Tanja Stadler

This paper introduces a Bayesian framework to improve phylogenetic inference from sparse lineage tracing data by modeling barcode loss, showing it enhances accuracy and recovers higher growth rates than standard filtering methods.

This study evaluates spike clock models in phylogenetics, finding they are supported in most datasets and improve estimates of speciation and extinction rates.

SciPhy estimates time-scaled phylogenies and cell population parameters from sequential genetic lineage tracing data using a Bayesian framework, showing improved accuracy over existing methods.

This paper evaluates the accuracy of inferring cell phylogenies and population dynamics from CRISPR lineage recordings using simulations and Bayesian methods.

The paper characterizes respiratory virus genomic diversity in Switzerland using hybrid-capture sequencing and phylogenetic analysis, identifying prevalent strains and evolutionary insights.

This paper introduces BELLA, a Bayesian neural network framework for modeling complex phylodynamic processes, showing improved accuracy over linear models in capturing nonlinear relationships between predictors and evolutionary rates.

This paper models a sequentially-edited lineage tracing system and identifies parameter regimes where distance-based methods can accurately reconstruct cellular phylogenies.

This study shows that phylodynamic migration rate estimates for HIV-1 within hosts are overestimated under selection using common models like BEAST2.

The paper uses single-cell whole-genome sequencing and Bayesian phylodynamic modeling to show colorectal tumors often originate decades before diagnosis and expand rapidly in the last two years.

The paper extends phylodynamic models to account for age-dependent cell division in developmental biology, showing biases in previous estimates using birth-death models and providing a BEAST2 implementation.

The paper rebuts a critique by showing that re-analyzed data supports the original findings that the transmissible cancer in Tasmanian devils has transitioned to endemism.

The paper quantifies post-sampling RNA degradation in wastewater samples and shows it leads to underestimation of viral concentrations, affecting public health surveillance.

This paper introduces a Bayesian method to infer multi-type population trajectories from genomic data, demonstrating its application to MERS-CoV genomes to estimate human and camel case numbers and spillover events.

This paper introduces the SRFBD model to improve phylogenetic inference by incorporating stratigraphic range data, showing it produces older divergence times and higher sampling proportions compared to the FBD model.

This study analyzes the spread of H5 avian influenza in high-density poultry farms using phylogeographic tools, identifying key factors driving transmission and control thresholds.

The paper estimates within-host mutation rates of ESBL-producing E. coli and K. pneumoniae using phylodynamic models on longitudinal genomic data from hospitalized patients.

EpiFusion improves estimation of outbreak dynamics by integrating phylodynamic and epidemiological data using particle filtering, enhancing accuracy and efficiency in large, poorly sampled epidemics.

The paper introduces a likelihood model for digital PCR data to improve statistical inference in environmental applications by addressing non-normal error structures.

The paper uses genomic data and phylodynamic models to show that Mycobacterium tuberculosis lineages have similar infectiousness durations but differ in onset timing, affecting transmission dynamics.

The paper introduces GABI, a Bayesian framework in BEAST 2 for analyzing lineage tracing data, demonstrating its application to zebrafish development.

This study uses genomic data and a Bayesian phylodynamic model to show HIV co-infection reduces Mycobacterium tuberculosis transmissibility in sub-Saharan Africa.

This paper introduces the Transmission Tree (TnT) model to improve understanding of epidemic dynamics by integrating pathogen gene sequences and clinical data through a Bayesian approach.

V-pipe 3.0 is a computational pipeline for analyzing viral genomic data, enabling reproducible and scalable estimation of within-sample genetic diversity.

TiDeTree estimates time-scaled single-cell phylogenies and population dynamics from genetic lineage tracing data using a Bayesian framework.

The paper compares sequencing and assembly methods' impact on plasmid and chromosomal phylogenies and horizontal gene transfer inference in E. coli.

This paper introduces a phylogeny-aware GWAS framework to correct for heritable pathogen effects on infectious disease traits, showing improved detection of host genetic variants in simulations and applied to HIV-1 data.

This study compares structured coalescent and multitype birth-death models for inferring migration rates between subpopulations using simulated phylogenies, finding MBD outperforms SC in epidemic outbreaks.

The paper examines SARS-CoV-2 genetic diversity within individual patients, finding that a small number of patients carry most of the diversity and that older patients tend to have more diverse viral variants.

This paper introduces BETS, a Bayesian method to evaluate temporal signal in sequence data, showing it accurately classifies datasets with or without temporal signal across various empirical examples.

This study examines how different computational methods affect the analysis of B cell evolution in murine antibody repertoire sequencing data post-viral infection.