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Background: Spectral counting is a shotgun proteomics approach comprising the identification and relative quantitation of thousands of proteins in complex mixtures. However, this strategy generates bewildering amounts of data whose biological interpretation is a challenge. Results: Here we present a new algorithm, termed GO Explorer (GOEx), that leverages the gene ontology (GO) to aid in the interpretation of proteomic data. GOEx stands out because it combines data from protein fold changes with GO over-representation statistics to help draw conclusions. Moreover, it is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. Its usefulness is demonstrated by applying it to help interpret the effects of perillyl alcohol, a natural chemotherapeutic agent, on glioblastoma multiform cell lines (A172). We used a new multi-surfactant shotgun proteomic strategy and identified more than 2600 proteins; GOEx pinpointed key sets of differentially expressed proteins related to cell cycle, alcohol catabolism, the Ras pathway, apoptosis, and stress response, to name a few. Conclusion: GOEx facilitates organism-specific studies by leveraging GO and providing a rich graphical user interface. It is a simple to use tool, specialized for biologists who wish to analyze spectral counting data from shotgun proteomics. GOEx is available at http://pcarvalho.com/ patternlab. [ABSTRACT FROM AUTHOR]

Background: A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liu et al. demonstrated a method to perform semi-relative protein quantitation in shotgun proteomics data by correlating the number of tandem mass spectra obtained for each protein, or "spectral count", with its abundance in a mixture; however, two issues have remained open: how to normalize spectral counting data and how to efficiently pinpoint differences between profiles. Moreover, Chen et al. recently showed how to increase the number of identified proteins in shotgun proteomics by analyzing samples with different MS-compatible detergents while performing proteolytic digestion. The latter introduced new challenges as seen from the data analysis perspective, since replicate readings are not acquired. Results: To address the open issues above, we present a program termed PatternLab for proteomics. This program implements existing strategies and adds two new methods to pinpoint differences in protein profiles. The first method, ACFold, addresses experiments with less than three replicates from each state or having assays acquired by different protocols as described by Chen et al. ACFold uses a combined criterion based on expression fold changes, the AC test, and the false-discovery rate, and can supply a "bird's-eye view" of differentially expressed proteins. The other method addresses experimental designs having multiple readings from each state and is referred to as nSVM (natural support vector machine) because of its roots in evolutionary computing and in statistical learning theory. Our observations suggest that nSVM's niche comprises projects that select a minimum set of proteins for classification purposes; for example, the development of an early detection kit for a given pathology. We demonstrate the effectiveness of each method on experimental data and confront them with existing strategies. Conclusion: PatternLab offers an easy and unified access to a variety of feature selection and normalization strategies, each having its own niche. Additionally, graphing tools are available to aid in the analysis of high throughput experimental data. PatternLab is available at http://pcarvalho.com/ patternlab. [ABSTRACT FROM AUTHOR]

We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website., (Copyright © 2020 Elsevier B.V. All rights reserved.)

We present a new module integrated into the widely adopted PatternLab for proteomics to enable analysis of isotope-labeled peptides produced using dimethyl or SILAC. The accurate quantitation of proteins lies within the heart of proteomics; dimethylation has shown to be reliable, inexpensive, and applicable to any sample type. We validate our algorithm using an M. tuberculosis dataset obtained from two biological conditions; we used three dimethyl labels, one serving as an internal control for labeling a mixture of samples from both biological conditions. This internal control certified the proper functioning of our software. Availability: http://patternlabforproteomics.org, freely available for academic use., (Copyright © 2019. Published by Elsevier B.V.)

Venoms are a rich source for the discovery of molecules with biotechnological applications, but their analysis is challenging even for state-of-the-art proteomics. Here we report on a large-scale proteomic assessment of the venom of Loxosceles intermedia, the so-called brown spider. Venom was extracted from 200 spiders and fractioned into two aliquots relative to a 10 kDa cutoff mass. Each of these was further fractioned and digested with trypsin (4 h), trypsin (18 h), pepsin (18 h), and chymotrypsin (18 h), then analyzed by MudPIT on an LTQ-Orbitrap XL ETD mass spectrometer fragmenting precursors by CID, HCD, and ETD. Aliquots of undigested samples were also analyzed. Our experimental design allowed us to apply spectral networks, thus enabling us to obtain meta-contig assemblies, and consequently de novo sequencing of practically complete proteins, culminating in a deep proteome assessment of the venom. Data are available via ProteomeXchange, with identifier PXD005523.