Skip to main content

Current Projects

clinical proteomic.jpg

Cancer Proteogenomics

In the CPTAC consortium, we characterize tumors with both proteomic and genomic data. We use multi-omics data to reveal new hints at cancer therapies, like using DNA copy number alterations and their trans-effected proteins to create a signature of survival in ovarian cancer(Cell 2016). We are also interested in the function of post-translational modifications and how these intersect with DNA mutations. Finally, we are dedicated to open science and data sharing technologies.

Key Questions
  1. What mutations and proteome state are correlated with a good survival in uterine and endometrial cancer?
  2. How do mutations affect the behavior of post-translational modifications?
  3. Are protein complexes dysregulated in a consistent manner across tumor types?

Machine Learning and Forensics

Bioinformatics can be used to identify a victim, criminal, or unknown sample. A wide variety prosecutorial needs are not met by DNA evidence, and could be solved by proteomic evidence. Here, there exists less of a data and statistical framework for how one measures accuracy and validity, i.e. what would be admissible in a court of law (JPR 2018).

Key Questions
  1. How can we measure and characterize human proteome variability?
  2. What is a statistically confident identification in a legal situation?
  3. Can we distinguish different human tissues at crime scenes?

Mass Spectrometry Identification

I have a sustained interest in fundamental algorithms for the identification of mass spectra. - phosphorylated peptides (JPR 2008)- proteogenomic integration (PNAS 2008, PLoS One 2011, Cell 2016)- data quality improvements (JPR2014, JASMS 2014, Bioinformatics 2015)- lipids (Bioinformatics 2017).- intact proteins (Nature Methods 2017), - de novo peptide identification. (bioRxiv)

Key Questions

  1. How can we be more confident in spectral identifications?
  2. How can we identify spectra faster?
  3. Will metabolomics ever be solved?