing in MPXV as evidence for sustained human transmission since at least 2016’. bioRxiv. Available at: https://doi.org/10.1101/2023.01.23.525187.

Bloom, J.D. and Neher, R.A. (2023) ‘Fitness effects of mutations to SARS-CoV-2 proteins’, Virus Evolution, 9(2). Available at: https://doi.org/10.1093/ve/vead055.

Druelle, V. and Neher, R.A. (2023) ‘Reversions to consensus are positively selected in HIV-1 and bias substitution rate estimates’, Virus Evolution, 9(1). Available at: https://doi.org/10.1093/ve/veac118.

Inzaule, S.C. et al. (2023) ‘Recommendations on data sharing in HIV drug resistance research’, PLoS Medicine, 20. Available at: https://doi.org/10.1371/journal.pmed.1004293.

Khan, Khadija et al. (2023) ‘Evolution and neutralization escape of the SARS-CoV-2 BA.2.86 subvariant’, Nature Communications, 14. Available at: https://doi.org/10.1038/s41467-023-43703-3.

Lee, J. et al. (2023) ‘Joint visualization of seasonal influenza serology and phylogeny to inform vaccine composition’, Frontiers in Bioinformatics, 3. Available at: https://doi.org/10.3389/fbinf.2023.1069487.

Neves, A. et al. (2023) ‘The Swiss Pathogen Surveillance Platform – towards a nationwide One Health data exchange platform for bacterial, viral and fungal genomics and associated metadata’, Microbial Genomics, 9. Available at: https://doi.org/10.1099/mgen.0.001001.

Shaw, Liam P. and Neher, Richard A. (2023) ‘Visualizing and quantifying structural diversity around mobile resistance genes’, Microbial Genomics, 9. Available at: https://doi.org/10.1099/mgen.0.001168.

Bloom, Jesse D et al. (2022) ‘Evolution of the SARS-CoV-2 mutational spectrum’. bioRxiv. Available at: https://doi.org/10.1101/2022.11.19.517207.

Barrat-Charlaix, Pierre, Vaughan, Timothy G. and Neher, Richard A. (2022) ‘TreeKnit: Inferring ancestral reassortment graphs of influenza viruses’, PLoS Computational Biology, 18(8), p. e1010394. Available at: https://doi.org/10.1371/journal.pcbi.1010394.

Happi, Christian et al. (2022) ‘Urgent need for a non-discriminatory and non-stigmatizing nomenclature for monkeypox virus’, PLoS Biology, 20(8), p. e3001769. Available at: https://doi.org/10.1371/journal.pbio.3001769.

Hodcroft, Emma B. et al. (2022) ‘Evolution, geographic spreading, and demographic distribution of Enterovirus D68’, PLoS Pathogens, 18(5), p. e1010515. Available at: https://doi.org/10.1371/journal.ppat.1010515.

Huisman, Jana S. et al. (2022) ‘Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2’, eLife, 11, p. 50. Available at: https://doi.org/10.7554/elife.71345.

Nadeau, Sarah A. et al. (2022) ‘Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data’, Science Translational Medicine, 15(680), p. eabn7979. Available at: https://doi.org/10.1126/scitranslmed.abn7979.

Neher, R.A. (2022) ‘Contributions of adaptation and purifying selection to SARS-CoV-2 evolution’, Virus Evolution, 8(2). Available at: https://doi.org/10.1093/ve/veac113.

Reichmuth, Martina L. et al. (2022) ‘Impact of cross-border-associated cases on the SARS-CoV-2 epidemic in Switzerland during summer 2020 and 2021’, Epidemics, 41, p. 100654. Available at: https://doi.org/10.1016/j.epidem.2022.100654.

Barrat-Charlaix, Pierre, Vaughan, Timothy G. and Neher, Richard A. (2021) ‘TreeKnit: Inferring Ancestral Reassortment Graphs of influenza viruses’. bioRxiv. Available at: https://doi.org/10.1101/2021.12.20.473456.

Aksamentov, Ivan et al. (2021) ‘Nextclade: clade assignment, mutation calling and quality control for viral genomes’, Journal of Open Source Software, 6(67), p. 3773. Available at: https://doi.org/10.21105/joss.03773.

Barrat-Charlaix, Pierre et al. (2021) ‘Limited Predictability of Amino Acid Substitutions in Seasonal Influenza Viruses’, Molecular Biology and Evolution, 38(7), pp. 2767–2777. Available at: https://doi.org/10.1093/molbev/msab065.

