News
Phylogenetic tree inference from single-cell RNA sequencing data with SCITE-RNA
SCITE-RNA is our new method for phylogenetic tree inference from single-cell RNA sequencing data. By alternating between cell lineage and mutation tree representations, it can escape local optima and allows us to link phylogenies and gene expression profiles. Read the article: https://rdcu.be/fot5N
Wastewater-based sequencing of respiratory syncytial virus to investigate lineage dynamics and antigenic site mutations: a retrospective genomic epidemiology study
We are excited to share that our paper “Wastewater-based sequencing of respiratory syncytial virus to investigate lineage dynamics and antigenic site mutations: a retrospective genomic epidemiology study” is now published in The Lancet Microbe. You can check out our published paper under the link: https://www.sciencedirect.com/science/article/pii/S2666524726000108
ETH Medal 2025
Congratulations to Xiang Ge Luo for winning the ETH Medal 2025 for her doctoral thesis, “Modeling tumor mutation trees for evolution-guided precision oncology”! https://www.research-collection.ethz.ch/entities/publication/a70802ed-0912-491f-86fb-0faf06281c93
Learning and forecasting selection dynamics of SARS-CoV-2 variants from wastewater sequencing data using Covvfit
We are excited to share that Covvfit is now published in Water Research. Covvfit is a method to estimate the selective advantage of viral variants using wastewater sequencing data. Using data from the Swiss SARS-CoV-2 surveillance, we show that it can reliably and efficiently estimate the advantage of emerging variants and forecast their spread. Read the full paper at https://doi.org/10.1016/j.watres.2026.126018.