The translation of a mass spectrum into a chemical identification is often a very imprecise and error prone part of the rapidly expanding fields of mass spectrometry-based proteomics and metabolomics. Our studies focus on developing new mass spectral data analysis algorithms (e.g., clustering) to better solve the common key persistent problems arising from factors such as mass shift and peak intensity variation; noise perception and removal, and ambiguous identifications. We are interested now in exploring the techniques to tackle the problems associated with fragmentation pathway or structure-spectrum correlation from MSn (MS2, MS3, and MS4) spectra. Available resources include NIST Tandem Mass Spectral Library and terabytes of mass spectral data that are stored at our mass spectrometry data center. Applicants are expected to be skilled in one of the programming language such as C++/C, Perl, Matlab, or R, and have majored in Chemistry, Statistics, or Computer Science.

 

Reference

Yang X, et al: Analytical Chemistry 86(13): 6393-6400, 2014

 

key words

Mass spectrometry; Data analysis; Algorithm; Clustering; MS/MS; Proteomics; Metabolomics; Compound identification; Data mining;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants