LABORATORY

Our lab performs a continuum of wet and dry laboratory methods in support of molecular, genomic, and data science analytics used for health science and basic science research.

Molecular (Wet) Lab

Our lab performs a variety of molecular techniques to support our research projects, including:

  • Training students, Post-docs, faculty on molecular techniques and study design
  • Specimen processing (Biohazard risk group 2)
  • Specimen banking (>6000 patients, >20,000 specimens)
  • Specimen types (blood, hair, saliva, urine)
  • Patient populations (oncology, diabetes, cardiovascular disease, HIV, substance use, preterm birth, mental health disorders)
  • Isolation of nucleic acid (total RNA, small RNA, DNA), serum, plasma from blood
  • Host (Human) and virus (HIV) nucleic acid processing
  • Gel electrophoresis
  • PCR
  • qPCR
  • ELISA
  • Sanger sequencing (using core facility)
  • NextGen library preparation and sequencing (DNAseq, RNAseq using core facility)
  • Custom and whole-genome microarray (i.e., genotype, transcriptome, and methylome using core facility)
  • Core facilities used: UCSF Genomics core (now defunct),  UC Berkeley DNA Sequencing Facility, UC Berkeley/QB3 Functional Genomics Laboratory and  Vincent J. Coates Genomics Sequencing Laboratory, UC Davis Genome Center DNA Technologies and Expression Analysis Core Facilities.

Computational (Dry) Lab

We have a modest but powerful computational infrastructure consisting of compute nodes, storage nodes, and administrative (e.g., monitoring and backup) servers. 

Our lab applies and develops a variety of computational approaches, including:

  • Training students, Post-docs, faculty on bioinformatics techniques and study design
  • High throughput ‘Omics data collection, storage, backup, wrangling, and retrieval
  • Microarray data processing
  • DNAseq/RNAseq alignments
  • Variant calling (e.g., GATK, MARSS)
  • Population genetics (GWAS, candidate gene associations, shinyGAStool )
  • Population epigenetics (methylation)
  • Whole-transcriptome differential gene expression and pathway analysis
  • Data-integrated ‘omics analyses (e.g., transcriptome and methylation)
  • Data analyses pipelines
  • Comparative Genomics
  • Phylogenomics
  • Machine Learning