Pinned Repositories
create-pptc-pdx-corplots
create-pptc-pdx-oncoprints
As part of an overall strategy for improving therapies for childhood cancers, the PPTC seeks to develop models for the types of tumors that will be encountered in early phase clinical testing by establishing patient derived xenografts (PDXs) from high-risk childhood cancers refractory to current standard of care treatments. Genomic profiling of these models is required to enable PPTC investigators to develop robust "responder hypotheses" when drug activity is observed. With funding provided by Alex's Lemonade Stand Foundation, we genomically characterize a major subset of 286 PDX models. We use whole exome sequencing, transcriptome sequencing, and SNPArray to characterize the tumor models. The focus on DNA and RNA sequencing data mirrors the current standard practice in most clinical diagnostics lab that use these technologies to detect the spectrum of targetable mutations, gene amplifications, and gene fusion events relevant to preclinical drug development.
epigenomics-data-descriptor
mosse-lab-tools
Various QoL tools and script snippets to aid researchers in the Mosse lab
NBL-cell-line-RNA-seq
pdx-classification
Applying Machine Learning Ras, NF1, and TP53 Classifiers to PDX model gene expression
pdx-mouse-subtraction
pptc-pdx-RNA-Seq-clustering
pptx-pdx-fusion-analysis
Fusion pipeline and figures
sCRAP
selective Cross Reactive Antigen Presentation
marislab's Repositories
marislab/epigenomics-data-descriptor
marislab/pdx-mouse-subtraction
marislab/create-pptc-pdx-oncoprints
As part of an overall strategy for improving therapies for childhood cancers, the PPTC seeks to develop models for the types of tumors that will be encountered in early phase clinical testing by establishing patient derived xenografts (PDXs) from high-risk childhood cancers refractory to current standard of care treatments. Genomic profiling of these models is required to enable PPTC investigators to develop robust "responder hypotheses" when drug activity is observed. With funding provided by Alex's Lemonade Stand Foundation, we genomically characterize a major subset of 286 PDX models. We use whole exome sequencing, transcriptome sequencing, and SNPArray to characterize the tumor models. The focus on DNA and RNA sequencing data mirrors the current standard practice in most clinical diagnostics lab that use these technologies to detect the spectrum of targetable mutations, gene amplifications, and gene fusion events relevant to preclinical drug development.
marislab/pdx-classification
Applying Machine Learning Ras, NF1, and TP53 Classifiers to PDX model gene expression
marislab/create-pptc-pdx-corplots
marislab/NBL-cell-line-RNA-seq
marislab/pptx-pdx-fusion-analysis
Fusion pipeline and figures
marislab/sCRAP
selective Cross Reactive Antigen Presentation
marislab/create-pptc-pdx-pie
Pie chart for Figure 1 of Manuscript
marislab/maftools
Summarize, Analyze and Visualize MAF files from TCGA or in house studies.
marislab/mosse-lab-tools
Various QoL tools and script snippets to aid researchers in the Mosse lab
marislab/pptc-pdx-RNA-Seq-clustering
marislab/pptc-pdx-tmb
marislab/gmkf-nbl-fusion-analysis
Fusion analysis for GMKF Neuroblastoma
marislab/immunoprofile_shinyapp
Shiny app for immunoprofiling across pediatric and adult tumors
marislab/pptc-pdx-classifier-analysis
marislab/pptc-pdx-copy-number-and-SVs
Indel and Breakpoints plots
marislab/pptc-pdx-ethnicity-inference
Ethnicity inference for Pediatric Preclinical Testing Consortium (PPTC) patient-derived xenograft models
marislab/pptc-pdx-mut-sigs
marislab/sCRAP_CommandLine
selective Cross Reactive Antigen Presentation (command line version)
marislab/tad_pathways_pipeline
Pipeline to implement a "TAD_Pathways" analysis. Discover candidate genes based on association signals in TADs