Motivation: Comparing two or more complex protein mixtures using liquid chromatography mass spectrometry (LC-MS) requires multiple analysis steps to locate and quantitate natural peptides within a single experiment and to align and normalize findings across multiple experiments. Results: We describe msInspect, an open-source application comprising algorithms and visualization tools for the analysis of multiple LC-MS experimental measurements. The platform integrates novel algorithms for detecting signatures of natural peptides within a single LC-MS measurement and combines multiple experimental measurements into a peptide array, which may then be mined using analysis tools traditionally applied to genomic array analysis. The platform supports quantitation by both label-free and isotopic labeling approaches. The software implementation has been designed so that many key components may be easily replaced, making it useful as a workbench for integrating other novel algorithms developed by a growing research community. Availability: The msInspect software is distributed freely under an Apache 2.0 license. The software as well as a Zip file with all peptide feature files and scripts needed to generate the tables and figures in this article are available at Contact:  mmcintos@fhcrc.org Supplementary Information: Supplementary materials are available at (select ‘Published Experiments’ from the list of Projects and then ‘msInspect Paper’).

Array-based comparative genomic hybridization (array-CGH) provides a high-throughput, high-resolution method to measure relative changes in DNA copy number simultaneously at thousands of genomic loci. Typically, these measurements are reported and displayed linearly on chromosome maps, and gains and losses are detected as deviations from normal diploid cells. We propose that one may consider denoising the data to uncover the true copy number changes before drawing inferences on the patterns of aberrations in the samples. Nonparametric techniques are particularly suitable for data denoising as they do not impose a parametric model in finding structures in the data. In this paper, we employ wavelets to denoise the data as wavelets have sound theoretical properties and a fast computational algorithm, and are particularly well suited for handling the abrupt changes seen in array-CGH data. A simulation study shows that denoising data prior to testing can achieve greater power in detecting the aberrant spot than using the raw data without denoising. Finally, we illustrate the method on two array-CGH data sets.

Posttranslational modification (PTM) of proteins is likely to be the most common mechanism of altering the expression of genetic information. It is essential to characterize PTMs to establish a complete understanding of the activities of proteins. Here, we present a sensitive detection method using surface-enhanced Raman spectroscopy (SERS) that can detect PTMs from as little as zeptomoles of peptide. We demonstrate, using model peptides, the ability of SERS to detect a variety of protein modifications, such as acetylation, trimethylation, phosphorylation, and ubiquitination. In addition, we show the capability to obtain positional information for modifications such as trimethylation and phosphorylation using SERS and wavelet decomposition data analysis techniques. We further show that it is possible to apply SERS to detect PTMs from biological samples such as histones. We envision that this detection method might be a valuable technique that is complementary to mass spectrometry in obtaining orthogonal chemical and modification-specific information from biological samples at sensitive levels.