Funari Laboratory

Pictured from left to right: Jordan Brown; Lindsay Spurka; Vincent Funari, PhD; Jie (Jay) Tang, PhD; Quoclinh Nguyen (not pictured).

The Funari Labatory is focused on custom approaches and solutions to genomic questions. Our work includes developing new wet lab techniques and bioinformatic approaches to analyzing genomic information. We specialize in bioinformatic analysis of NGS data for microbiome profiling, RNA expression profiling, and exome analysis. Our novel genomic approaches have led to identifying mutations that cause disease, establishing candidate disease genes using expression profiling, and identifying candidate miRNAs involved in diabetes, as well as identifying fungal and bacterial profiles associated with gut diseases.

The Funari Laboratory is affiliated with the Medical Genetics Institute and Department of Biomedical Sciences.

 

Molecular Basis of Diaphanospondylodysostosis

(A) NGS sequencing coverage for exon 9 of BMPER
(B) The zoomed panel of the region boxed in (A)
(C) Confirmation of the mutation by Sanger sequence analysis

(Funari, 2010)

 

 

 

 

 

 

  
  

Mouse Mycobiome Analysis

DNA was isolated from murine feces, and mycobiome analysis was performed using Roche 454 and Illumina GA sequencing of ITS1-2 rDNA. The taxonomic distribution of the most abundant fungal genera is shown (large pie chart), and the species breakdowns for major groups are provided (small pie charts).

 

 

 

 

 

  
  

Analysis of disease-selective probesets

Gene-gene correlations were identified previously for all cartilage-selective probesets. Dendrograms from two-way clustering of the median-centered correlation data suggest three distinct expression patterns. The strongest node (blue) was selected as a cartilage profile for further expansion by seeding the UGET.

 

 

 

 

 

 

  
  

Unsupervised analysis of all tissues using U133 2.0 microarrays

Two-way hierarchical clustering of 46 normal tissues (including five fetal cartilage and 41 non-cartilage tissues) and 9483 probesets which vary more than two standard deviations from the mean expression of the probeset.

(Funari, 2007)

  
 

An overview of VNB algorithm presented as a flow chart. A box represents either input data or a result. An oval represents an action. A diamond represents a decision point. VNB program.

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