Benschop, Kimberley S. M. et al. (2021) ‘Molecular Epidemiology and Evolutionary Trajectory of Emerging Echovirus 30, Europe’, Emerging infectious diseases, 27(6), pp. 1616–1626. Available at: https://doi.org/10.3201/eid2706.203096.

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.

Boehm, Erik et al. (2021) ‘Novel SARS-CoV-2 variants: the pandemics within the pandemic’, Clinical microbiology and infection, 27(8), pp. 1109–1117. Available at: https://doi.org/10.1016/j.cmi.2021.05.022.

Dyrdak, Robert et al. (2021) ‘Interactions between seasonal human coronaviruses and implications for the SARS-CoV-2 pandemic: A retrospective study in Stockholm, Sweden, 2009-2020’, Journal of clinical virology, 136(March), p. 104754. Available at: https://doi.org/10.1016/j.jcv.2021.104754.

Goncalves Cabecinhas, Ana Rita et al. (2021) ‘SARS-CoV-2 N501Y Introductions and Transmissions in Switzerland from Beginning of October 2020 to February 2021-Implementation of Swiss-Wide Diagnostic Screening and Whole Genome Sequencing’, Microorganisms, 9(4), p. 9040677. Available at: https://doi.org/10.3390/microorganisms9040677.

Hodcroft, Emma B. et al. (2021) ‘Spread of a SARS-CoV-2 variant through Europe in the summer of 2020’, Nature, 595(7869), pp. 707–712. Available at: https://doi.org/10.1038/s41586-021-03677-y.

Huddleston, John et al. (2021) ‘Augur: a bioinformatics toolkit for phylogenetic analyses of human pathogens’, Journal of Open Source Software, 6(57), p. 4. Available at: https://doi.org/10.21105/joss.02906.

Konings, Frank et al. (2021) ‘SARS-CoV-2 Variants of Interest and Concern naming scheme conducive for global discourse’, Nature Microbiology, 6(7), pp. 821–823. Available at: https://doi.org/10.1038/s41564-021-00932-w.

Vogels, Chantal B. F. et al. (2021) ‘Multiplex qPCR discriminates variants of concern to enhance global surveillance of SARS-CoV-2’, PLoS Biology, 19(5), p. e3001236. Available at: https://doi.org/10.1371/journal.pbio.3001236.

Huisman, Jana S. et al. (2020) ‘Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2’. medRxiv. Available at: https://doi.org/10.1101/2020.11.26.20239368.

Hodcroft, Emma B. et al. (2020) ‘Emergence and spread of a SARS-CoV-2 variant through Europe in the summer of 2020’. medRxiv. Available at: https://doi.org/10.1101/2020.10.25.20219063.

Dyrdak, R. et al. (2020) ‘Interactions between seasonal human coronaviruses and implications for the SARS-CoV-2 pandemic: A retrospective study in Stockholm, Sweden, 2009-2020’. medRxiv . Available at: https://doi.org/10.1101/2020.10.01.20205096.

Barrat-Charlaix, Pierre et al. (2020) ‘Limited predictability of amino acid substitutions in seasonal influenza viruses’. bioRxiv. Available at: https://doi.org/10.1101/2020.07.31.231100.

Morris, Dylan H. et al. (2020) ‘Asynchrony between virus diversity and antibody selection limits influenza virus evolution’. bioRxiv. Available at: https://doi.org/10.1101/2020.04.27.064915.

Jackson, Laurelle et al. (2020) ‘Complementation can maintain a quasispecies of drug sensitive and resistant HIV’. bioRxiv. Available at: https://doi.org/10.1101/2020.04.20.051854.

Egli, Adrian et al. (2020) ‘High-resolution influenza mapping of a city reveals socioeconomic determinants of transmission within and between urban quarters’. bioRxiv. Available at: https://doi.org/10.1101/2020.04.03.023135.

Neher, Richard A. et al. (2020) ‘Potential impact of seasonal forcing on a SARS-CoV-2 pandemic’. medRxiv. Available at: https://doi.org/10.1101/2020.02.13.20022806.

Puller, Vadim, Sagulenko, Pavel and Neher, Richard A. (2020) ‘Efficient inference, potential, and limitations of site-specific substitution models’. bioRxiv. Available at: https://doi.org/10.1101/2020.01.18.911255.

