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’).

The open-source Computational Proteomics Analysis System (CPAS) contains an entire data analysis and management pipeline for Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) proteomics, including experiment annotation, protein database searching and sequence management, and mining LC-MS/MS peptide and protein identifications. CPAS architecture and features, such as a general experiment annotation component, installation software, and data security management, make it useful for collaborative projects across geographical locations and for proteomics laboratories without substantial computational support.

Multiple approaches for simplifying the serum proteome have been described. These techniques are generally developed across different laboratories, samples, mass spectrometry platforms, and analysis tools. Hence, comparing the available schemes is impossible from the existing literature because of confounding variables. We describe a head-to-head comparison of several serum fractionation schemes, including N-linked glycopeptide enrichment, cysteinyl-peptide enrichment, magnetic bead separation (C3, C8, and WCX), size fractionation, protein A/G depletion, and immunoaffinity column depletion of abundant serum proteins. Each technique was compared to results obtained from unfractionated human serum. The results show immunoaffinity subtraction is the most effective means for simplifying the serum proteome while maintaining reasonable sample throughput. The reported dataset is publicly available and provides a standard against which emergent technologies can be compared and evaluated for their contribution to serum-based biomarker discovery.