Bioinformatický seminár

Tue 13 Dec. 2011, 17:20

Title: Noy et al. Shape-Based Feature Matching Improves Protein Identification via LC-MS and Tandem MS
Speaker: Barbora Morávková, Jarka Jančigová

Abstract The characterization of proteins via liquid chromatography-mass
spectrometry (LC-MS) and tandem MS is a challenge due to the large dynamic
range and the high complexity of the molecules of interest. In LC-MS
experiments, the inconsistent variation in the travel time of analytes in
the LC column results in nonlinear shifts in the LC retention time (RT).
This variability must be corrected to accurately match corresponding
peptide features across samples in LC-MS experiments. Standard methods for
RT alignment applied to the raw data are computationally expensive, making
it impractical to process a large number of samples. More successful
algorithms perform the alignment on features that matched across
experiments based on pre-specified mass and RT windows. Features that
match across multiple experiments are more likely to be true positives
and, therefore, will be more suitable to drive the alignment correction.
However, depending on the feature matching algorithm, ambiguities can
arise when more than one candidate feature match falls within the
specified windows which might affect the alignment performance. In
addition, some of the feature-based alignment algorithms do not correct
for nonlinear RT shifts. We propose a novel feature matching algorithm
that incorporates wavelet-based shape information about the features. We
tested our algorithm on two different applications of MS. First, we
combined the feature matching algorithm with a robust nonparametric
kernel-type regression to form a nonlinear feature-based alignment
framework for LC-MS experiments. We validated our alignment framework on
LC-MS data from complex samples with known spiked-in proteins,
demonstrating our ability to correctly identify each of them with higher
reproducibility and probability score when comparing with the SuperHirn
software. In addition, by using our feature-based alignment framework, we
were able to increase the number of matched features and improve the
correlation between replicates. Second, we tested our feature matching
algorithm on MALDI MS with MS/MS acquisitions. We found that using only
features that matched across replicates of tandem mass spectra we could
improve the identification of peptides compared with the current
state-of-the-art software. Supplementary Material is available online at .