Alm, Erik et al. (2020) ‘Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020’, Eurosurveillance, 25(32), pp. 7–14. Available at: https://doi.org/10.2807/1560-7917.es.2020.25.32.2001410.

Battegay, Manuel et al. (2020) ‘2019-novel Coronavirus (2019-nCoV): estimating the case fatality rate - a word of caution’, Swiss Medical Weekly, 150, p. w20203. Available at: https://doi.org/10.4414/smw.2020.20203.

Bedford, Trevor et al. (2020) ‘Cryptic transmission of SARS-CoV-2 in Washington state’, Science, 370(6516), pp. 571–575. Available at: https://doi.org/10.1126/science.abc0523.

Fauver, Joseph R. et al. (2020) ‘Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in the United States’, Cell, 181(5), pp. 990–996.e5. Available at: https://doi.org/10.1016/j.cell.2020.04.021.

Huddleston, John et al. (2020) ‘Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution’, eLife, 9, p. e60067. Available at: https://doi.org/10.7554/elife.60067.

Mavian, Carla et al. (2020) ‘Sampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable’, Proceedings of the National Academy of Sciences of the United States of America, 117(23), pp. 12522–12523. Available at: https://doi.org/10.1073/pnas.2007295117.

Midgley, Sofie Elisabeth et al. (2020) ‘Co-circulation of enterovirus D68 subclades, including a novel B3 cluster, across Europe in a season of expected low prevalence, 2019/20’, Eurosurveillance, 25(2), pp. 7–18. Available at: https://doi.org/10.2807/1560-7917.es.2020.25.2.1900749.

Morris, Dylan H. et al. (2020) ‘Asynchrony between virus diversity and antibody selection limits influenza virus evolution’, eLife, 9, p. e62105. Available at: https://doi.org/10.7554/elife.62105.

Müller, Nicola F. et al. (2020) ‘Characterising the epidemic spread of influenza A/H3N2 within a city through phylogenetics’, PLoS Pathogens, 16(11), p. e1008984. Available at: https://doi.org/10.1371/journal.ppat.1008984.

Neher, Richard A. et al. (2020) ‘Potential impact of seasonal forcing on a SARS-CoV-2 pandemic’, Swiss Medical Weekly, 150, p. w20224. Available at: https://doi.org/10.4414/smw.2020.20224.

Noll, Nicholas B. et al. (2020) ‘COVID-19 Scenarios: an interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2’. medRxiv. Available at: https://doi.org/10.1101/2020.05.05.20091363.

Puller, Vadim, Sagulenko, Pavel and Neher, Richard A. (2020) ‘Efficient inference, potential, and limitations of site-specific substitution models’, Virus Evolution, 6(2), p. veaa066. Available at: https://doi.org/10.1093/ve/veaa066.

Salathé, Marcel et al. (2020) ‘COVID-19 epidemic in Switzerland: on the importance of testing, contact tracing and isolation’, Swiss Medical Weekly, 150, p. w20225. Available at: https://doi.org/10.4414/smw.2020.20225.

Bedford, Trevor et al. (2019) ‘Seasonal influenza circulation patterns and projections for September 2019 to September 2020’. bioRxiv. Available at: https://doi.org/10.1101/780627.

Potter, Barney I. et al. (2019) ‘Evolution and rapid spread of a reassortant A(H3N2) virus that predominated the 2017-2018 influenza season’. bioRxiv. Available at: https://doi.org/10.1101/543322.

Carvajal-Yepes, M. et al. (2019) ‘A global surveillance system for crop diseases’, Science, 364(6447), pp. 1237–1239. Available at: https://doi.org/10.1126/science.aaw1572.

Cunrath, Olivier et al. (2019) ‘Quantitative contribution of efflux to multi-drug resistance of clinical Escherichia coli and Pseudomonas aeruginosa strains’, EBioMedicine, 41, pp. 479–487. Available at: https://doi.org/10.1016/j.ebiom.2019.02.061.

Dyrdak, Robert et al. (2019) ‘Intra- and interpatient evolution of enterovirus D68 analyzed by whole-genome deep sequencing’, Virus Evolution, 5(1), p. vez007. Available at: https://doi.org/10.1093/ve/vez007.

