GO Explorer: A gene-ontology tool to aid in the interpretation of shotgun proteomics data

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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 <url>http://pcarvalho.com/patternlab</url>.</p

Anthracycline rechallenge using pegylated liposomal doxorubicin in patients with metastatic breast cancer: a pooled analysis using individual data from four prospective trials

BACKGROUND: The aim of this study was to determine the activity of anthracycline rechallenge using pegylated liposomal doxorubicin (PLD) in patients with metastatic breast cancer (MBC) previously treated with conventional anthracyclines. METHODS: Pooled individual data from four prospective trials were used, and the primary end point of the pooled analysis was clinical benefit rate (CBR). The studies comprised 935 patients, of whom 274 had received PLD in the metastatic setting after prior exposure to conventional anthracyclines (rechallenge population). RESULTS: The majority of patients were heavily pretreated. Previous anthracycline therapy was administered in the adjuvant (14%) or metastatic setting (46%), or both (40%). The overall CBR from rechallenge with PLD was 37.2% (95% CI, 32.4-42.0). In univariate analyses, the CBR was significantly higher in patients with less exposure to prior chemotherapy, in taxane-naive patients, and in patients with a favourable Eastern Cooperative Group performance status of 0 vs 1 vs 2 (53.3 vs 35.5 vs 18.2%; P<0.001). In multivariate analyses, performance status proved to be the only independent predictor of the CBR achieved with PLD rechallenge (P=0.038). There was no statistically significant difference in CBR regarding the setting, cumulative dose of and/or resistance to prior anthracyclines, or time since prior anthracycline administration. CONCLUSION: Anthracycline rechallenge using PLD is effective in patients with MBC who have a favourable performance status, regardless of setting, resistance, cumulative dose or time since prior conventional anthracycline therapy. British Journal of Cancer (2010) 103, 1518-1523. doi:10.1038/sj.bjc.6605961 www.bjcancer.com Published online 26 October 2010 (C) 2010 Cancer Research U

Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology

Differential Proteomic Analysis of Mammalian Tissues Using SILAM

Differential expression of proteins between tissues underlies organ-specific functions. Under certain pathological conditions, this may also lead to tissue vulnerability. Furthermore, post-translational modifications exist between different cell types and pathological conditions. We employed SILAM (Stable Isotope Labeling in Mammals) combined with mass spectrometry to quantify the proteome between mammalian tissues. Using 15N labeled rat tissue, we quantified 3742 phosphorylated peptides in nuclear extracts from liver and brain tissue. Analysis of the phosphorylation sites revealed tissue specific kinase motifs. Although these tissues are quite different in their composition and function, more than 500 protein identifications were common to both tissues. Specifically, we identified an up-regulation in the brain of the phosphoprotein, ZFHX1B, in which a genetic deletion causes the neurological disorder Mowat–Wilson syndrome. Finally, pathway analysis revealed distinct nuclear pathways enriched in each tissue. Our findings provide a valuable resource as a starting point for further understanding of tissue specific gene regulation and demonstrate SILAM as a useful strategy for the differential proteomic analysis of mammalian tissues

Search for narrow resonances in dilepton mass spectra in proton-proton collisions at root s=13 TeV and combination with 8 TeV data

Peer reviewe

Cross section measurement of t-channel single top quark production in pp collisions at root s=13 TeV

Peer reviewe

Search for light bosons in decays of the 125 GeV Higgs boson in proton-proton collisions at root s=8 TeV

Peer reviewe

Search for R-parity violating supersymmetry with displaced vertices in proton-proton collisions at root s=8 TeV

Peer reviewe