Pelin Icer Baykal

This study evaluates the reproducibility of eight read alignment tools using synthetic replicates, finding significant variability in their performance and impact on downstream variant calling.

The paper evaluates nine computational tools for identifying SARS-CoV-2 lineages in wastewater sequencing data using simulated samples, finding most tools reliably detect lineages above 5% frequency.

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

SOPHIE reconstructs viral transmission networks using a maximum likelihood framework integrating phylogenetic and random graph models, without requiring case-specific epidemiological data.

The paper compares intra-host HCV population characteristics between recent and persistent infections, finding significant differences and developing a machine learning classifier with 95.22% accuracy.

The paper normalizes heterogeneous genomic data into images and applies machine learning to classify viral infection stages and detect transmission clusters with high accuracy.