Computational and Statistical Genomics

Overview

Slingshot

Our faculty develop sophisticated computational and statistical methods to extract robust biological knowledge from the massive genomic datasets generated by modern sequencing technologies. This group pioneers approaches that address fundamental challenges in genomics—from identifying functional genetic variants and understanding gene regulation to mapping cellular heterogeneity and decoding the architecture of complex traits. Research encompasses the full spectrum of genomic analysis, including developing algorithms for sequence alignment and variant calling, creating statistical frameworks for differential expression analysis and regulatory network inference, pioneering methods for ribosome profiling to measure translation transcriptome-wide, designing clustering approaches and trajectory inference methods for single-cell and spatial transcriptomics, building models that link genetic variation to phenotype, comparative genomics of plant immune receptors and pathogen effectors, gut microbiome structure and function, and integrating heterogeneous data sources with biological annotation metadata.

The impact spans human disease (identifying causal mutations and therapeutic targets, understanding post-transcriptional gene regulation), crop improvement (engineering disease resistance in wheat and other crops, discovering agronomic trait genes), precision medicine (connecting single-cell profiles to patient health outcomes, developing novel cell type discovery methods), and basic biology (understanding how genomes encode and regulate cellular function, from translation dynamics to stem cell differentiation). By combining rigorous statistical theory with innovative computational approaches and reproducible research practices—exemplified by Bioconductor's open-source tools for genomic data analysis—our researchers transform raw sequencing data into actionable insights about gene function, disease mechanisms, evolutionary processes, and agricultural applications. With access to cutting-edge experimental technologies including ribosome profiling, single-cell RNA-seq, spatial transcriptomics, and CRISPR-based functional genomics screens, and rich collaborative networks across Berkeley's biology, statistics, computer science, and public health departments, this work creates the analytical foundations that enable genomic discoveries with direct applications to precision medicine, food security, and our fundamental understanding of how genomes work.

Primary Faculty

  • Adam Arkin, Professor, Department of Bioengineering
  • Steven Brenner, Professor, Departments of PMB, MCB, and Bioengineering
  • Sandrine Dudoit, Professor, Department of Statistics and Division of Biostatistics
  • Leah Guthrie, Assistant Professor, Department of Bioengineering
  • Ian Holmes, Professor, Department of Bioengineering
  • Haiyan Huang, Professor, Department of Statistics
  • Nicholas Ingolia, Professor, Department of MCB
  • Ksenia Krasileva, Associate Professor, Department of Plant and Microbial Biology
  • Priya Moorjani, Associate Professor, CCB and Department of MCB
  • Rasmus Nielsen, Professor, Departments of Integrative Biology and Statistics
  • Elizabeth Purdom, Associate Professor, Department of Statistics
  • Karthik Shekhar, Assistant Professor, Department of CBE
  • Yun S. Song, Professor, Departments of EECS and Statistics
  • Aaron Streets, Associate Professor, Department of Bioengineering
  • Peter Sudmant,  Associate Professor, Department of Integrative Biology
  • Allon Wagner, Assistant Professor, Departments of EECS and MCB
  • Ashley Wolf, Assistant Professor, CCB and Division of Infectious Diseases and Vaccinology

Secondary Faculty

  • Rachel Brem, Professor, Department of Plant and Microbial Biology
  • Liana Lareau, Associate Professor, Department of Bioengineering
  • Michael Nachman, Professor, Department of Integrative Biology
  • Jingshen Wang, Associate Professor, Division of Biostatistics

Affiliated Faculty

Professors of the Graduate School