Egli, Adrian et al. (2019) ‘Symposium report: One Health meets sequencing’, Microbes and Infection, 22(1), pp. 1–7. Available at: https://doi.org/10.1016/j.micinf.2019.07.004.

Potter, Barney I. et al. (2019) ‘Evolution and rapid spread of a reassortant A(H3N2) virus that predominated the 2017-2018 influenza season’, Virus Evolution, 5(2). Available at: https://doi.org/10.1093/ve/vez046.

Wüthrich, Daniel et al. (2019) ‘Evaluation of two workflows for whole genome sequencing-based typing of influenza A viruses’, Journal of Virological Methods, 266, pp. 30–33. Available at: https://doi.org/10.1016/j.jviromet.2019.01.009.

Yan, Le, Neher, Richard A. and Shraiman, Boris I. (2019) ‘Phylodynamic theory of persistence, extinction and speciation of rapidly adapting pathogens’, eLife, 8, p. e44205. Available at: https://doi.org/10.7554/elife.44205.

Noll, Nicholas et al. (2018) ‘Resolving structural diversity of Carbapenemase-producing gram-negative bacteria using single molecule sequencing’. bioRxiv. Available at: https://doi.org/10.1101/456897.

Yan, Le, Neher, Richard A. and Shraiman, Boris I. (2018) ‘Phylodynamics of rapidly adapting pathogens: extinction and speciation of a Red Queen’. bioRxiv. Available at: https://doi.org/10.1101/455444.

Dyrdak, Robert et al. (2018) ‘Intra- and interpatient evolution of enterovirus D68 analyzed by whole-genome deep sequencing’. bioRxiv. Available at: https://doi.org/10.1101/420836.

Neher, Richard A. (2018) ‘Efficient estimation of evolutionary rates by covariance aware regression’. bioRxiv. Available at: https://doi.org/10.1101/408005.

Neher, Richard A. and Bedford, Trevor (2018) ‘Real-time analysis and visualization of pathogen sequence data’. bioRxiv. Available at: https://doi.org/10.1101/286187.

Bedford, Trevor and Neher, Richard A. (2018) ‘Seasonal influenza circulation patterns and projections for Feb 2018 to Feb 2019’. bioRxiv. Available at: https://doi.org/10.1101/271114.

Egli, Adrian et al. (2018) ‘Improving the quality and workflow of bacterial genome sequencing and analysis: paving the way for a Switzerland-wide molecular epidemiological surveillance platform’, Swiss Medical Weekly, 148(49-50), p. w14693. Available at: https://doi.org/10.4414/smw.2018.14693.

Hadfield, James et al. (2018) ‘Nextstrain: real-time tracking of pathogen evolution’, Bioinformatics, 34(23), pp. 4121–4123. Available at: https://doi.org/10.1093/bioinformatics/bty407.

Jackson, Laurelle et al. (2018) ‘Incomplete inhibition of HIV infection results in more HIV infected lymph node cells by reducing cell death’, eLife, 7(e30134), pp. 1–24. Available at: https://doi.org/10.7554/elife.30134.

Javed, Mumina et al. (2018) ‘Colistin susceptibility test evaluation of multiple-resistance-level Pseudomonas aeruginosa isolates generated in a morbidostat device’, The journal of antimicrobial chemotherapy, 73(12), pp. 3368–3374. Available at: https://doi.org/10.1093/jac/dky337.

Karasov, Talia L. et al. (2018) ‘Arabidopsis thaliana and Pseudomonas Pathogens Exhibit Stable Associations over Evolutionary Timescales’, Cell Host & Microbe, 24(1), pp. 168–179.e4. Available at: https://doi.org/10.1016/j.chom.2018.06.011.

Neher, Richard A. and Bedford, Trevor (2018) ‘Real-time analysis and visualization of pathogen sequence data’, Journal of clinical microbiology, 56(11), pp. e00480–18. Available at: https://doi.org/10.1128/jcm.00480-18.

Sagulenko, Pavel, Puller, Vadim and Neher, Richard A. (2018) ‘TreeTime: Maximum-likelihood phylodynamic analysis’, Virus Evolution, 4(1), p. vex042. Available at: https://doi.org/10.1093/ve/vex042.

Bedford, Trevor and Neher, Richard A. (2017) ‘Seasonal influenza circulation patterns and projections for Sep 2017 to Sep 2018’. bioRxiv. Available at: https://doi.org/10.1101/191676.

