Author

P. Sagulenko

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Evolutionary Biology· 3

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:DNA methylation· 2 Organism:virus· 2 Research Focus:host-pathogen & immune response· 1 Research Focus:phylogenetics / evolution· 1 Research Focus:variant calling· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

R. Neher· 2 shared V. Puller· 2 shared B. Potter· 1 shared C. Callender· 1 shared C. Megill· 1 shared J. Hadfield· 1 shared J. Huddleston· 1 shared R. A. Neher· 1 shared

Activity by Year

2017-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersEvolutionary Biology

R. Neher

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Author profile →

3 papers

Recent·Most cited

Efficient inference, potential, and limitations of site-specific substitution model

V. Puller, P. Sagulenko, R. Neher

The paper develops an efficient algorithm to estimate site-specific substitution models and evaluates their accuracy using simulated data and HIV alignments, finding issues with identifiability and divergence saturation.

bioRxiv · Jan 18, 2020Evolutionary Biology

Nextstrain: real-time tracking of pathogen evolution

J. Hadfield, C. Megill, S. M. Bell, J. Huddleston, B. Potter, C. Callender, P. Sagulenko, T. Bedford, R. A. Neher

Nextstrain provides real-time tracking of pathogen evolution by integrating viral genome data with other public health data through a database, bioinformatics pipeline, and visualization platform.

bioRxiv · Nov 22, 2017Evolutionary Biology

TreeTime: maximum likelihood phylodynamic analysis

P. Sagulenko, V. Puller, R. Neher

TreeTime is a Python framework for phylodynamic analysis that uses an approximate Maximum Likelihood approach to estimate ancestral states, infer evolution models, and scale linearly with dataset size.

bioRxiv · Aug 2, 2017Evolutionary Biology