Efficient Algorithms for Liquid Chromatography Coupled Mass Spectrometry Based Protein Quantification

Abstract:

Identification of genes and genetic pathways affected in disease and
disease treatment is one of the driving aims of studies that conduct
comprehensive, quantitative surveys of proteins across many experimental
samples and replicates. The prevailing tool for conducting such surveys
is a class of experimental techniques and 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 measurements of proteins, their peptide
fragments, or enrichment and depletion of their post-translational
modifications presents a substantial computational challenge. This
thesis describes a new application of space partitioning data structures
and a series of algorithms that leverage the fast geometric 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 of 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 with respect to their quantitative accuracy and
computational efficiency on a wide range of experimental data sets
spanning several experimental methodologies and source protein samples.