"seraphim" 2.0

seraphim is a R package for studying phylogenetically informed movements. It can for instance be used to investigate the impact of environmental factors on the dispersal history and dynamics of viral lineages, to estimate lineage dispersal statistics, to map continuous phylogeographic reconstructions, or to conduct continuous phylogeographic simulations. See below for a list of new features and tools implemented in version 2.0, as well as our recent application note in Bioinformatics (Dellicour et al. 2026) for a summary of these new features.

Stay tuned!

If you want to remain informed about last updates or improvements, just send an e-mail to simon.dellicour[at]ulb[dot]be with "seraphim mailing list" in the object.

What's new in "seraphim" 2.0?

• spatio-temporal information embedded in annotated MCC or posterior trees retrieved from a Bayesian continuous phylogeographic inference can now be extracted using two new functions — mccTreeExtractionsand postTreeExtractions — to generate similar extraction files, one per tree and with each row corresponding to a distinct phylogenetic branch.
• since the initial 2016 application note of the package, the previous spreadGraphic function has been replaced by new functions for the generation of uncertainty polygons that can be saved as continuous vectorial files (e.g. in a shapefile format) instead of in a raster file (see the related tutorial here). Those polygons correspond to the highest density posterior (HPD) regions reflecting the uncertainty associated with the Bayesian phylogeographic inference. They can either be associated with each internal node of a maximum clade credibility tree (using spreadGraphic1) or be estimated by time slice while considering several posterior trees and all internal nodes falling in each time slice (using spreadGraphic2).
• the spreadStatistics function has been updated to now also include the estimation of isolation-by-distance (IBD) signal metrics (see our 2024 study as well as the dedicated tutorial for further detail).
• the spreadFactors function now focuses on testing the association between environmental factors on the diffusion - instead of the dispersal - velocity of lineages (see our 2025 study as well as the dedicated tutorial for further detail).
• the spreadFactors function can now also be used to conduct alternative post hoc analyses on the isolation-by-resistance (IBR), i.e. to what extent environmental factors can be associated with a deviation from an IBD pattern (see our 2025 study).
• in addition to the two post hoc approaches implemented in the spreadFactors function, the package can now also be used to follow prior-informed (as opposed to post hoc) landscape phylogeographic approaches to investigate the impact of environmental factors on the diffusion velocity of lineages (see our 2025 study and the related tutorial for further detail). Such prior-informed landscape phylogeographic analyses can for instance be conducted through an environmental factor based multidimensional scaling transformation with the mdsTransformation function added to the package.
• the package now includes four phylogeographic simulators implemented in distinct functions: (i) the function treesRandomisations to conduct tree branches randomisation on an environmental raster according to various randomisation procedure and with the possibility to consider an impact on the environmental values on the repulsion or attraction of lineages, (ii) the function simulatorRRW1 to conduct simulations of a relaxed random walk (RRW) diffusion process along time-scaled phylogenies (which can, e.g., be used to investigate the impact of barriers on the dispersal frequency of lineages, as illustrated here), (iii) the function simulatorRRW2 to conduct simulations based on a birth-death process and a Brownian random walk (BRW) or a RRW diffusion process (applied in our 2024 study), and (iv) the function simulatorRRW3 to conduct simulations of a RRW diffusion process with a dispersal velocity impacted by an environmental raster (applied in our 2025 study).

Installation

In R, seraphim can be installed with the devtools package:

install.packages("devtools"); library(devtools)
install_github("sdellicour/seraphim/unix_OS") # (for a Unix OS)
install_github("sdellicour/seraphim/windows") # (for a Windows OS)

References

Package references

• Dellicour S, Faria N, Rose R, Lemey P, Pybus OG (2026). SERAPHIM 2.0: an extended toolbox for studying phylogenetically informed movements. Bioinformatics 42: btag093
• Dellicour S, Rose R, Faria N, Lemey P, Pybus OG (2016). SERAPHIM: studying environmental rasters and phylogenetically-informed movements. Bioinformatics 32: 3204-3206

Estimation of dispersal statistics

• Dellicour S, Bastide P, Rocu P, Fargette D, Hardy OJ, Suchard MA, Guindon S, Lemey P (2024). How fast are viruses spreading in the wild? PLoS Biology 22: e3002914
• Dellicour S, Rose R, Pybus OG (2016). Explaining the geographic spread of emerging epidemics: a framework for comparing viral phylogenies and environmental landscape data. BMC Bioinformatics 17: 82

Landscape phylogeographic analyses

Investigating the association between lineage diffusion velocity and environmental factors:

• Dellicour S, Gámbaro F, Jacquot M, Lequime S, Baele G, Gilbert M, Pybus OG, Suchard MA, Lemey P (2025). Comparative performance of viral landscape phylogeography approaches. Proceedings of the National Academy of Sciences of the USA 122: e2506743122
• Dellicour S, Rose R, Faria NR, Vieira LFP, Bourhy H, Gilbert M, Lemey P, Pybus OG (2017). Using viral gene sequences to compare and explain the heterogeneous spatial dynamics of virus epidemics. Molecular Biology & Evolution 34: 2563-2571

Investigating the association between lineage dispersal locations and environmental factors:

• Dellicour S, Lequime S, Vrancken B, Gill MS, Bastide P, Gangavarapu K, Matteson NL, Tan Y, du Plessis L, Fisher AA, Nelson MI, Gilbert M, Suchard MA, Andersen KG, Grubaugh ND, Pybus OG, Lemey P (2020). Epidemiological hypothesis testing using a phylogeographic and phylodynamic framework. Nature Communications 11: 5620
• Dellicour S, Troupin C, Jahanbakhsh F, Salama A, Massoudi S, Moghaddam MK, Baele G, Lemey P, Gholami A, Bourhy H (2019). Using phylogeographic approaches to analyse the dispersal history, velocity, and direction of viral lineages – application to rabies virus spread in Iran. Molecular Ecology 28: 4335-4350

Investigating the association between lineage dispersal frequency and environmental factors:

• Dellicour S, Baele G, Dudas G, Faria NR, Pybus OG, Suchard MA, Rambaut A, Lemey P (2018). Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. Nature Communications 9: 2222

Phylogeographic simulators

Simulations of a RRW diffusion process along time-scaled phylogenies:

• Dellicour S, Baele G, Dudas G, Faria NR, Pybus OG, Suchard MA, Rambaut A, Lemey P (2018). Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. Nature Communications 9: 2222

Simulations based on a birth-death process and a BRW or a RRW diffusion process:

• Dellicour S, Bastide P, Rocu P, Fargette D, Hardy OJ, Suchard MA, Guindon S, Lemey P (2024). How fast are viruses spreading in the wild? PLoS Biology 22: e3002914

Simulations of a RRW diffusion process with a dispersal velocity impacted by an environmental raster:

• Dellicour S, Gámbaro F, Jacquot M, Lequime S, Baele G, Gilbert M, Pybus OG, Suchard MA, Lemey P (2025). Comparative performance of viral landscape phylogeography approaches. Proceedings of the National Academy of Sciences of the USA 122: e2506743122