Sagulenko, Pavel, Puller, Vadim and Neher, Richard A. (2017) ‘TreeTime: maximum likelihood phylodynamic analysis’. bioRxiv. Available at: https://doi.org/10.1101/153494.

Puller, Vadim, Neher, Richard and Albert, Jan (2017) ‘Estimating time of HIV-1 infection from next-generation sequence diversity’. bioRxiv. Available at: https://doi.org/10.1101/129387.

Bedford, Trevor and Neher, Richard A. (2017) ‘Seasonal influenza circulation patterns and projections for 2017-2018’. bioRxiv. Available at: https://doi.org/10.1101/113035.

Ding, Wei, Baumdicker, Franz and Neher, Richard A. (2017) ‘panX: pan-genome analysis and exploration’, Nucleic acids research, 46(1), p. e5. Available at: https://doi.org/10.1093/nar/gkx977.

Morris, Dylan H. et al. (2017) ‘Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology’, Trends in Microbiology, 26(2), pp. 102–118. Available at: https://doi.org/10.1016/j.tim.2017.09.004.

Puller, Vadim, Neher, Richard and Albert, Jan (2017) ‘Estimating time of HIV-1 infection from next-generation sequence diversity’, PLoS Computational Biology, 13(10), p. e1005775. Available at: https://doi.org/10.1371/journal.pcbi.1005775.

Regenbogen, Bianca et al. (2017) ‘Rapid and consistent evolution of colistin resistance in XDR Pseudomonas aeruginosa during morbidostat culture’, Antimicrobial agents and chemotherapy, 61(9), pp. e00043–17. Available at: https://doi.org/10.1128/aac.00043-17.

You, Yuan et al. (2017) ‘Temporal dynamics of gene expression and histone marks at the Arabidopsis shoot meristem during flowering’, Nature Communications, 8, p. 15120. Available at: https://doi.org/10.1038/ncomms15120.

Zanini, Fabio et al. (2017) ‘In vivo mutation rates and the landscape of fitness costs of HIV-1’, Virus Evolution, 3(1), p. vex003. Available at: https://doi.org/10.1093/ve/vex003.

Boullé, Mikaël et al. (2016) ‘HIV Cell-to-Cell Spread Results in Earlier Onset of Viral Gene Expression by Multiple Infections per Cell’, PLoS Pathogens, 12(11), p. e1005964. Available at: https://doi.org/10.1371/journal.ppat.1005964.

Brodin, Johanna et al. (2016) ‘Establishment and stability of the latent HIV-1 DNA reservoir’, eLife, 5, p. 15. Available at: https://doi.org/10.7554/elife.18889.

Hassouna, I. et al. (2016) ‘Revisiting adult neurogenesis and the role of erythropoietin for neuronal and oligodendroglial differentiation in the hippocampus’, Molecular Psychiatry, 21(12), pp. 1752–1767. Available at: https://doi.org/10.1038/mp.2015.212.

Neher, Richard A. et al. (2016) ‘Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses’, Proceedings of the National Academy of Sciences of the United States of America, 113(12), pp. E1701–9. Available at: https://doi.org/10.1073/pnas.1525578113.

Zanini, Fabio et al. (2016) ‘Error rates, PCR recombination, and sampling depth in HIV-1 whole genome deep sequencing’, Virus Research, 239, pp. 106–114. Available at: https://doi.org/10.1016/j.virusres.2016.12.009.

Brodin, Johanna et al. (2015) ‘Challenges with using primer IDs to improve accuracy of next generation sequencing’, PLoS ONE, 10(3), p. e0119123. Available at: https://doi.org/10.1371/journal.pone.0119123.

Neher, Richard A. and Bedford, Trevor (2015) ‘nextflu: real-time tracking of seasonal influenza virus evolution in humans’, Bioinformatics, 31(21), pp. 3546–8. Available at: https://doi.org/10.1093/bioinformatics/btv381.

Zanini, Fabio et al. (2015) ‘Population genomics of intrapatient HIV-1 evolution’, eLife, 4, p. 26. Available at: https://doi.org/10.7554/elife.11282.

Good, Benjamin H. et al. (2014) ‘Genetic diversity in the interference selection limit’, PLoS Genetics, 10(3), p. e1004222. Available at: https://doi.org/10.1371/journal.pgen.1004222.