Zia Khan will present his preFPO on Friday, July 23, 2010 at 1:30pm in Carl Ichan Lab (CIL) 200. The members of his committee are: Mona Singh (CS) advisor, Leonid Kruglyak (EEB) advisor, Ben Garcia (MOL) reader, Hillary Coller (MOL), and Saeed Tavazoie (MOL) nonreaders. Everyone is invited to attend his talk. His abstract follows below. Title: Efficient Algorithms for Liquid Chromatography Coupled Mass Spectrometry Based Protein Quantification Abstract: One of the driving aims of studies that conduct comprehensive, quantitative surveys of proteins and across many experimental samples and replicates is the identification of genes and genetic pathways effected in disease and disease treatment. The prevailing experimental tool for such surveys is instrumentation known as liquid chromatography coupled tandem mass spectrometry (LC-MS/MS). LC-MS/MS generates large data sets in the form of thousands to millions of mass spectra in a single experiment. Converting these spectra into interpretable quantitative output that shows changes in amounts of proteins, their peptide fragments, or enrichment and depletion of post translational modifications presents a substantial computational challenge. This thesis describes a new application of space partitioning data structures: a series of algorithms that leverage fast geometry queries supported by these data structures to significantly improve the speed and quality LC-MS/MS data analysis. In addition, this thesis develops a collection methods, implemented in an open-source software system called PVIEW (http://compbio.cs.princeton.edu/pview), that use the output of these algorithms to enable accurate quantification of proteins, protein fragments, and post translational modifications. These methods are evaluated for quantitative quality and computational efficiency on a wide range of experimental data sets spanning several experimental methodologies and source protein samples.