University of California-Berkeley · EngineeringSong Lab
Yun S Song · Berkeley, California
Research Focus
Dr. Yun S. Song's lab focuses on developing advanced computational methods to analyze and interpret large genomic data sets. This research aims to enhance our understanding of molecular evolution and genetic variations across different timescales, especially in relation to human health. The lab works on creating user-friendly software tools that can help biomedical researchers leverage genomic data for significant discoveries, particularly in cancer biology and immune responses.
computational methodsgenealogical inferencegenomic datamolecular evolutionCRISPRphylogenetic analysissingle-cell genomicsantibody maturationcancer evolution
Research Aims
4AIM 01Develop scalable computational methods for estimating model parameters in molecular evolution.
AIM 02Create robust algorithms for accurate genealogical inference from genomic data.
AIM 03Design user-friendly software tools for the biomedical research community.
AIM 04Apply methods to analyze human genetic data and improve disease understanding.
Skills the Lab Values
Python programmingstatistical modelingmaximum likelihood estimationdata analysisgenealogical tree reconstructionCRISPR/Cas9 techniquesgenomic data analysissoftware developmentalgorithm optimization
Output against the department
Publications312top 27% of dept.74th of 278
this lab
most publishedleast published
Citations14ktop 27% of dept.74th of 278
this lab
most citedleast cited
Career totals from OpenAlex, compared against labs that share a department. A department spans everything from one paper to tens of thousands, so bar heights are on a log scale — otherwise the largest lab would flatten every other bar to a line.