P. S. Pennings

Using a decision tree to predict COVID case numbers: a tutorial for beginners

This paper develops an educational module using decision trees to predict COVID case numbers, aimed at teaching machine learning basics to biology students.

SCIP: a self-paced, community-based summer coding program creates community and increases coding confidence, lessons learned from the pandemic

SCIP provides an online summer coding program for students, enhancing coding confidence and community engagement during the pandemic.

A phylogenetic approach to studying the roles of within-host evolution and between-host transmission of resistance for clinical Escherichia coli infections

This study uses phylogenetic methods to analyze how antibiotic resistance in E. coli evolves within hosts versus spreads between hosts for different antibiotics.

Sequence type diversity amongst antibiotic-resistant bacterial strains is lower than amongst antibiotic-susceptible strains

The paper compares sequence-type diversity between antibiotic-resistant and susceptible bacterial strains across multiple datasets and finds resistant strains are less diverse in most cases.

Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity

The paper analyzes HIV resistance evolution patterns under temporal and spatial drug heterogeneity models, showing resistance develops even after long-term therapy and in predictable sequences.

Reversion is most likely under high mutation supply, when compensatory mutations don't fully restore fitness costs

This study shows that high mutation rates increase reversion to wild type when compensation is partial, but fully compensatory mutations reduce reversion probability.

Assessing in vivo mutation frequencies and creating a high-resolution genome-wide map of fitness costs of Hepatitis C virus

This study estimates in vivo fitness costs of HCV mutations using frequency-based analysis across 195 patients, revealing mutation type and genomic location effects on fitness costs.

Genetic Adaptation in New York City Rats

The paper investigates genetic adaptation in New York City rats by analyzing whole-genome sequences to identify signatures of selection related to urban living.

Evolutionary dynamics in structured populations under strong population genetic forces

This study uses modeling to show how migration and selection interact to create non-monotonic FST patterns in adapting populations and estimates high migration rates in a Simian-HIV population.

Drug resistance evolution in HIV in the late 1990s: hard sweeps, soft sweeps, clonal interference and the accumulation of drug resistance mutations

The paper examines HIV drug resistance evolution in the late 1990s, showing patterns like soft and hard sweeps, clonal interference, and mutation accumulation in treated patients.

The clarifying role of time series data in the population genetics of HIV

This paper uses time series data to show that soft sweeps drove early HIV treatment failure, contrasting with harder sweeps associated with better drugs.

Inferring Population Genetics Parameters of Evolving Viruses Using Time-series Data

This paper introduces FITS, a computational framework for inferring virus population genetics parameters from time-series data, demonstrating high accuracy in classifying mutations as advantageous, neutral, or deleterious.

Soft sweeps and beyond: Understanding the patterns and probabilities of selection footprints under rapid adaptation

The paper examines patterns and probabilities of selection footprints under rapid adaptation, showing that soft sweeps are frequent but complex footprints remain inadequately explained by current models.

Within-patient HIV mutation frequencies reveal fitness costs of CpG dinucleotides, drastic amino acid changes and G->A mutations

The paper estimates the fitness costs of 870 HIV mutations in vivo by analyzing mutation frequencies in the pol gene across 160 patients, finding higher costs for non-synonymous, drastic amino acid changes, and CpG-creating mutations.