Morganti, Josh

Morganti Lab Research Overview and Computational needs (Fall 2022) 

PI: Josh Morganti 

Data scientist: Sangderk Lee 

Graduate Students: TBD 

High-throughput single cell-based transcriptomics, genomics, metabolomics, and network analysis on neurodegenerative disease study 

Specific Aim: determine how aging alters glial responses to trauma, inflammation, and neurodegeneration

Description: We produce scRNA-Seq, spatial transcriptomics, and genome accessibility dataset to identify fundamental pathological mechanisms, drivers, and targets of Alzheimer’s disease and post-traumatic brain injury.  

Personnel:

Josh Morganti (PI)

Sangderk Lee (Data scientist), Added on MCC cluster, 11/10/2022 

Kai Saito, Bioinfromatics Analyst, Added on MCC cluster, 07/11/2023 

Graduate Students:

Itika Arora, Added on MCC cluster 03/17/2023 

Computational methods:

Sequence read processing, genome mapping and annotation, network analysis, and differentially expressed gene (DEG) analysis. The methods are currently available.

Software:

Unix/R/python-based software 

• cellranger-arc (https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/pipelines/latest/what-is-cell-ranger-arc) 
• cellranger (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) 
• spaceranger (https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger) 
• STAR (https://github.com/hbctraining/Intro-to-rnaseq-hpc-O2) 
• DESeq2 (https://bioconductor.org/packages/release/bioc/html/DESeq2.html) 
• pySCENIC (https://pyscenic.readthedocs.io/en/latest/installation.html) 
• Seurat (https://satijalab.org/seurat/) 
• Signac (https://stuartlab.org/signac/) 

Software availability: All programs are accessible freely online