Analytical methods and tools for processing mass spectrometry-based proteomics data are still in early stages of development compared to other ‘omics fields. For this reason, we sought to develop an accessible online application to facilitate normalization, basic statistical testing, and biological interpretation of mass-spec based proteomics data. GiaPrOnto (Graphical Interpretation and Analysis of Proteins and their Ontologies) is an R-Shiny application written to fill this void (Weiner, et al, Mol. Cell. Proteom. 2018). Input to GiaPrOnto can be the direct output of MaxQuant protein or PTM (commonly used protein quantification software), or any properly formatted text file. GiaPrOnto performs normalization, basic statistical tests (e.g. t-test), gene ontology (GO) evaluation, and provides visualization of sample features and results. Each figure can be exported in high resolution (right click) and includes a comprehensive legend to assist in its interpretation. Currently, supported Gene Ontology includes human, mouse, worm, fly, yeast and Arabidopsis. For questions, suggestions or bug reports, please contact Amber K. Weiner (email@example.com).
High throughput DNA sequencing has revolutionized the study of both normal and disease tissues, including cancer. However, batch effects and other potential artifacts can hinder the accurate identification of copy number variations (CNVs) from DNA sequencing data. Issues that complicated CNV detection from single nucleotide polyporphism (SNP) array data can affect the analysis of DNA sequencing data (e.g. GC content). We therefore developed a novel method for improving normalization and CNV detection from exome sequencing data (Jiang et al, Nucleic Acids Res. 2015). This work was spearheaded by Yuchao Jiang while he was a graduate student rotating in our lab and was completed in collaboration with Dr. Nancy Zhang at Wharton. Version 2 has since been released by Dr. Jiang who is now a faculty member at the University of North Carolina.
The Diskin Lab Github provides public repositories for our code and pipelines, for example:
- Inferring ethnicity from genetic variant data
- Analysis of structural variations (SVs)
Visit Diskin Lab Github