Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


osu1187113396.pdf (5.88 MB)

ETD Abstract Container

Abstract Header

Novel data analysis methods and algorithms for identification of peptides and proteins by use of tandem mass spectrometry

Xu, Hua
http://rave.ohiolink.edu/etdc/view?acc_num=osu1187113396

Abstract Details

2007, Doctor of Philosophy, Ohio State University, Chemistry.
Tandem mass spectrometry is one of the most important tools for protein analysis. This thesis is focused on the development of new methods and algorithms for tandem mass spectrometry data analysis. A database search engine, MassMatrix, has also been developed that incorporates these methods and algorithms. The program is publicly available both on the web server at www.massmatrix.net and as a deliverable software package for personal computers. Three different scoring algorithms have been developed to identify and characterize proteins and peptides by use of tandem mass spectrometry data. The first one is targeted at the next generation of tandem mass spectrometers that are capable of high mass accuracy and resolution. Two scores calculated by the algorithm are sensitive to high mass accuracy due to the fact that this new algorithm explicitly incorporates mass accuracy into scoring potential peptide and protein matches for tandem mass spectra. The algorithm is further improved by employing Monte Carlo Simulations to calculate ion abundance based scores without any assumptions or simplifications. For high mass accuracy data, MassMatrix provides improvements in sensitivity over other database search programs. The second scoring algorithm based on peptide sequence tags inferred from tandem mass spectra further improves the performance of MassMatrix for low mass accuracy tandem mass spectrometry data. The third algorithm is the first automated data analysis method that uses peptide retention times in liquid chromatography to evaluate potential peptide matches for tandem mass spectrometry data. The algorithm predicts reverse phase liquid chromatography retention times of peptides by their hydrophobicities and compares the predicted retention times with the observed ones to evaluate the peptide matches. In order to handle low quality data, a new method has also been developed to reduce noise in tandem mass spectra and screen poor quality spectra. In addition, a data analysis method for identification of disulfide bonds in proteins and peptides by tandem mass spectrometry data has been developed and incorporated in MassMatrix. By this new approach, proteins and peptides with disulfide bonds can be directly identified in tandem mass spectrometry with high confidence without any chemical reduction and/or other derivatization.
Michael Freitas (Advisor)
288 p.
Chemistry, Analytical
Mass spectrometry; Proteomics; Database search; Data analysis; Bioinformatics

Recommended Citations

Citations

  • Xu, H. (2007). Novel data analysis methods and algorithms for identification of peptides and proteins by use of tandem mass spectrometry [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1187113396

    APA Style (7th edition)

  • Xu, Hua. Novel data analysis methods and algorithms for identification of peptides and proteins by use of tandem mass spectrometry. 2007. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1187113396.

    MLA Style (8th edition)

  • Xu, Hua. "Novel data analysis methods and algorithms for identification of peptides and proteins by use of tandem mass spectrometry." Doctoral dissertation, Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1187113396

    Chicago Manual of Style (17th edition)

osu1187113396
1,106
© 2007, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.