Prof. Dr. Sebastian Höhna

Source: https://www.geo.lmu.de/en/faculty-for-geosciences/persons/contact-page/sebastian-hoehna-82686a87.html Parent: https://www.geo.lmu.de/en/commissions/

Office address:

Richard-Wagner-Str. 10

Room D 005

I’m a computational biologist with focus on Bayesian phylogenetic inference. Natural selection by means of adaptation and genetic inheritance are key principles in evolutionary biology. Evolutionary relationships are commonly depicted by phylogenetic trees. Using phylogenetic methods we can learn about the evolutionary history of species and the processes that have contributed to present diversity. I’m developing statistical and computational methods to infer phylogenies from molecular sequence data. These methods additionally identify periods of adaptive genetic evolution at lineage or genes. Furthermore, I develop mathematical models to study macroevolutionary patterns, such as, diversification rate variation over time and among lineages and episodes of global mass extinctions.

CV Sebastian Höhna (PDF, 250 KB)

Research Interests

I am focusing on several aspects of theoretical and computational phylogenetics. Using our server facilities our primary aim is to develop new statistical models and computational methods to answer research questions in phylogenetics, which include:

Estimating phylogenetic relationships
Estimating divergence times and rates of molecular evolution
Inferring rates and mode of lineage diversification (speciation and extinction)
Analyzing causes of conflicting gene trees.

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Robust Estimation of Gene Trees

Gene trees provide fundamental information about the processes in genetic evolution which is contained in the variation of their topology and branching times. In this work we are focused on how to reliably estimate gene trees by developing more robust statistical methods.

Research projects:

Von Genomen zur Makroevolution: Entwicklung von phylogenetischen Methoden zur Genbaum, Speziesbaum und Divergenzraten Schätzung (DFG, seit 2022)

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Modelling gene expression evolution in fireflies

Gene expression is a key driver of trait variation, particularly among closely related species. This project aims to develop innovative methods to model gene expression evolution using Brownian motion and Ornstein-Uhlenbeck processes. Specially, we focus on within-species variance, a critical yet underexplored aspect of gene expression. The firefly family (Lampyridae) serves as a novel study system due to their recurrent sexual dimorphism across the phylogeny. As sexual dimorphism is inherently linked to sex-biased gene expression, this makes fireflies an ideal model for investigating sex-biased gene expression evolution.

Aktuelle Forschungsprojekte

Die genomische Grundlage von extremen Sexualdimorphismus bei Glühwürmchen (DFG, priority programm SPP 2349 GEvol , seit 2023)

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Macroevolutionary dynamics and biodiversity

Biodiversity is modeled by the process of speciation and extinction. There is clear evidence from both living and extinct species that biodiversity is extremely variable through time and among species. However, we still do not know what factors drive speciation and extinction rates on a macroevolutionary level (that is, beyond species boundaries). The goal of this project is to combine statistical, computational, neontological – i.e. relating to species living today – and paleobiological approaches to study macroevolutionary dynamics.

Aktuelles Forschungsprojekt

Which Factors Drive Macroevolutionary Rates of Speciation and Extinction (MacDrive) (EU ERC Starting Grant, seit 2023)

RevBayes - Bayesian phylogenetic inference using probabilistic graphical models and an interpreted language

Bayesian phylogenetic inference using probabilistic graphical models and an interpreted language. RevBayes provides a flexible framework for performing Bayesian statistical analysis of phylogeny and related topics, such as divergence time estimation, diversification rate estimation, continuous and discrete trait evolution, and historical biogeography.

TESS - Diversification Rate Estimation and Fast Simulation of Reconstructed Phylogenetic Trees

An R package for Diversification Rate Estimation and Fast Simulation of Reconstructed Phylogenetic Trees under Tree-Wide Time-Heterogeneous Birth-Death Processes Including Mass-Extinction Events. TESS both provides the likelihood function as well as reversible-jump method to estimate global diversification rates and rate shifts.

Pesto.jl - Phylogenetic Estimation of Shifts in the Tempo of Origination

A module for Phylogenetic Estimation of Shifts in the Tempo of Origination. Under the birth-death-shift process (Höhna et al. 2019, biorxiv), the diversification rate is allowed to shift across the phylogeny, where branch-specific diversification rates, as well as the number of rate shifts can be inferred. Pesto is implemented using an efficient estimation algorithm, allowing for fast inferences even for large phylogenies with thousands of species.

Convenience - convergence diagnostics in Bayesian Phylogenetic MCMC

An R package for convergence diagnostics in Bayesian Phylogenetic MCMC. This R package helps in convergence assessment for phylogenetic inference not only in continuous parameters but also in the trees sampled as well. More information and a tutorial on convergence assessment using Convenience can be found in the