Laboratory

We are a joint lab with Dr. Kord Kober.

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

SON Investigators

Our group of interdisciplinary researchers are spread across the School of Nursing and represent a diverse set of research programs.

  •     Elena Flowers
  •     Kord Kober
  •     Christine Miaskowski
  •     Heather Leutwyler
  •     Monica McLemore
  •     Annesa Flentje
  •     Glenn-Milo Santos
  •     Xiao Hu

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)
  •     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