A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet

Abstract PeptideProphet is a post-processing algorithm designed to evaluate the confidence in identifications of MS/MS spectra returned by a database search. In this manuscript we describe the "what and how" of PeptideProphet in a manner aimed at statisticians and life scientists who would like to gain a more in-depth understanding of the underlying statistical modeling. The theory and rationale behind the mixture-modeling approach taken by PeptideProphet is discussed from a statistical model-building perspective followed by a description of how a model can be used to express confidence in the identification of individual peptides or sets of peptides. We also demonstrate how to evaluate the quality of model fit and select an appropriate model from several available alternatives. We illustrate the use of PeptideProphet in association with the Trans-Proteomic Pipeline, a free suite of software used for protein identification.http://deepblue.lib.umich.edu/bitstream/2027.42/112836/1/12859_2012_Article_5421.pd

High-resolution genome-wide scan of genes, gene-networks and cellular systems impacting the yeast ionome

Peer reviewedPublisher PD

Spatial Segmentation and Feature Selection for Desi Imaging Mass Spectrometry Data with Spatially-Aware Sparse Clustering.

Recent experimental advances in matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) have demonstrated the usefulness of these technologies in the molecular imaging of biological samples. However, development of computational methods for the statistical interpretation and analysis of the chemical differences present in the distinct regions of these samples is still a major challenge. In this poster, we propose statistically-minded methods and computational tools for analyzing DESI imaging experiments. Specifically, we present techniques for signal processing and unsupervised multivariate image segmentation, which are also applicable to other imaging mass spectrometry (IMS) methods such as MALDI

A computational framework for the inference of protein complex remodeling from whole-proteome measurements

Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets

Apolipoprotein E-Mimetics Inhibit Neurodegeneration and Restore Cognitive Functions in a Transgenic Drosophila Model of Alzheimer's Disease

BACKGROUND: Mutations of the amyloid precursor protein gene (APP) are found in familial forms of Alzheimer's disease (AD) and some lead to the elevated production of amyloid-beta-protein (Abeta). While Abeta has been implicated in the causation of AD, the exact role played by Abeta and its APP precursor are still unclear. PRINCIPAL FINDINGS: In our study, Drosophila melanogaster transgenics were established as a model to analyze AD-like pathology caused by APP overexpression. We demonstrated that age related changes in the levels and pattern of synaptic proteins accompanied progressive neurodegeneration and impairment of cognitive functions in APP transgenic flies, but that these changes may be independent from the generation of Abeta. Using novel peptide mimetics of Apolipoprotein-E, COG112 or COG133 proved to be neuroprotective and significantly improved the learning and memory of APP transgenic flies. CONCLUSIONS: The development of neurodegeneration and cognitive deficits was corrected by injections of COG112 or COG133, novel mimetics of apolipoprotein-E (apoE) with neuroprotective activities